Aşkar, Petek; B.Akkoyunlu*Kolb Öğrenme Stili Envanteri*Eğitim ve
Bilim. 87,1993. 37-47.
Title: Are Learning Approaches and Thinking Styles Related? A Study in Two Chinese Populations.
Subject(s): LEARNING strategies; THOUGHT & thinking -- Psychological aspects
Source: Journal of Psychology, Sep2000, Vol. 134 Issue 5, p469, 21p
Author(s): Zhang, Li-Fang; Sternberg, Robert J.
ARE LEARNING APPROACHES AND THINKING STYLES RELATED? A STUDY IN TWO
CHINESE POPULATIONS
ABSTRACT. This article presents the results of an investigation of the
construct validity of J. B. Biggs's (1987) theory of learning approaches
and of R. J. Sternberg's (1988) theory of thinking styles in two Chinese
populations. The study is also an examination of the nature of the
relations between the two theories. University students from Hong Kong (n =
854) and from Nanjing, mainland China (n = 215), completed the Study
Process Questionnaire (J. B. Biggs, 1992) and the Thinking Styles Inventory
(R. J. Sternberg & R. K. Wagner, 1992). Results indicated that both
inventories were reliable and valid for assessing the constructs underlying
their respective theories among both Hong Kong and Nanjing university
students. Results also showed that the learning approaches and thinking
styles are related in the hypothesized ways: The surface approach was
hypothesized to be positively and significantly correlated with styles
associated with less complexity, and negatively and significantly
correlated with the legislative, judicial, liberal, and hierarchical
styles. The deep approach was hypothesized to be positively and
significantly correlated with styles associated with more complexity, and
negatively and significantly correlated with the executive, conservative,
local, and monarchic styles. Implications of these relations are discussed.
THINKING AND LEARNING STYLES are sources of individual differences in
academic performance that are related not to abilities but to how people
prefer to use their abilities. There are alternative theories of thinking
and learning styles, all of which share a common goal--that is, to explain
individual differences in performance that are not explained by abilities
(Sternberg, 1994, 1997).
Given the differences among theories of thinking and learning styles, a
question that arises is whether such theories relate to different
constructs, using a common root word "style," or rather if they are
theories of the same construct but have different names for overlapping
styles. Psychologists and educators need to understand whether the various
theories--and the measures associated with them--provide insights into
different constructs or the same constructs under different labels.
Following this view, the primary goal of the present study was to verify
the nature of the relations between Biggs's (1987, 1992) theory of
approaches to learning and Sternberg's (1988, 1990, 1994, 1997) theory of
mental self-government, a theory of thinking styles.
What, exactly, is a style? How does a style differ
is a preferred way of thinking or of doing things.
ability, but rather a preference in the use of the
It is an interface between ability and personality
from an ability? A style
A style is not an
abilities people have.
(Sternberg, 1994, 1997).
Since the beginning of the cognitive-styles movement in the 1950s and early
1960s, different theories of thinking styles have been constructed. Because
there are many more than we could address here (for more extensive reviews,
see Grigorenko & Sternberg, 1995; Kogan, 1983; Sternberg, 1997), we review
only selected theories.
Myers (1980; Myers & McCaulley, 1988) proposed a series of psychological
types based on Jung's (1923) theory of types. According to Myers, there are
16 types, resulting from all possible combinations of (a) two ways of
perceiving (sensing vs. intuiting), (b) two ways of judging (thinking vs.
feeling), (c) two ways of dealing with self and others (being introverted
vs. being extraverted), and (d) two ways of dealing with the outer world
(judging vs. perceiving). Gregorc (1985) proposed four main types of
styles, based on all possible combinations of two dimensions (concrete vs.
abstract and sequential vs. random). Renzulli and Smith (1978) suggested
various learning styles, with each corresponding to a method of teaching
(e.g., projects, drill and recitation, and discussion), and Holland (1973)
proposed six styles (realistic, investigative, artistic, social,
enterprising, and conventional) that have been used as a basis for
understanding career interests. Some other theories of styles are not
general theories; rather, they are theories of specific aspects of
cognitive-stylistic functioning (Grigorenko & Sternberg, 1995). For
example, Kagan (1976) studied individual differences between impulsive and
reflective persons, and Witkin (1978) examined the differences between
field-independent and field-dependent individuals.
Recently, two theories have been proposed that are fairly general. One is
Biggs's (1987, 1992) theory of students' approaches to learning, also known
as the 3P model; the other is Sternberg's (1988, 1990, 1997) theory of
mental self-government.
Biggs's Theory of Approaches to Learning
Adapted from Dunkin and Biddle's (1974) presage-process-product model,
Biggs's model addresses those three components in the classroom. Presage
concerns components before learning takes place; process pertains to
components while learning is taking place; product pertains to outcomes
after learning has taken place. In the present study we focus on the
process of learning. According to the 3P model, there are three common
approaches to learning: surface, which involves a reproduction of what is
taught to meet the minimum requirements; deep, which involves a real
understanding of what is learned; and achieving, which involves using a
strategy that will maximize one's grades. Each approach is composed of two
elements: motive and strategy (see Biggs, 1987, 1992, for a description of
the Study Process Questionnaire [SPQ]). Motive describes why students
choose to learn, whereas strategy describes how students go about their
learning.
An alternative theory is that of Marton (e.g., Marton & Booth, 1997), who
proposed surface and deep but not achieving strategies. A question in need
of resolution, therefore, is whether the achieving style truly is
distinguishable from the other two as a third style, or is a variant of one
or both of them. Related work has been done by Entwistle and his colleagues
(e.g., Entwistle, 1988, 1990; Entwistle, Koseki, & Politt, 1987; Entwistle
& Marton, 1994), who have considered both the two-style and three-style
models.
One of the instruments used to assess learning approaches among university
students is the SPQ (Biggs, 1987, 1992), which was originally designed to
assess the learning approaches of Canadian and Australian students. Many
studies have been undertaken with the SPQ. Focusing on students' motives
and strategies for learning, Biggs (1992) summarized major endeavors
regarding the 3P model using the SPQ before 1992. These motives and
strategies for learning have been examined in the following contexts:
cross-cultural comparisons, the language medium of instruction,
teaching/learning environments, student characteristics, professional and
staff development, and factor structure and dimensionality of subscales.
More recent work examining learning approaches as defined by the 3P model
have as their foci the investigation of the relationships between learning
approaches and academic achievement (e.g., Albaili, 1995; Rose, Hall,
Bolen, & Webster, 1996) and the construction of other versions of the SPQ
(e.g., Albaili; Watkins & Murphy, 1994). Investigation of the factorial
structure of the SPQ continues to be one of the major approaches to
examining the instrument and its underlying 3P model (e.g., Bolen, Wurm, &
Hall, 1994; Niles, 1995; O'Neil & Child, 1984). In addition, individual
differences based on age and gender (e.g., Sadler-Smith & Tsang, 1998;
Watkins & Hattie, 1981; Wilson, Smart, & Watson, 1996) also have been of
major interest to scholars using the SPQ in their investigations of student
learning.
In their study of the relationship between SPQ scores and overall grade
point average (GPA) among 202 U.S. undergraduate students, Rose et al.
(1996) found that only scores on the achieving approach contributed to
prediction (negative correlation) of GPA. Albaili (1995), in his study of
246 United Arab Emirates undergraduate students, found that GPAs were
negatively correlated with the surface approach and positively correlated
with the deep and achieving approaches.
As mentioned earlier, the SPQ was originally constructed to measure
Australian and Canadian university students' learning approaches. Other
versions of the SPQ, however, also have been constructed. For example, in
1992, a Hong Kong version was established (Biggs, 1992). In 1994, when they
studied Brunei university students, Watkins and Murphy came up with a
simplified English as a Second Language (ESL) version and a Malay version.
In 1995, Albaili established an Arabic version of the SPQ in his study of
university students in the United Arab Emirates. All of the versions of the
SPQ proved to be reliable and valid measures for assessing students'
learning approaches.
The study of the validity of the SPQ has taken two forms. One is the
examination of its internal structure. The other is the examination of the
SPQ compared with other instruments. Many studies have had a focus on the
examination of the internal structure of the SPQ. Although some studies
supported Biggs's original argument that there are three factors in the
SPQ, other studies have shown that there are only two factors. For example,
in their study of a sample of U.S. university students' approaches to
learning, Bolen et al. (1994) identified three factors--Surface Approach,
Deep Approach, and Achieving Approach. Similarly, O'Neil and Child (1984),
studying British university students, also identified three factors in the
SPQ. However, in a study in Australia by Niles (1995) of overseas and
Australian university students, and in Watkins and Dahlin's (1997) study of
university students in Sweden, a two-factor model was identified (see also
Entwistle, 1981; Marton & Booth, 1997). The two factors were Deep Approach
and Surface Approach to learning, and the two Achieving subscales were
split between the two factors.
Few researchers have investigated the relations between the SPQ and other
instruments. We identified three such studies in the literature. A first
study was conducted by Kember and Gow (1990), who administered both the SPQ
and the Approaches to Studying Inventory (ASI; Entwistle, 1981) to Hong
Kong university students. Although all three factors (Surface, Deep, and
Achieving) appeared in the SPQ, only two factors (Deep and Achieving)
appeared in the ASI. Surface learning was replaced by a factor labeled
"narrow orientation" (Harper & Kember, 1989), which has been variously
called "operation learning" by Watkins (1982, p. 80) and "disorganized
study" by Ramsden and Entwistle (1981, p. 372).
A second study was carried out by Murray-Harvey (1994), who conducted a
factor analysis on the Productivity Environmental Preference survey and the
SPQ data collected from 400 Australian university students. Results
indicated that the two inventories measure two quite different
conceptualizations of student learning. It was concluded that learning
approach is relatively stable over time and that learning style is not
quite as stable.
A third study was conducted by Wilson et al. (1996), who also studied the
relationship between the SPQ and the ASI. Analyzing the data collected from
283 Australian university students, the authors found significant
correlations between the scales in the two inventories. They concluded that
the two inventories measure similar constructs.
Age and gender are two of the variables that scholars have investigated in
relation to the SPQ. Findings are, again, varied. For example, Sadler-Smith
and Tsang (1998), studying British and Hong Kong university students, did
not find any age or gender difference in the British sample; they did,
however, observe an interaction of age and gender in their effects on deep
and strategic (see Entwistle, 1981) approaches. That is, mature male
students reported higher scores on the deep approach than did the nonmature male students; however, for female students, this pattern was
reversed. Sadler-Smith and Tsang specified 23 years as the cutoff age
between non-mature and mature participants.
By the same token, Watkins and Hattie (1981) also observed age and gender
differences. They found that male students scored significantly higher on
the scales measuring surface learning than did female students, whereas
female students scored significantly higher on the scales measuring deep
learning than did their male counterparts. They also found that older
students scored significantly higher on the scales measuring deep learning
than did their younger counterparts. On the contrary, Wilson et al. (1996)
found no gender differences.
In summary, there is strong evidence that the SPQ is a reliable and valid
instrument for assessing the learning approaches of university students,
including Chinese university students.
Sternberg's Theory of Mental Self-Government
Sternberg's (1988, 1990, 1997) theory of mental self-government addresses
people's thinking styles, which may be used in many settings, including
university, home, and community. At the heart of this theory is the notion
that people need somehow to govern or manage their everyday activities.
There are many ways of doing so; whenever possible, people choose styles of
managing themselves with which they are comfortable. Still, people are at
least somewhat flexible in their use of styles and try with varying degrees
of success to adapt themselves to the stylistic demands of a given
situation. Thus, an individual with one preference in one situation may
have a different preference in another situation. Moreover, styles may
change with time and with life demands. Thinking styles are at least partly
socialized (Sternberg, 1994, 1997), a fact that suggests that, to some
extent, they can be modified by the environment in which people reside. As
applied to individuals, the theory of mental self-government posits 13
thinking styles that fall along five dimensions of mental self-government:
(a) functions, (b) forms, (c) levels, (d) scope, and (e) leanings.
Functions
As in government, there are three functions in human beings' mental selfgovernment: legislative, executive, and judicial. An individual with a
legislative style enjoys being engaged in tasks that require creative
strategies. These individuals prefer to choose their own activities, or at
least to do the activities chosen for them in their own way. An individual
with an executive style is more concerned with implementation of tasks with
set guidelines. Such an individual prefers more direction or guidance in
structuring tasks. An individual with a judicial style focuses attention on
evaluating the products of others' activities.
Forms
Also as in government, a human being's mental self-government takes four
different forms: monarchic, hierarchic, oligarchic, and anarchic. An
individual with a monarchic style enjoys being engaged in tasks that allow
complete focus on one thing at a time. In contrast, an individual with a
hierarchic style prefers to distribute attention to several tasks that are
given priority according to their value to the individual in achieving his
or her goals. An individual with an oligarchic style also likes to work on
multiple activities in the service of multiple objectives, but may not
enjoy setting priorities. Finally, an individual with an anarchic style
enjoys working on tasks that allow flexibility as to what, where, when, and
how one works, but he or she eschews systems of almost any kind.
Levels
As with governments, human beings' mental self-government functions at two
different levels: local and global. An individual with a local style enjoys
being engaged in tasks that require working with concrete details. In
contrast, an individual with a global style prefers to pay more attention
to the overall picture of an issue and to abstract ideas.
Scope
Mental self-government can deal with internal and external matters. An
individual with an internal style enjoys being engaged in tasks that allow
that individual to work independently. In contrast, an individual with an
external style likes being engaged in tasks that allow for collaborative
ventures with other people.
Leanings
Finally, in mental self-government, there are two leanings: liberal and
conservative. An individual with a liberal style enjoys engaging in tasks
that involve novelty and ambiguity, whereas an individual with a
conservative style prefers adhering to the existing rules and procedures in
performing tasks.
The theory of mental self-government has been operationalized through
inventories, including the Thinking Styles Inventory (TSI; Sternberg &
Wagner, 1992), which have been shown to be reliable and valid for U.S. and
Hong Kong samples. Furthermore, results from such research have shown some
value of the theory and have generated a number of implications for
teaching and learning in educational settings. In the United States,
Sternberg and Grigorenko conducted a series of studies. In one such study,
Sternberg and Grigorenko (1995) reported significant relationships between
teaching styles and grade taught, length of teaching experience, and
subject area taught. Specifically, teachers teaching at lower grade levels
were more legislative than teachers teaching at higher grade levels;
complementarily, teachers teaching at lower grade levels were less
executive than teachers at higher grade levels. It was shown that teachers
with more teaching experience were more executive, local, and conservative
than were those teachers with less teaching experience. Furthermore, it was
found that humanities teachers were more liberal than were science
teachers.
A second set of findings indicated significant relationships between
students' learning styles and such demographic data as students'
socioeconomic status (SES) and birth order (Sternberg & Grigorenko, 1995).
Specifically, participants of higher SES status tended to score higher on
the legislative style. Likewise, participants who were later-borns in their
family scored higher on the legislative style than did participants who
were earlier-borns. A third data set indicated that teachers inadvertently
favored those students who had thinking styles similar to their own
(Sternberg & Grigorenko). In a more recent study, Grigorenko and Sternberg
(1997) found that certain thinking styles contribute significantly to
prediction of academic performance over and above prediction of scores on
ability tests. Their study also indicated that students with particular
thinking styles fared better on some forms of evaluation than on others.
Three studies concerning the theory of mental self-government have been
carried out in Hong Kong (Zhang, 1999; Zhang & Sachs, 1997; Zhang &
Sternberg, 1998). These studies indicate that the thinking styles defined
by Sternberg's theory also can be identified among university students in
Hong Kong. The internal consistency reliabilities and validity data are
generally satisfactory (see description in the Method section, under
Inventories). Furthermore, results from these studies have suggested that
students' thinking styles are statistically different based on such
variables as age, sex, college class, teaching experience, college major,
school subject taught, and travel experience. For example, male
participants scored higher on the global style than did their female
counterparts. Participants who had had more teaching experience (as
measured by the length of teaching) and those who had had more travel
experience scored higher on the creativity-promoting thinking styles, such
as legislative and liberal. In our recent study (Zhang & Sternberg, 1998)
of 622 Hong Kong university students, we found that thinking styles (as
defined by the theory of mental self-government) could serve as reasonable
predictors of academic achievement over and above self-rated abilities. For
example, higher achievement was positively correlated with the use of
conservative, hierarchic, and internal styles of thinking; yet, higher
achievement was negatively correlated with the use of the legislative,
liberal, and external styles of thinking.
Although both the SPQ and the TSI and their underlying theories have been
well researched, the present study is the first to investigate the
relationships among the scales in the two inventories and the connections
between the two theories. In the present study, we examined the relations
between the two theories and corresponding measures of styles in two
Chinese populations--university students from Nanjing, mainland China, and
university students from Hong Kong. The means to achieve this goal was to
determine the reliability and validity of the SPQ and of the TSI, to
examine the relations between the scales in the two inventories, and to
determine whether the hypothesized relationships between the SPQ and the
TSI exist among more than one sample. These two inventories were studied
together because they are based on similar theoretical constructs. By
nature, both Biggs's theory of learning approaches and Sternberg's theory
of mental self-government concern two types of mental functioning and thus,
two ways of processing information: more simple and more complex.
We proposed two sets of hypotheses, drawn in part on past work in the field
by Beishuizen, Stoutjesdijk, and Van-Putten (1994), who studied the
relation between cognitive levels of task accomplishment and deep versus
surface processing of material. Beishuizen et al. expected deep-processing
students to benefit from metacognitive support and surface-processing
students to benefit from cognitive support. They found that students who
processed at a surface level tended to benefit from cognitive support.
Students who combined self-regulation with deep processing and students who
combined external regulation with surface processing outperformed students
who showed the opposite pairings of type of regulation with type of
processing.
We expected students who take a surface approach to learning and those who
use executive, monarchic, local, and conservative styles to be individuals
who want to get things done with given structures, who do not want to make
mistakes, and who want to "play it safe." We expected students who take a
deep approach to learning and those who tend toward legislative, judicial,
hierarchic, anarchic, global, and liberal styles to want to make up their
own minds and use their own judgments in learning. We expected these
students to want to work more in situations in which their creativity and
imagination would be allowed free rein. Furthermore, we expected them to be
less afraid of making mistakes.
Thus, we proposed the following: First, the surface approach should be
positively and significantly correlated with styles associated with less
complexity--executive, monarchic, local, and conservative styles.
Complementarily, this approach should be negatively and significantly
correlated with the legislative, judicial, liberal, and hierarchic styles.
Second, the deep approach should be positively and significantly correlated
with styles associated with more complexityNlegislative, judicial,
hierarchic, anarchic, global, and liberal styles. Complementarily, this
approach should be negatively and significantly correlated with the
executive, conservative, local, and monarchic styles.
No specific predictions were made regarding the relations between the
achieving approach subscales of the SPQ and the subscales of the TSI,
because previous research (e.g., Niles, 1995; Watkins & Dahlin, 1997; Wong,
Lin, & Watkins, 1996) has yielded conflicting results. In particular, the
achieving motive and strategy subscales of the SPQ (which assess the
achieving approach) may be either clustered with one of the two scales
(Deep and Surface) or split between the two. In other words, like Marton
and Booth's (1997) theory, Biggs's theory conceptually addresses two
approaches to learning: deep and surface.
Method
Participants
Hong Kong sample. A total of 854 (362 male and 492 female) students were
selected randomly from about 4,000 entering students at the University of
Hong Kong during the orientation week of the fall semester of 1997. These
participants were from all of the nine faculties (Architecture, Arts,
Dentistry, Education, Engineering, Law, Medicine, Science, and Social
Sciences) and the School of Business at the university. Of these students,
501 were in social sciences/humanities, 349 were in natural sciences, and 4
were not identifiable. Of all the participants, 702 were undergraduate
freshmen, 66 were beginning to pursue their post-graduate certificates, and
86 were starting their education for a master's degree. The average age of
the participants was 21 years; 66% were 19 years old or younger, 20% were
between the ages of 20 and 25, and 14% were between 26 and 57 years of age.
At the time the study was conducted, 535 of the participants were not
holding any job, 110 were working full-time, and 198 were working parttime. Eleven did not indicate their employment status.
Nanjing sample. A total of 215 (114 male, 101 female) entering freshmen
from two big universities in Nanjing, mainland China, participated in the
study at the beginning of the fall semester of 1997. Ten teachers were
trained in the administration of the questionnaires. Each of the 10
teachers informed his or her class about the nature of the study. Those
students who were not willing to participate in the study were not required
to participate. Those who volunteered (98% of the students) to participate
were from several areas of study, including chemistry, computer science,
education, finance, history, law, management, mathematics, medicine, and
political science. Classified into the two broad fields of study, 126 were
from social sciences/humanities and 89 were from natural sciences. The
average age of the participants was 19 years, with a range from 15 to 23.
In all, 75% of the participants were 19 years old or younger.
Inventories
Two inventories and a demographic questionnaire were used in the study. The
first inventory was Biggs's SPQ (1992; Chinese version normed on Hong Kong
university students). The second was Sternberg and Wagner's (1992) TSI.
Both of the inventories were developed originally in English and were later
translated and back-translated between Chinese and English.
The SPQ is a self-report questionnaire consisting of 42 items. This
questionnaire has 6 subscales, with 7 items on each subscale. For each
item, the respondents are asked to rate themselves on a 5-point scale
anchored by 1 (never or only rarely true of you) and 5 (always or almost
always true of you). The 6 subscales are Surface Motive, Surface Strategy,
Deep Motive, Deep Strategy, Achieving Motive, and Achieving Strategy.
Therefore, the 3 scales based on the three approaches to learning are
Surface (Motive and Strategy), Deep (Motive and Strategy), and Achieving
(Motive and Strategy). As described earlier, motive describes why students
choose to learn, whereas strategy describes how students go about their
learning.
As mentioned earlier, numerous studies involving the use of the SPQ have
been conducted all over the world (e.g., Albaili, 1995; Bolen et al., 1994;
Kember & Gow, 1990; Murray-Harvey, 1994; Watkins & Akande, 1992; Watkins &
Regmi, 1990). Most of those studies have resulted in internal consistencies
ranging from the mid .50s to the low or mid .70s for the 6 subscales and
from the low .70s to the low .80s for the three scales (see Albaili, 1995,
for details).
The TSI (Sternberg & Wagner, 1992) is a self-report questionnaire
consisting of 65 items. The inventory has 13 subscales, with 5 items on
each subscale. For each item, respondents are asked to rate themselves on a
7-point scale anchored by 1 (does not characterize you at all) and 7
(characterizes you extremely well). These 13 subscales correspond to the 13
thinking styles described in Sternberg's theory of mental self-government.
Sternberg and Wagner (1992) collected norms for various age groups on the
long version of the TSI (which contains 104 items, 8 for each of the 13
subscales). For Sternberg and Wagner's college sample, subscale
reliabilities ranged from .42 (monarchic) to .88 (external), with a median
reliability of .78. In another study using the TSI, Sternberg (1994) found
a five-factor model corresponding to the five dimensions of mental selfgovernment described in his theory of thinking styles. These five factors
accounted for 77% of the variance in the data.
The TSI also has been validated against instruments based on other theories
of styles (e.g., Myers-Briggs Type Indicator, Gregorc's measure of mind
styles), as well as a standard IQ test, the Scholastic Assessment Test
(SAT), and GPA. Results from these construct-validity studies indicated
that, among U.S. students, the TSI is a reliable and valid instrument for
studying thinking styles as defined by the theory of mental selfgovernment.
The TSI also has proved to be reliable and valid for identifying thinking
styles of university students in Hong Kong. The statistics from three
studies (Zhang, 1999; Zhang & Sachs, 1997; Zhang & Sternberg, 1998)
conducted in Hong Kong are similar in magnitude to those obtained by
Sternberg (1988, 1990, 1994, 1997). For example, the alpha coefficients in
Sternberg's (1994) study ranged from .44 to .88; those in Zhang and Sachs's
(1997) study ranged from .53 to .87 (from .46 to .89 in Zhang, 1999, and
from .43 to .78 in Zhang & Sternberg, 1998). Although Zhang and Sachs's
(1997) study extracted only three factors corresponding to the constructs
in the theory of mental self-government, both Sternberg's (1994) and
Zhang's (1999) studies extracted five factors (the former accounted for 77%
of the variance and the latter, 78%). In these studies, each factor roughly
corresponded to one of the five dimensions delineated in the theory. In our
recent study (Zhang & Sternberg, 1998), the validity of the TSI was tested
through an interscale correlation matrix. It was shown that the scales
were, in general, correlated in the predicted directions. For example, the
correlation between the executive and conservative styles was .63 (p <
.001); that between the legislative and liberal styles was .41 (p < .001);
and that between the internal and external styles was -.30 (p < .001).
Data Analysis
The following analyses were conducted both separately for men and women and
for the sexes combined. The reliability of each of the 6 subscales in the
SPQ and the 13 subscales in the TSI was estimated by Cronbach's alpha. The
validity of each of the two inventories was examined through the relations
shown among the subscales by its respective intercorrelation matrix. The
relations between the two theories were examined via a correlation matrix,
with the subscales of the SPQ providing one set of variables and those of
the TSI providing another.
Results
In both the Hong Kong and Nanjing samples, t tests on the 6 subscales of
the SPQ and the 13 subscales of the TSI resulted in a few pairs of
statistically significant (p < .05) means for men and women. On a 5-point
Likert-type scale (of the SPQ), the statistically significant mean
differences were (a) .19 on Achieving Motive, (b) .11 on Deep Strategy, and
(c) .11 on Surface Motive for the Hong Kong sample; and (a) .24 on Deep
Motive and (b) .31 on Deep Strategy for the Nanjing sample. On a 7-point
Likert-type scale, the statistically significant mean differences were (a)
.12 on the legislative style, (b) .20 on the judicial style, (c) .15 on the
global style, (d) .38 on the liberal style, and (e) .23 on the internal
style for the Hong Kong sample; and (a) .39 on the legislative style, (b)
.74 on the liberal style, and (c) .35 on the internal style for the Nanjing
sample. In all cases, men scored higher than women. These differences,
although statistically significant, were small in magnitude. Furthermore,
none of the remaining statistical analyses conducted for men and women
separately indicated significant gender differences. These analyses
included (a) a correlational analysis on the 13 subscales of the TSI, (b) a
factor analysis on the SPQ, and (c) a correlational analysis between the
subscales of the two inventories. Because of the lack of gender differences
in the previous three statistical procedures, the results are reported with
combined gender analyses.
Subscale Reliabilities for the SPQ
The alpha estimates of internal consistency for the Deep and Achieving
Motive and Strategy subscales for both the Hong Kong and Nanjing samples
are in line with those obtained by Biggs (1987) for his Australian norming
sample (see Table 1). The findings also are in line with estimates obtained
by other authors, such as Watkins and Dahlin (1997), in their study of
Swedish university students. However, the alpha coefficients of the Surface
Motive and Surface Strategy subscales are higher for the samples in this
study (in the mid .60s and low .70s) than for the aforementioned Australian
and Swedish samples (low .40s for Surface Motive and mid .50s for Surface
Strategy). The alpha coefficients for the 6 subscales ranged from .65 to
.80, with a median of .73, for the Hong Kong students, and from .64 to .74,
with a median of .70, for the Nanjing students. The alpha coefficients for
the Surface, Deep, and Achieving scales were .80, .82, and .83,
respectively, for the Hong Kong sample, and .78, .78, and .76,
respectively, for the Nanjing sample. These alpha coefficients were
considered sufficiently high to allow further statistical analyses.
Subscale Reliabilities for the TSI
The magnitudes of the estimates of internal consistency for the TSI for the
Hong Kong sample and the Nanjing sample were similar (see Table 2).
Furthermore, these results are comparable to those obtained by Sternberg
(1994) in his study of U.S. participants, by Zhang and Sachs (1997), and by
Zhang (1999). Notice that 3 subscales were less internally consistent in
those respective studies. These subscales were local, monarchic, and
anarchic. Even so, the estimates of internal consistency obtained in the
present study were considered to be adequate to allow further statistical
analyses.
Subscale Intercorrelations for the SPQ
In accordance with Biggs's theory, we predicted that the Deep Motive and
Deep Strategy subscales would be significantly negatively correlated with
the Surface Motive and Surface Strategy subscales. Furthermore, as
mentioned earlier, no prediction was made on the Achieving Motive and
Achieving Strategy subscales because these subscales may be positively and
significantly correlated with either the Deep subscales or the Surface
subscales, or split between the two (Watkins & Dahlin, 1997; Wong et al.,
1996). The predictions were fully supported by the results from the Nanjing
sample. Results from the Hong Kong sample, however, did not support these
predictions, in that three of the correlations were in the direction
opposite from what was expected from the theory. These correlation
coefficients were (a) Surface Motive with Deep Motive (r = .17, p < .01),
(b) Surface Motive with Deep Strategy (r = .16, p < .01), and (c) Surface
Strategy with Deep Strategy (r = .10, p < .01). These three correlations
indicate that students who took a surface approach to learning also tended
to take a deep approach, a pattern not consistent with Biggs's theory,
according to which surface subscales presumably should be negatively
correlated with the deep subscales.
Because of the presence of the three unexpected correlations, we conducted
a principal-axis factor analysis with a varimax rotation, to examine
further the validity of the SPQ for the Hong Kong sample. A scree test
(Cattell, 1966) indicated that a two-factor solution would be appropriate.
Furthermore, there were two factors with eigenvalues greater than 1. Thus,
a two-factor model was retained (see Table 3 for details). The analysis
yielded a clear factor for a deep approach (factor loadings: .86 for Deep
Motive; .89 for Deep Strategy; .76 for Achieving Strategy) and one for a
surface approach (factor loadings: .88 for Surface Motivation; .87 for
Surface Strategy; .71 for Achieving Motive). The Achieving Motive and
Achieving Strategy subscales thus were split between the Deep and Surface
subscales, as expected (Niles, 1995; Watkins & Dahlin, 1997; Wong et al.,
1996).
A principal-axis factor analysis with a varimax rotation also was conducted
with the Nanjing participants' data to confirm the validity of the SPQ for
the Nanjing sample. Results from this analysis revealed the same two
factors as those for the Hong Kong data (see Table 3). The first factor
corresponded to the deep approach (factor loadings: .81 for Deep Motive;
.81 for Deep Strategy; .77 for Achieving Strategy). The second factor
corresponded to the surface approach (factor loadings: .86 for Surface
Motive; .86 for Surface Strategy; .61 for Achieving Motive).
Consequently, the SPQ, when conceptualized as a two--rather than threefactor instrument, appeared to be valid for assessing the learning
approaches of the two Chinese samples. These results from factor analyses
supported not only previous studies using the SPQ (e.g., Niles, 1995;
Watkins & Dahlin, 1997; Wong et al., 1996) but also Marton and Booth's
(1997) findings regarding learning approaches.
Subscale Intercorrelations for the TSI
In general, for both the Hong Kong and Nanjing samples, the correlations
among the 13 subscales were in the direction predicted by the theory of
mental self-government (see Table 4 for details). Some of the examples are
(a) Executive with Conservative (r = .65 for Hong Kong; r = .66 for
Nanjing), (b) Legislative with Liberal (r = .42 for Hong Kong; r = .50 for
Nanjing), (c) Conservative with Liberal (r = -.10 for Hong Kong; r = -.42
for Nanjing), (d) Global with Local (r = .08 for Hong Kong; r = -.35 for
Nanjing), and (e) Internal versus External (r = -.23 for Hong Kong; r = .28 for Nanjing). Except for the correlation between Global and Local for
Nanjing, these correlations were significant at the .01 level. Furthermore,
the magnitudes of these correlations were generally stronger for the
Nanjing sample than for the Hong Kong sample.
Correlations Among Subscales in the Two Inventories
In general, the hypotheses were supported by the data from both samples
(see Table 5). The majority of the correlations were in the expected
directions. Some of the examples are (a) Surface Motive with executive
style (r = .24 for Hong Kong; r = .23 for Nanjing), (b) Surface Strategy
with liberal style (r = -.03 for Hong Kong; r = -.31 for Nanjing), (c) Deep
Motive with judicial style (r = .40 for Hong Kong; r = .31 for Nanjing),
and (d) Surface Strategy with judicial style (r = -.13 for Hong Kong; r = .11 for Nanjing). These correlations varied from being statistically
insignificant to being significant at the .01 level. Achieving subscales
were inconsistently correlated positively with either the Deep or the
Surface subscales. These correlations indicated that students who took a
surface approach to learning tended to use an executive thinking style, but
not judicial or liberal thinking styles. In addition, students who took a
deep approach to learning tended to use the judicial thinking style.
There were a few correlations that clearly did not support the predictions.
First, for the Hong Kong sample, the correlation between Deep Strategy and
executive style was significantly positive (r = .18, p < .001), meaning
that the Hong Kong students in this sample who used a deep strategy to
learn also preferred using an executive thinking style. Second, our
prediction about the relations between learning approach subscales and the
global and local styles were only partially supported (see Table 5).
Results of this study suggested that regardless of their level of mental
functioning (global or local), students could take either a deep or surface
approach to learning. Finally, all learning approach subscales were
positively and significantly correlated with the monarchic style, which
probably means that students with a monarchic thinking style may take
either a deep or a surface approach to learning. These unexpected
correlations were mostly from the Hong Kong sample, however. These results
perhaps can be explained by Pask's (1976) concept of the "versatile
learner." For example, the deep learners in Hong Kong may be creative
(using the legislative and liberal styles) in their learning; meanwhile,
they may also follow closely their teachers' instructions (using the
executive and conservative styles).
Discussion
The major goal of this study was to establish the relations between the
constructs in Biggs's theory of learning approaches and Sternberg's theory
of thinking styles in two Chinese populations. Results indicated that the
two inventories were reliable and valid (there are two factors in the SPQ-Deep Approach and Surface Approach) for assessing the underlying
theoretical constructs for these two populations and that the subscales in
the two inventories were related in largely predicted ways. Our study
suggests that the SPQ and the TSI measure similar constructs. Students who
reported taking a surface approach to learning preferred using executive,
local, and conservative thinking styles (which are more traditional, norm
favoring, and task oriented), whereas students who reported taking a deep
approach to learning preferred using legislative, judicial, and liberal
thinking styles (which are more creative, norm questioning, and meaning
seeking). Although most of the correlations between the scales of the two
inventories were low, they were statistically significant. In addition,
these results both supported our own hypotheses (based on the study of
Beishuizen et al., 1994) about the relationships between the two
inventories and confirmed previous research findings of similar studies
(e.g., Wilson et al., 1996). Therefore, we believe that these correlations,
although weak, revealed true relationships between the two inventories.
The contributions of this study may be considered from two perspectives:
research and practice.
From a research viewpoint, the results of this study have enhanced our
knowledge about theories of styles. The question raised earlier was whether
theories of styles are different theories of different things, using a
common root word ("style") or theories of the same thing but with different
names for overlapping styles. Sternberg (1997) suggested that alternative
theories of styles cover roughly similar attributes, but with different
labels. The relations indicated by the subscales in the two inventories
used in this study suggest that Biggs's (1987, 1992) theory of students'
approaches to learning and Sternberg's (1988, 1990, 1994, 1997) theory of
mental self-government cover similar but not identical ground, with
different names for overlapping styles. This finding is also consistent
with previous construct-validity studies of measures derived from the
theory of mental self-government (e.g., compared with the Myers-Briggs Type
Indicator and with Gregorc's measures of mind styles; Sternberg, 1994). Of
further theoretical importance is the finding that the two-learning-style
approach of Marton and Booth (deep and surface; 1997) appears to capture
better the structure of the data than does the three learning-style
approach of Biggs (deep, surface, achieving).
From a practical viewpoint, we believe that there are three implications.
First, both teachers and students should be aware that people approach
learning differently and use their abilities in a variety of ways.
Second, but equally important, teachers and students should understand the
relations between learning approaches and thinking styles. An understanding
of the existence of different learning approaches and different thinking
styles can assist teachers in using several measures to facilitate
effective learning. Teachers should try to teach via a variety of styles so
that all students, regardless of their preferred ways of dealing with
learning tasks, can benefit from teachers' instructions. Alternatively,
because learning styles can be modified (Saracho, 1993; Sternberg, 1988,
1990, 1997), awareness of the different learning styles can make students
more in tune with how they usually approach their learning tasks and help
them identify their preferred, as well as their nonpreferred, learning
styles. As a result, students may learn not only how to capitalize on their
strengths and compensate for their weaknesses but also how to adapt to
those learning environments with which their own styles may not be
compatible.
Third, a teacher can use different assessment techniques to allow for
different learning and thinking styles (Sternberg, 1988, 1990, 1994, 1997).
Recognizing this fact, Biggs (1995) coined the term "backwash effect." In
particular, he argued that assessment drives the ways in which students
learn and think, the content of the curriculum, and how teachers teach.
Therefore, among other things, assessment links Biggs's and Sternberg's
theories--it has a common impact on both learning approaches and thinking
styles. Learning approaches and thinking styles as implemented at a given
time may vary as a function of the assessment measures used. For example,
if student performance is measured by a multiple-choice test, students may
tend to take a surface approach to learning and use executive,
conservative, internal, and local thinking styles. In contrast, if student
performance is assessed by a group project, it is more likely that students
will take a deep approach to learning and use such thinking styles as
judicial, legislative, liberal, and external.
An awareness of the interrelations between the two theories also can be
helpful in teachers' efforts toward the enhancement of effective learning.
Each of the learning approaches discussed by Biggs (1987, 1992), as
mentioned earlier, contains two concepts, motivation and strategy.
Students' learning motivations, learning strategies, and thinking styles
are intertwined. Given this intertwining, teachers can facilitate the
students' efforts to be flexible in their implementations of styles. For
example, teachers may wish to motivate students to take a deep approach to
learning more important material, but a surface approach to learning less
important material. The significant positive correlations manifested in
this study indicate that when students are deeply motivated to learn, they
will think critically and creatively, and certainly, also will use a deep
strategy in performing their learning tasks. Alternatively, teachers may
allow for different thinking styles by using the aforementioned strategies,
such as teaching about styles, instructing in different ways, and using
varied assessment tools.
TABLE 1 Study Process Questionnaire Subscales: Means, Standard Deviations,
and Alpha Coefficients
Legend for Chart:
A
B
C
D
E
F
G
-
Subscale
Items
M HK
M NJ
SD HK
SD NJ
alpha HK
H - alpha NJ
A
B
C
F
D
G
E
H
Achieving Motive
3, 9, 15, 21, 27, 33, 39
3.04
.73
3.51
.78
.74
.72
Achieving Strategy
6, 12, 18, 24, 30, 36, 42
3.16
.66
3.49
.80
.69
.73
Deep Motive
2, 8, 14, 20, 26, 32, 38
3.26
.64
3.42
.65
.58
.64
Deep Strategy
5, 11, 17, 23, 29, 35, 41
3.33
.62
3.60
.75
.58
.74
Surface Motive
1, 7, 13, 19, 25, 31, 37
2.96
.73
2.80
.68
.66
.67
Surface Strategy
4, 10, 16, 22, 28, 34, 40
2.74
.58
2.47
.70
.60
.64
Note. HK = Hong Kong. NJ = Nanjing. Hong Kong n = 854. Nanjing
n = 215.
TABLE 2 Thinking Styles Inventory Subscales: Means, Standard Deviations,
and Alpha Coefficients
Legend for Chart:
A
B
C
D
E
F
G
H
-
Subscale
Items
M HK
M NJ
SD HK
SD NJ
alpha HK
alpha NJ
A
B
C
F
D
G
E
H
Legislative
5, 10, 14, 32, 49
4.91
.86
5.45
.71
.81
.65
Executive
8, 11, 12, 31, 39
4.91
.97
4.68
.66
.79
.61
Judicial
20, 23, 42, 51, 57
4.67
.92
4.87
.72
.85
.62
Global
7, 18, 38, 48, 61
4.28
.95
4.59
.58
.76
.60
Local
1, 6, 24, 44, 62
4.35
.90
4.35
.48
.72
.49
Liberal
45, 53, 58, 64, 65
4.20
1.0
4.74
.80
.94
.81
Conservative
13, 22, 26, 28, 36
4.50
3.96
.86
1.12
.72
.74
Hierarchical
4, 19, 25, 33, 56
4.87
1.06
5.01
.76
.88
.78
Monarchic
2, 43, 50, 54, 60
4.59
.86
4.98
.48
.76
.43
Oligarchic
27, 29, 30, 52, 59
4.57
.95
4.62
.64
.80
.66
Anarchic
16, 21, 35, 40, 47
4.45
.76
4.48
.44
.73
.13
Internal
9, 15, 37, 55, 63
4.35
.97
4.71
.77
.99
.67
External
3, 17, 34, 41, 46
4.83
1.06
5.12
.74
.89
.72
Note. HK = Hong Kong. NJ = Nanjing. Hong Kong n = 854.
Nanjing n = 215.
TABLE 3 Oblimin-Rotated Two-Factor Model for the Study Process
Questionnaire
Legend for Chart:
A
B
C
D
E
-
Subscale/Item
Hong Kong Factor 1
Hong Kong Factor 2
Nanjing Factor 1
Nanjing Factor 2
A
Surface Motive
Surface Strategy
Deep Motive
Deep Strategy
Achieving Motive
Achieving Strategy
% of variance
Cumulative %
Eigenvalue
B
C
D
E
-.04
-.10
.88
.90
.32
.74
48.2
48.2
2.89
.89
.89
-.07
-.07
.67
.15
24.7
72.9
1.48
-.12
-.10
.81
.82
.50
.77
36.5
36.5
2.19
.86
.86
-.04
-.16
.60
.04
31.0
67.5
1.86
Note. Hong Kong n = 854. Nanjing n = 215.
TABLE 4 Interscale Pearson Correlation Matrix for 13 Subscales of the
Thinking Styles Inventory
Legend for Chart:
A
B
C
D
E
F
G
H
I
J
-
Subscale
1
2
3
4
5
6
7
8
9
K
L
M
N
-
10
11
12
13
A
B
1. Legislative
2. Executive
3. Judicial
4. Global
5. Local
6. Liberal
7. Conservative
C
I
D
J
E
K
F
L
G
M
-.09
.22
.34
.10
.24
.02
.06
.22
.50
.54
-.14
-.10
.23
.05
.31
.04
.31
.34
.22
-.20
-.01
.66
.29
.20
.37
.27
.18
.11
.13
.29
.51
.20
-.11
.22
.25
.18
.34
.24
.11
-.35
.13
.20
.24
.06
.03
.34
.15
.32
.17
.08
.22
.24
.09
.04
.30
.17
.03
.33
.52
.19
.37
.05
.26
.33
.37
-.42
.07
.65
-.05
.05
.21
.16
.29
.33
.12
-.10
-.08
.15
.33
.44
.36
.22
.42
.23
H
N
8. Hierarchical
.30
.32
.45
.23
.18
.22
.41
.33
.28
.26
.20
.14
9. Monarchic
.40
.42
.30
.28
.34
.31
.35
.31
.25
.20
.41
.13
10. Oligarchic
.21
.41
.18
.18
.35
.26
.33
.30
.14
-.06
.44
.37
11. Anarchic
.35
.25
.34
.33
.31
.24
.33
.39
.34
.27
.25
.23
12. Internal
.64
.21
.23
.34
.34
.28
.05
.27
.23
.40
.16
-.28
13. External
.07
.21
.36
.35
.15
.20
.31
.28
.35
.21
-.23
.07
Note. Numbers above the diagonal are for the Nanjing sample.
Numbers below the diagonal are for the Hong Kong sample. Hong
Kong n = 854. Nanjing n = 215.
TABLE 5 Pearson Correlation Matrix for the Subscales in the Study Process
Questionnaire and Thinking Styles Inventory
Legend for Chart:
A
B
C
D
E
F
-
Subscale
SM HK
SM NJ
DM HK
DM NJ
AM HK
G
H
I
J
K
L
M
-
AM
SS
SS
DS
DS
AS
AS
NJ
HK
NJ
HK
NJ
HK
NJ
A
B
F
J
C
G
K
D
H
L
E
I
M
Legislative
.05
.21(*)
.26(*)
-.09
.20
.33(*)
.28(*)
-.02
.10(*)
.24(*)
-.12
.02
Executive
.24(*)
.20(*)
.18(*)
.23(*)
.20
-.04
.17(*)
.26(*)
.20(*)
.08
.34(*)
.20
-.00
.17(*)
.38(*)
-.02
.15
.49(*)
.40(*)
-.13(*)
.26(*)
.31(*)
-.11
.18
Judicial
Global
.17(*)
.18(*)
.25(*)
.05
.13
.13
.24(*)
.13(*)
.13(*)
.04
.02
.00
Local
.17(*)
.21(*)
.26(*)
.18
.14
.10
.24(*)
.17(*)
.30(*)
.15
.23(*)
.23(*)
Liberal
.07
.20(*)
.37(*)
-.15
.08
.53(*)
.37(*)
-.03
.19(*)
.31(*)
-.31(*)
.18
Conservative
.25(*)
.19(*)
.07
.36(*)
.19
-.16
.07
.36(*)
.19(*)
.00
.47(*)
.07
Hierarchical
-.01
.13(*)
.36(*)
-.13
.23(*)
.39(*)
.32(*)
-.04
.39(*)
.35(*)
-.14
.49(*)
Monarchic
.22(*)
.26(*)
.24(*)
.20
.30(*)
.21
.28(*)
.22(*)
.29(*)
.23(*)
.18
.31(*)
Oligarchic
.18(*)
.10
.13(*)
.23(*)
.24(*)
.14
.13(*)
.19(*)
.12(*)
.23(*)
.23
.25(*)
Anarchic
.04
.10
.24(*)
.14
.28(*)
.27(*)
.25(*)
.08
.18(*)
.26(*)
.08
.30(*)
Internal
.07
.24(*)
.20(*)
-.02
.36(*)
.30(*)
.24(*)
.05
.07
.13
-.02
.10
External
.02
-.02
.22(*)
.07
.02
.24(*)
-.06
.09
-.02
.20(*)
.02
.22(*)
Note. HK = Hong Kong. NJ = Nanjing. Hong Kong n = 854. Nanjing
n = 215. SM = Surface Motivation. DM = Deep Motivation.
AM = Achieving Motivation. SS = Surface Strategy. DS = Deep
Strategy. AS = Achieving Strategy.
(*) p < .01.
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Received July 6, 1999
Research for this project was supported in part by the Committee on
Research and Conference Grants as administered by The University of Hong
Kong.
Preparation of this article was supported in part under the Javits Act
Program (Grant No. R206R50001) as administered by the Office of Educational
Research and Improvement, U.S. Department of Education. Grantees
undertaking such projects are encouraged to express freely their
professional judgment. This article, therefore, does not necessarily
represent the position or policies of the Office of Educational Research
and Improvement or the U.S. Department of Education, and no official
endorsement should be inferred.
Address correspondence to Li-fang Zhang, Department of Education, The
University of Hong Kong, Pokfulam Road, Hong Kong; lfzhang@hkucc.hku.hk (email).
~~~~~~~~
By Li-Fang Zhang, Department of Education The University of Hong Kong and
Robert J. Sternberg, Department of Psychology Yale University
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Title: The psychometric properties of the Learning Style Inventory and
the Learning Style Questionnaire: Two normative measures of learning
styles.
Subject(s): LEARNING strategies; LEARNING ability
Source: South African Journal of Psychology, Jun2000, Vol. 30 Issue 2,
p44, 9p, 14 charts, 1 diagram
Author(s): Pickworth[*], Glynis E.; Schoeman, Willem J.
THE PSYCHOMETRIC PROPERTIES OF THE LEARNING STYLE INVENTORY AND THE
LEARNING STYLE QUESTIONNAIRE: TWO NORMATIVE MEASURES OF LEARNING STYLES
David Kolb has provided a detailed, useful and widely accepted theory of
experiential learning and learning styles. He developed the Learning Styles
Inventory (LSI) to assess four learning abilities and four learning styles.
Kolb's work is viewed favourably for establishing the existence of
individual differences in learning styles, but the major criticism against
his work is focused on his method of measuring learning styles and more
specifically on the psychometric properties of the LSI. The LSI is an
ipsative instrument and the limitations placed on the statistical analysis
of data of ipsative measures makes it inappropriate for reliability and
validity evaluation of the instrument. In this study the psychometric
properties of two normative measures of learning styles, a normative
version of the LSI (referred to as the LSI-Likert) and the Learning Style
Questionnaire (LSQ), are investigated. A review of the literature on the
LSI is presented and the development of normative versions of the LSI is
reviewed. First-year university students registered for either a science or
human sciences degree completed the two normative instruments. The internal
reliability of the four learning ability scales was determined using alpha
coefficient. The internal reliability of the LSI-Likert and LSQ was found
to be relatively high. The presence of a response bias for both instruments
was suspected. It appeared that the LSI-Likert was more successful than the
LSQ in differentiating learning abilities and styles in the sample used.
Item factor analysis demonstrated two bipolar factors in line with Kolb's
theory for the LSQ. The four-factor solution for the LSI-Likert produced
four factors which to some extent represented the four learning abilities.
* To whom correspondence should be addressed.
Kolb's theory of experiential learning
The experiential learning movement emerged through the theories and work of
John Dewey, Kurt Lewin and Jean Piaget. The work of these three theorists
form the foundation of Kolb's theory of experiential learning (Hickcox,
1990). According to Kolb (1984) learning is a continuous process through
which knowledge is derived from, and modified through, testing out the
experiences of the learner. Kolb also postulated that learning requires the
resolution of conflicts between dialectically opposed modes of adaptation
to the world. On the prehension (perceiving) dimension the process of
apprehension (concrete experience) opposes the process of comprehension
(abstract conceptualisation). Kolb referred in this regard to research on
brain hemisphere dominance that provides evidence that "there are two
distinct, coequal, and dialectically opposed ways of understanding the
world" (Kolb, 1984, p. 48), the right-brain mode corresponding to
apprehension and the left-brain mode corresponding to comprehension. On the
transformation (processing) dimension the process of intention (reflective
observation) opposes the process of extension (active experimentation).
Kolb stated that Carl Jung's concepts of introversion (intention) and
extraversion (extension) best describe this transformation dimension.
Learning results from the resolution of conflicts between involvement in
new experiences versus conceptualising, and between acting versus
reflecting.
The process of experiential learning is described as a four-stage cycle
involving four learning abilities: Concrete Experience (CE), Reflective
Observation (RO), Abstract Conceptualization (AC), and Active
Experimentation (AE) (see Figure 1). It is theorized that one learns best
by going through the CE, RO, AC, AE sequence of the cycle and that people
learn more effectively as they develop learning abilities in their areas of
weakness. In the experiential learning process concrete experience is
followed by observation and reflection, leading to the formation of
abstract concepts that result in hypotheses to be tested in future actions
and this in turn leads to new experiences. The learning cycle is
continuously recurring and is directed by individual needs and goals (Kolb,
Rubin & McIntyre, 1984).
The learning abilities are represented on a two dimensional learning styles
plane by two bipolar dimensions, one a vertical axis running from CE to AC,
and the other a horizontal axis running from AE to RO. The four quadrants
formed by the intersection of the two bipolar axes represent the four
learning styles derived from the combination of two preferred learning
abilities: Diverger (CE and RO), Assimilator (AC and RO), Converger (AC and
AE), and Accommodator (CE and AE). (See Figure 1). Kolb (1984) took a
contextualist view of learning styles and stated that "psychological types
or styles are not fixed traits but stable states" (p. 63). These stable
states or enduring patterns of individual human behaviour arise from
consistent patterns of transactions between the person and the environment.
However, an individual can adjust their learning style according to the
demands of the task at hand.
Sugarman (1985) pointed out that as Kolb's theory combines a theory of
learning and a theory of learning styles there are at least three
components that must be addressed in an evaluation of his work:
(a) establishing the existence of individual differences in learning
styles;
(b) effectively measuring these differences, if they are found to exist;
and
(c) validating the cyclical model of learning.
Kolb's work is viewed favourably for aspects (a) and (c), but the major
criticism against his work is focused on his method of measuring learning
styles and more specifically on the psychometric properties of the Learning
Styles inventory (LSI). in the following section literature reporting on
the psychometric properties of the LSI is summarized.
Assessment of learning styles: The Learning Styles Inventory (LSI)
The LSI was developed by Kolb and takes the form of a self-description,
self-scoring test that aims to help an individual to identify their
relative emphasis on the four learning abilities within the learning cycle
(CE, RO, AC and AE) as well as their predominant learning style (Diverger,
Assimilator, Converger or Accommodator).
The LSI-1976
According to Hickcox (1990) Kolb published the first version of the LSI in
1971. However, the inventory is generally referred to in the literature as
the 1976 version. This version will be referred to as the LSI-1976. The
LSI-1976 consists of nine sets of words, each set consisting of four words.
The four words, each representing one of the four learning abilities, are
presented in the same order (CE, RO, AC, AE) throughout so that the words
associated with each of the four learning abilities are grouped in columns
to facilitate scoring for the self-scoring format of the inventory. A
respondent rank orders the four words in each of the nine sets according to
how well he/she perceives each word as describing his/her individual
learning style. The rankings for only six of the nine items, that is, for
only 24 of the 36 words contribute to the scores for the four learning
abilities CE, RO, AC and AE. The other twelve words serve as distracters.
Two combination scores AC-CE (that indicates the extent to which an
individual emphasizes abstractness over concreteness) and AE-RO (the extent
that an individual emphasizes action over reflection) are calculated.
By plotting these scores on the vertical and horizontal axes respectively,
the respondent is positioned in one of the four quadrants representing one
of the four learning styles. Due to the ranking format the instrument is an
ipsative measure (Kerlinger, 1973). Research results pertaining to the
psychometric properties of the LSI-1976 are summarized in Table 1.
The LSI-1985
A revised version of the LSI was published in 1985. This version of the LSI
will be referred to as the LSI-1985. The format was changed and the LSI1985 consists of 12 sentence-completion items and therefore has more items
than the LSI-1976. Each sentence has four word endings corresponding to the
four learning abilities. As for the LSI-1976 the four words are presented
in the same order (CE, RO, AC, AE) throughout to facilitate the scoring of
the self-scoring inventory. A respondent rank orders the four words for
each sentence or item. The ratings for all 12 words are summed for each of
the learning abilities CE, RO, AC, AE. These scores are used to calculate
the combination scores AC-CE and AE-RO and by plotting these two scores on
the corresponding bipolar axes, the respondent is assigned to one of the
four quadrants, each representing one of the four learning styles. Kolb
thus increased the number of items and placed the words in the context of a
sentence, but remained committed to the ranking format and the instrument
remains an ipsative measure. Research results pertaining to the
psychometric properties of the LSI-1985 are summarized in Table 2.
The LSI IIA
In 1993 a further revised version of the LSI was published. The instrument
is called the LSI IIA and the following information is given in the
publishers McBer & Company's catalogue: "The LSI IIA has a revised
questionnaire format and scoring key. The twelve-question inventory now has
scrambled sentence endings and new scoring instructions that have proved to
have high test-retest reliability in recent studies." (p. 11). The same 12
items are presented in the same order, but the four word endings for each
item have been randomized. Kolb remains committed to the ranking format and
the instrument remains an ipsative measure. To date no literature has been
found relating to the LSI IIA.
Problems associated with ipsative measures
Hicks (1970) defined an ipsative measure as follows: "A format in which
respondents compare or rank items will always yield purely ipsative scores
if respondents rank all alternatives per item, if all these rankings are
scored, and if alternatives representing all assessed variables are
compared with each other and presented for preferential choice by the
respondent." (p. 170). According to this definition the LSI-1985 and LSI
IIA are purely ipsative instruments. The LSI-1976 is a partially ipsative
instrument as not all alternatives ranked by respondents are scored.
Kolb remained committed to the ranking format of the inventory thus making
it an ipsative measure. An ipsative measure is designed to measure withinindividual differences, and this creates difficulties when researchers try
to make between-subjects analyses. Statistically the ipsative measure
results in a between-subjects sum of squares of zero and one individual's
preferences cannot be compared with another's (Merritt & Marshall, 1984).
Cornwell and Dunlap (1994) stated that ipsative scores cannot be factored
and that correlation-based analysis of ipsative data produced
uninterpretable and invalid results. As ipsative scores contain only
categorical information across individuals multinomial statistical
techniques are appropriate. Instead of using the sum of the rank ordered
ipsative scores, Cornwell and Dunlap suggested rank ordering the summed
responses across the four learning modes for each individual and then
applying multinomial techniques to this categorical data. Cornwell,
Manfredo and Dunlap (1991) recommended the use of non-ipsative scores for
evaluating the construct validity of the LSI.
The minimum requirement for an instrument's scores to be amenable to
construct interpretations is that the instrument must yield internally
consistent scores (Tenopyr, 1988). Tenopyr states that the internal
consistencies of scales of ipsative inventories are interdependent and that
there is a possibility for artifactual internal consistency to be generated
in such inventories. This places limitations on the usefulness of
reliability data for ipsative inventories and such instruments are not
suitable for psychometric evaluation and should not be used for making
important decisions concerning individuals. ipsative scores are also not
suitable for theory building (Hicks, 1970). The usual statistics are not
applicable to ipsative measures because of the lack of independence and
negative correlations among items and analysis of correlations, as in
factor analysis, could be seriously distorted by the negative correlations
(Kerlinger, 1973). Many of the studies tabled in this article have treated
ipsative data normatively and the results of such studies are of little
value. Although an ipsative measure is designed to measure intra-individual
differences, the limitations placed on the statistical analysis' of data of
ipsative measures makes it inappropriate for reliability and validity
evaluation of the instrument.
Normative versions of the LSI
From previous studies using normative forms of the LSI-1976 Marshall and
Merritt (1986) concluded that a semantic differential format could be used
to develop a reliable and valid normative assessment instrument to assess
individual's preferences for ways of learning as proposed by Kolb. They
developed the Learning Style Questionnaire (LSQ). In the experimental phase
100 semantic differential word pairs were compiled, with 25 word pairs for
each of the four scales. A five-point scale was used by respondents to rate
the consistency with which the opposing words characterized their
particular learning style. This experimental form of the LSQ was
administered to 543 university students from randomly selected classes at
two universities. Thirty-seven different majors were represented. About
three-fourths of the subjects were under 23 years of age; two-thirds were
female and about two-thirds had completed at least two years of college.
The 100 items were analyzed and 40 items were selected for the final
instrument, 10 items for each of the four scales (CE, RO, AC, AE). The
internal consistency reliabilities based on alpha coefficient for the
finalized 40-item LSQ were: CE = .78, RO = .86, AC = .85, AE = .88, CE-AC =
.90 and RO-AC = .93. Least squares factor analysis of the items was used to
examine the construct validity of the instrument. All 40 items loaded on
bipolar factors in accordance with Kolb's proposed learning abilities and
styles. The authors concluded that the reliability estimates for both
bipolar dimensions were very high and that the construct validity for these
dimensions had been demonstrated. They recommended that the instrument be
used to determine individual learning styles as well as for research
purposes.
Romero, Tepper and Tetrault (1992) developed a normative, two-dimensional
instrument to measure learning style. Rather than construct an instrument
that assesses the four learning abilities, the authors constructed an
instrument that assessed the two dimensions concreteness/abstractness and
reflection/action directly. The instrument consists of 14 pairs of selfdescriptive anchor statements, each pair on a six-point Likert scale. Seven
bipolar items assess concreteness versus abstractness, and seven bipolar
items assess reflection versus action. The instrument was administered to
two independent samples. The one sample consisted of 507 undergraduate
students in the fields of liberal arts, business and engineering. The
average age was about 21 years and 53% were male. The instrument was
administered once to this sample. The second sample consisted of 153 MBA
students and the instrument was administered twice with a six week
interval. The average age was 28 years and 65% were male. The internal
consistency alpha coefficient for the concreteness/abstract scale was .84
for sample I and .78 for sample 2. The alpha coefficient for the
reflective/action scale was .86 for sample 1 and .80 for sample 2. The
test-retest stability for sample 2 was .75 for the concreteness/abstract
scale and .73 for the reflection/action scale. The authors reported that
the internal consistency and test-retest stability were acceptable. The two
dimensional structure of the instrument was confirmed by factor analysis of
both samples using LISREL. Validity support was obtained by comparing
student majors with learning style for sample 1.
Geiger, Boyle and Pinto (1993) constructed a normative version of the LSI1985 that was scored on n seven-point Likert scale consisting of 48 (12
sentence items X four word endings) separate items randomly presented. The
standard LSI-1985 and the normative versions were administered to 455
business administration students (first, second and third year students).
The age range was from 18 to 47 years (mean age = 21.4 years) and 281 were
male and 174 female. Alpha coefficient internal consistency reliability
measures for the ipsative version were as follows: CE = .83, RO = .81, AC =
.85 and AE = .84. Alpha coefficient reliabilities for the normative version
were as follows: CE = .83, RO = .77, AC = .86 and AE =.84. Correlations of
the four scale scores were used to determine the equivalence of the
ipsative and normative versions. Correlations ranged from .368 to .526
indicating a moderate amount of agreement. Adjusted scale correlations
ranged from .466 to .615 with three of the four coefficients exceeding .50.
Separate factor analyses were performed on the two versions. For the
ipsative version two strong bipolar dimensions were identified running from
CE to RO and from AE to AC. These dimensions are not congruent with Kolb's
theorized bipolar dimensions. Analysis of the normative version did not
produce any bipolar dimensions, but strong support for the four separate
learning abilities was obtained. The comments made in the previous section
on the inappropriate use of the statistical analysis of data of ipsative
measures pertain to these findings.
Method
Measures
Due to the problems relating to ipsative measures described previously it
was decided to investigate the psychometric properties of two normative
measures of learning style. The Learning Style Questionnaire (LSQ)
developed by Marshall and Merritt (1986), described previously, and the
Likert-scale form of the LSI-1985 developed by Geiger et al. (1993),
described previously, were used. The five-point Likert-scale version of the
LSI used in this study will be referred to as the LSI-Likert. These two
instruments were obtained from their American authors and were available
only in English. This study can be seen as a pilot study to start
investigating the reliability and construct validity of the two instruments
in a South African population.
Sample
First-year students registered for full-time courses presented in English
at the University of Pretoria in the fields of science (BSc) and the human
sciences (BA) participated in the study. A total of 464 students were
tested at the beginning of the 1995 and 1996 academic years. Due to some
incomplete answer sheets 419 answer sheets could be scored for the LSILikert and 415 for the LSQ. In scoring the four ability scales (CE, RO, AC,
AE) missing or ambiguous responses were substituted with the group average
score for an item, for a maximum of two items per questionnaire. For the
LSI-Likert the group average score was substituted for one item in 43 cases
and for two items in 9 cases. For the LSQ the group average score was
substituted for one item in 39 cases and for two items in 4 cases. The
allocation of a learning style to a subject is determined by the composite
scores ACCE and AE-RO. If a zero score was obtained for either of these
composite scores a subject was not allocated a learning style.
There was a higher proportion of females than males in the sample with
approximately two-thirds females. Regarding home language, 35% of the
students were English first language speakers and for the rest English was
a second language. The african cultural group comprised 50% of the sample
and the white group 38%. The rest were from the Coloured, Indian and Asian
cultural groups. The two fields of study (BSc and BA) were fairly evenly
represented. The BSc field of study represented 46% of the sample and
comprised students studying mainly for degrees in the biological and
agricultural sciences, and engineering fields. The BA field of study
represented 54% of the sample and comprised first-year Psychology students.
The order in which the LSI-Likert and LSQ was completed was varied with 51%
of the students completing the LSI-Liken followed by the LSQ, and 49%
completing the LSQ followed by the LSI-Likert. Hotteling's T test indicated
that the four scales for the LSI-Likert and LSQ did not have equal vector
of means for these two test groups and it was concluded that the order in
which the LSI-Likert and the LSQ were completed did not affect the scores
obtained for the two instruments (Pickworth, 1997).
Results and discussion
Item analysis and internal reliability
Item analysis was done for the LSI-Likert and the LSQ using the ITEMAN
Conventional Item analysis Program (Assessment Systems Corporation, 1993).
Intercorrelations and alpha coefficient reliabilities for the four scales
of the two instruments were also calculated using the ITEMAN program. The
LSI-Likert item-scale correlations for the CE scale ranged from .29 to .61
(mean = .47), for the RO scale from .30 to .59 (mean = .46), for the AC
scale from .34 to .61 (mean = .52), and for the AE scale from .33 to .62
(mean = .49). Intercorrelations for the four scales are given in Table 3
and the alpha coefficients in Table 4.
The LSQ item-scale correlations for the CE scale ranged from .47 to .69
(mean = .58), for the RO scale from .48 to .72 (mean = .59), for the AC
scale from .41 to .68 (mean = .56), and for the AE scale from .44 to .70
(mean = .58). Intercorrelations for the four scales are given in Table 3
and the alpha coefficients in Table 4.
Response bias
A five-point Likert scale was used for the LSI-Likert. Options 1 and 2 (Not
at all like me and Somewhat unlike me) were endorsed at most by 35% of
respondents. For 28 out of the 48 items options 1 and 2 where used by 10%
or less of respondents. Relatively high item means, ranging from 3.0 to
4.7, reflect this. This could indicate a response bias.
Each item of the LSQ consists of a word pair on a five-point semantic
differential scale. Each of the two words in an item represent opposite
learning abilities. In the list of word pairs below the item number is
given and the word highlighted was endorsed by less than 20% of the
respondents using one of the two response options Generally (Most of the
time) or Over half the time:
The Abstract Conceptualisation scale
15
consider
impulsive
17
reason
hunch
26
careful
emotional
27
logical
sentimental
29
thinking
instinctive
34
resolving
feeling
36
intellectual
emotional
The Concrete Experience scale
4
sensing
thinking
5
premonition
reason
12
perceptual
intellectual
18
impulsive
planning
25
intuitive
reasoning
30
hunch
logical
The Active Experimentation scale
6
active
reserved
23
involved
distant
39
solve
reflect
40
exercise
view
The Reflective Observation scale
31
passive
active
37
reflective
productive
The above could reflect a response bias in which "logical" (Abstract
Conceptualization) words are favoured over "feelings" (Concrete Experience)
words, and "active" (Active Experimentation) words are favoured over
"passive/reflective" (Reflective Observation) words. The "logical" and
"active" words may be perceived to be more socially correct in a learning
context. It must also be remembered that the majority of the students are
not English first language speakers and may have experienced difficulty
with the meanings of the words. In some cases the words more commonly
endorsed may be words they are more familiar with.
Learning style frequency
The distributions of learning styles for the BSc and BA groups as measured
by the LSI-Likert and LSQ were calculated using the FREQ procedure of the
SAS statistical package (SAS Institute Inc., 1990). This procedure was also
used to calculate the Chi-square test of significance for the frequencies.
The frequency distributions of learning styles as measured by the LSILikert are given in Table 5 and as measured by the LSQ in Table 6.
For the LSI-Likert the Chi-square statistic had a value of 27.49 with three
degrees of freedom which was significant at the 5% level of significance.
There was thus a strong association between field of study and learning
style as measured by the LSI-Likert. There were more Divergers in the BA
group, more Convergers in the BSc group and more Accommodators in the BA
group. Assimilators were fairly equally represented in the BSc and BA
groups. Except that one would expect more Assimilators in the BSc than the
BA group, these results are in line with the descriptions of the learning
styles (Kolb, 1984) and thus provide some evidence of construct validity
for the learning style constructs for the LSI-Likert.
For the LSQ the Chi-square statistic had a value of 7.556 with three
degrees of freedom which is not significant at the 5% level of
significance. There is thus no strong association between field of study
and learning style as measured by the LSQ.
Item factor analysis
Factor analysis of the items of the LSI-Likert and the LSQ was performed
using the principal factor method to extract factors, followed by a direct
quartimin (oblique) rotation of factors. The BMDP4M factor analysis
statistical package (BMDP Statistical Software Inc., 1993) was used. The
factor loadings for the two-factor and four-factor solution for the LSILikert are reported in Tables 7.1 and 7.2. The factor loadings for the twofactor and four-factor solution for the LSQ are reported in Tables 8.1 and
8.2. The two-factor solution was expected to yield the CE -- AC and AE -RO bipolar axes and the four-factor solution was expected to yield the four
learning abilities (CE, RO, AC, AE).
The first factor for the two-factor solution for the LSI-Likert (see Table
7.1) combines mainly AC and RO items and would appear to represent the
Assimilator learning style. The second factor combines mainly CE and AE
items and would appear to represent the Accommodator learning style. The
anticipated bipolar axes did not emerge and the LSI therefore does not
support the bipolar axes theorized by Kolb.
For the four-factor solution of the LSI-Likert (see Table 7.2) the four
factors appear to represent to some extent each of the four learning
abilities with the first factor representing AC, the second AE, the third
CE and the fourth RO. However, the first two factors combine items
representing other learning abilities.
Two bipolar factors, AE -- RO and AC -- CE, emerge for the two-factor
solution for the item factor analysis for the LSQ (see Table 8.1). The LSQ
therefore supports the bipolar axes as theorized by Kolb.
For the four-factor solution of the LSQ (see Table 8.2) the first factor is
bipolar representing the AE -- RO axis and supports the construct validity
of the AE and RO learning abilities. The second factor appears to represent
the CE learning ability, the third factor the AC learning ability and the
fourth factor incorporates RO, AC and one CE item. The four learning
abilities are therefore supported for the LSQ. Both the delineation of the
two bipolar AE -- RO and AC -- CE axes, as well as the four learning
abilities (CE, RO, AC, AE) reflects the careful developmental work done by
Marshall and Merritt to produce an instrument that measures the constructs
proposed by Kolb in his experiential learning theory.
Conclusion
The results indicate that the internal reliability of the LSQ is somewhat
higher than for the LSI-Likert (see Table 4). The presence of a response
bias on both instruments is suspected, it would appear that the LSI-Likert
was more successful than the LSQ in differentiating learning abilities and
styles in the sample used. Frequency distributions of learning style
demonstrated more differentiated patterns for the LSI-Likert than for the
LSQ (see Tables 5 and 6). The
Chi-square statistic was significant only for the LSI-Likert. Except that
one would expect a higher percentage of Assimilators in the BSc group than
the BA group, the distributions of learning styles as measured by the LSILikert were in accordance with Kolb's theory. Item factor analysis of the
LSI-Likert and the LSQ demonstrates that the LSQ produces two bipolar
factors in line with Kolb's proposed theoretical constructs whereas the
LSI-Likert did not (see Tables 7.1 and 8.1). The four-factor solution for
the LSI-Likert and the LSQ produces evidence for the four learning
abilities (see Tables 7.2 and 8.2).
From the results of this study it would appear that the normative measures
of learning style used in this study show promise for use in counselling,
academic advising and for research purposes. This study did not make
comparisons between gender, different cultural groups and English speaking
versus non-English speakers. The effect of these variables needs to be
investigated. The reliability and construct validity of the two instruments
should also be investigated further.
Acknowledgement
The financial assistance of the Centre for Science Development of the Human
Sciences Research Council towards this research is hereby acknowledged.
Opinions expressed in this article and conclusions arrived at, are those of
the authors and are not necessarily to be attributed to the Centre for
Science Development of the Human Sciences Research Council.
Table 1
Summary of reliability and validity findings for the LSI-1976
Legend for Chart:
A
B
C
D
E
-
Author(s)
N
Reliability
Bipolar theory
Validity
A
B
C
D
E
Plovnick (1974)
N = 27
Test-retest (3-4 month interval)
Pearson product-moment correlations
CE = .48 RO = .73 AC = .65 AE = .64
CE-AC = .61 RO-AE = .71
-Supported
1976 LSI Technical Manual
(cited in Geller, 1979)
N = 687
Internal consistency
Spearman-Brown split-half
correlations
CE = .55 RO = .62 AC = .75 AE = .66
AC-CE = .74 AE-RO = .82
Test-retest (3, 6 and 7 month
intervals)
--N = 23
CE = .48 RO = .51 AC = .73 AE = .43
AC-CE = .51 AE-RO = .48
N = 18
CE = .46 RO = .34 AC = .64 AE = .50
AC-CE = .53 AE-RO = .51
N = 42
CE = .49 RO = .40 AC = .40 AE = .33
AC-CE = .30 AE-RO = .43
Freedman & Stumpf (1978, 1980)
N = 412
Internal consistency: Alpha
coefficient
Moderate support
Limited support
N = 1179
CE = .40 RO = .57 AC = .70 AE = .47
Test-retest (five-week interval)
Moderate support
Limited support
N = 101
Pearson product-moment correlations
CE = .39 RO = .49 AC = .63 AE = .47
AC-CE = .58 AE-RO = .51
Whitney & Caplan (1978)
N = 111
--Not supported
Wunderlich & Gjerder (1978)
N = 24
Test-retest (six-week interval)
Correlations ranged from .44 to .72
-Not supported
Geller (1979)
N = 50
Test-retest (31 day interval)
Pearson product-moment correlations
CE = .56 RO = .52 AC = .59 AE = .61
AC-CE = .70 AE-RO = .55
---
West (1982)
N = 42
--Not supported
Fox (1984)
N = 54
--Not supported
Garvey, Bootman, Mc Ghan & Meredith (1984)
N = 501
Internal consistency
Alpha coefficient
CE= .30 RO = .58 AC = .60 AE = .36
Spearman-Brown Prophecy Formula
AC-CE = .72 AE-RO = .79
-Partial support
Merritt & Marshall (1984)
N = 187
Internal consistency: Alpha
coefficient
CE = .29 RO = .58 AC = .52 AE = .41
Supported
--
Sims, Veres, Watson & Buckner (1986)
N = 438
Internal consistency: Alpha
coefficient
CE = .48 RO = .58 AC = .52 AE = .23
--
-N = 309
Test-retest (3 applications at
five-week intervals)
N = 132
Zero-order correlation coefficients
CE = .45 to .60 RO = .46 to .57
AC = .51 to .60 AE = .42 to .46
N = 739
--
Katz (1986)
Supported
Supported
Wilson (1986)
N = 102
Internal consistency
Split-half correlation coefficients
CE = .15 RO = .53 ac = .49 ae = .41
AC-CE = .45 AE-RO = .52
Not supported
--
N = 51
Test-retest (six-week interval)
CE = .40 RO = .77 AC = .63 AE = .40
AC-CE = .53 AE=RO = .61
Green, Snell & Parimanath (1990)
N = 147
--Supported
Lashinger (1990)
--
--Partial support
Review of experiential learning theory research in the nursing
profession.
Welman & Huysamen (1993)
N = 573
Internal consistency: Alpha
coefficient
AC-CE = .63 AE-RO = .55
-Partial support
Table 2
Summary of reliability and validity findings for the LSI-1985
Sims, Veres, Watson & Buckner (1986)
N = 181
Internal consistency: Alpha
coefficient
CE = .76 RO = .84 AC = .85 AE = .82
---
N = 131
Test-retest (3 applications at
five-week intervals)
N = 94
Zero-order correlation coefficients
CE = .24 to .44 RO = .39 to .66
AC = .42 to .50 AE = .56 to .62
Highhouse & Doverspike (1987)
N = 111
--Partial support
Veres, Sims & Shake (1987)
N = 230
Internal consistency: Alpha
coefficient
CE = .82 RO = .85 AC = .83 AE = .84
---
N = 230
Test-retest (3 applications at
three-week intervals)
Zero-order correlation coefficients
CE = .30 to .52 RO = .36 to .46
AC = .45 to .56 AE = .28 to .44
---
Atkinson (1988)
N = 26
Test-retest (nine-day interval)
Pearson product-moment coefficient
CE = .57 RO = .40 AC = .54 AE = .59
AC-CE = .69 AE-RO = .24
---
Cornwell, Manfredo & Dunlap (1991)
N = 317
-Not supported
Not supported
Ruble & Stout (1991)
N = 231
Internal consistency: Alpha
coefficient
CE = .82 RO = .79 AC = .81 AE = .82
---
N - 139
Test-retest (five-week interval)
Pearson product-moment correlations
CE = .18 RO = .46 AC = .36 AE = .47
AC-CE = .22 AE-RO = .54
Veres, Sims & Locklear (1991) Random ordering of the four
sentence endings of the LSI-1985
--
Internal consistency
Mean Alpha coefficients for three
applications
---
N = 711
CE = .56 RO = .67 AC = .71 AE = .52
N = 1042
CE = .67 RO = .67 AC = .74 AE = .58
Test-retest (3 applications at
eight-week intervals)
Zero-order correlation coefficients
N = 711
CE = .92 to .96 RO = .93 to .97
AC = .94 to .97 AE = .91 to .96
N = 1042
CE = .97 to .99 RO = .97 to .98
AC = .97 to .99 AE = .96 to .99
Geiger, Boyle & Pinto (1992)
N = 718
-Not supported
Not supported
Cornwell & Manfredo (1994)
N = 292
--Not supported
Table 3
Intercorrelations for the scales of the LSI-Likert and the LSQ
CE
RO
AC
AE
CE
-.254
.335
RO
.252
-.411
AC
-.424
.033
-AE
.077
-.521
.265
Correlations above the diagonal are for the LSI-Likert
diagonal are for the LSQ.
.454
.305
.439
-and those below the
Table 4
Alpha coefficients for the scales of the LSI-Likert and the LSQ
LSI-Likert
Geiger et al.
CE
.741
.83
RO
.717
.77
AC
.799
.86
AE
.799
.84
LSQ
.801
.812
.823
.839
Marshall & Merritt
.78
.86
.85
.88
The alpha coefficients reported by Geiger et al. (1993) and Marshall &
Merritt (1986) are included for comparison.
Table 5
Frequency of learning styles as measured by the LSI-Likert for the BSc and
BA fields of study
Legend for Chart:
A
B
C
D
-
Learning style
BSc
BA
Total
A
B
C
D
3
1.78%
26
13.47%
29
8.01%
Assimilator
Frequency
Column %
45
26.63%
47
24.35%
92
25.41%
Converger
Frequency
Column %
96
56.80%
72
37.31%
168
46.41%
Accommodator
Frequency
Column %
25
14.79%
48
24.87%
73
20.17%
Diverger
Frequency
Column %
Total
169
193
362
46.69%
53.31%
100%
The Chi-square has a value of 27.49 with three degrees of freedom which is
significant at the 5% level.
Table 6
Frequency of learning styles as measured by the LSQ for the BSc and BA
fields of study
Legend for Chart:
A
B
C
D
-
Learning style
BSc
BA
Total
A
B
C
D
8
4.32%
9
4.57%
17
4.45%
Assimilator
Frequency
Column %
42
22.70%
40
20.30%
82
21.47%
Converger
Frequency
Column %
123
66.49%
118
59.90%
241
63.09%
12
6.49%
30
15.23%
42
10.99%
Diverger
Frequency
Column %
Accommodator
Frequency
Column %
Total
185
197
382
48.43%
51.57%
100%
The Chi-square has a value of 7.556 with three degrees of freedom which is
not significant at the 5% level.
Table 7.1
Item factor analysis for the LSI-Likert: Oblique rotated factor loadings
for a two factor solution
Legend for Chart:
A
B
C
D
-
Scale
Item
Factor 1
Factor 2
A
Abstract Conceptualisation
B
C
D
4
6
10
11
19
24
25
26
29
32
43
47
.435
.501
.158
.457
.492
.245
.429
.388
.579
.186
.562
.462
-.045
.026
.130
-.170
.153
.142
-.085
.255
-.008
.358
.166
.235
Concrete Experience
1
7
14
15
18
22
28
31
33
38
42
45
-.028
-.011
-.119
.080
.406
.030
-.047
-.039
0.048
.208
.153
.216
.309
.231
.307
.440
.169
.122
.212
.237
.445
.474
.580
.179
Active Experimentation
5
12
13
17
20
34
35
37
39
41
44
48
.072
.418
-.160
.265
.077
.321
-.093
.048
.104
.137
.040
.291
.441
.243
.553
.102
.330
.294
.561
.458
.423
.512
.446
.279
Reflective Observation
2
3
8
9
16
21
23
27
30
36
40
46
.377
.301
.258
.195
.384
.194
.196
.336
.360
.280
.364
.304
-.046
-.223
.260
.094
-.002
.120
.147
-.079
-.056
.034
-.050
-.204
VP[*]
4.130
3.974
* The VP is the variance explained by the factor. It is computed as the sum
of squares for the elements of the factor's column in the factor loading
matrix.
Table 7.2
Item factor analysis for the LSI-Likert: Oblique rotated factor loadings
for a four factor solution
Legend for Chart:
A
B
C
D
E
F
-
Scale
Item
Factor
Factor
Factor
Factor
1
2
3
4
A
B
Abstract Conceptualisation
C
D
E
4
6
10
11
19
24
25
26
29
32
43
47
.316
.485
.187
.280
.551
.447
.394
.476
.486
.395
.625
.640
-.081
-.002
-.040
-.172
-.228
-.166
-.247
.167
-.066
.112
.088
.028
.000
-.132
.192
-.038
.037
.165
.073
-.054
-.035
.168
-.135
-.010
.205
.101
.026
.248
.050
-.153
.110
.027
.188
-.143
.074
-.062
.054
-.082
-.127
.212
.432
.029
-.202
.202
-.062
.178
.353
.356
.187
.016
.147
-.039
.210
.051
.037
-.025
-.025
-.066
.069
.351
.454
-.008
.500
.281
.756
.294
.014
.135
.678
.678
.628
.526
.077
.066
.277
-.081
.120
-.025
-.060
.083
.046
.167
.167
.014
-.243
-.037
-.093
.103
.398
.063
.438
.061
.417
.046
.404
.541
.563
.614
.468
.217
-.031
.003
.075
-.048
-.041
.108
.073
.034
.074
.083
.132
-.063
-.084
-.010
-.189
.066
.109
-.099
-.207
.122
.296
.230
.124
-.019
.043
-.195
.130
.069
.045
.035
.093
.023
.062
.057
.041
-.310
2.719
-.087
-.035
.054
-.030
-.025
.029
.177
.111
.020
.074
.026
.139
2.508
.382
.259
.002
.102
.372
.039
.278
.632
.581
.443
.572
.208
2.387
Concrete Experience
1
7
14
15
18
22
28
31
33
38
42
45
Active Experimentation
5
12
13
17
20
34
35
37
39
41
44
48
.240
.546
.093
.279
.060
.506
.188
.031
-.045
.056
.014
.401
Reflective Observation
2
3
8
9
16
21
23
27
30
36
40
46
VP[*]
.149
.111
.354
.181
.171
.226
.050
-.087
-.011
.030
.016
.150
4.350
* The VP is the variance explained by the factor. It is computed as the sum
of squares for the elements of the factor's column in the factor loading
matrix.
Table 8.1
Item factor analysis for the LSQ: Oblique rotated factor loadings for a two
factor solution
Legend for Chart:
A
B
C
D
-
Scale
Item
Factor 1
Factor 2
A
B
C
D
Abstract Conceptualisation
10
15
17
24
26
27
29
34
36
38
.159
.083
-.101
-.059
-.023
-.011
.041
-.099
-.008
.012
-.315
-.414
-.505
-.515
-.533
-.609
-.481
-.425
-.546
-.288
Concrete Experience
1
4
5
12
14
18
21
25
28
30
-.107
-.053
.071
.059
.009
-.081
-.078
.094
.117
.074
.330
.527
.495
.412
.491
.463
.612
.524
.398
.593
Active Experimentation
6
7
11
13
16
19
23
32
39
40
-.602
-.599
-.507
-.399
-.588
-.458
-.520
-.647
-.236
-.441
.003
.075
.138
.268
.075
-.181
-.197
-.035
-.307
-.071
Reflective Observation
2
3
8
9
20
22
31
33
35
37
.611
.512
.667
.666
.363
.338
.549
.349
.503
.434
-.056
-.022
-.102
-.045
.055
.248
.183
-.070
.039
.230
VP[*]
5.388
5.102
* The VP is the variance explained by the factor. It is computed as the sum
of squares for the elements of the factor's column in the factor loading
matrix.
Table 8.2
Item factor analysis for the LSQ: Oblique rotated factor loadings for a
four factor solution
Legend for Chart:
A
B
C
D
E
F
-
Scale
Item
Factor
Factor
Factor
Factor
1
2
3
4
A
B
C
D
E
-.111
.007
.217
.010
.072
.027
.067
.103
-.003
-.024
-.169
-.276
-.369
-.038
-.149
-.197
-.267
.009
-.104
.130
.163
.137
.136
.707
.486
.563
.216
.569
.620
.536
.171
.296
.371
-.094
.199
.109
.355
.035
.025
-.011
.153
.115
-.020
-.028
.125
.054
.123
-.118
-.126
-.092
.366
.561
.607
.538
.193
.505
.501
.671
.499
.534
-.037
-.122
-.034
-.023
-.586
-.004
-.279
.071
.033
-.154
.089
.097
.075
.035
.315
-.145
.035
-.136
-.060
-.120
-.026
.094
.086
.310
.195
.005
-.063
.128
-.049
.156
-.058
-.028
-.163
-.051
.135
.280
.159
.185
.250
.243
.067
.085
.204
.084
-.045
-.111
.082
-.007
.275
.051
Abstract Conceptualisation
10
15
17
24
26
27
29
34
36
38
Concrete Experience
1
4
5
12
14
18
21
25
28
30
Active Experimentation
6
7
11
13
16
19
23
32
39
40
.638
.649
.603
.450
.597
.425
.552
.668
.331
.470
Reflective Observation
2
-.532
.142
.133
.270
3
-.436
.173
.130
.239
8
-.538
.081
.055
.471
9
-.543
.190
.106
.454
20
-.284
.048
-.104
.227
22
-.358
.316
.053
-.088
31
-.513
.321
.070
.104
33
-.183
-.098
-.196
.513
35
-.459
.171
.083
.137
37
-.375
.194
-.147
.155
VP[*]
5.119
3.608
3.077
1.718
* The VP is the variance explained by the factor. It is computed as the sum
of squares for the elements of the factor's column in the factor loading
matrix.
DIAGRAM: Figure 1 Kolb's model of experiential learning, learning abilities
and learning styles.
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Wilson, D.K. (1986). An investigation of the properties of Kolb's Learning
Style Inventory. Leadership & Organization Development Journal, 7(3), 3-15.
Wunderlich, R. & Gjerde, C.L. (1978). Another look at Learning Style
Inventory and medical career choice. Journal of Medical Education, 53, 4554.
~~~~~~~~
By Glynis E. Pickworth[*], Faculty
P.O. Box 667, Pretoria 0001, South
and Willem J. Schoeman, Department
University, P.O. Box 524, Auckland
of Medicine, University of Pretoria,
Africa, E-mail: glynis@medic.up.ac.za
of Psychology, Rand Afrikaans
Park 2006, South Africa
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Title: A Strategy For Helping Students Learn How to Learn.
Subject(s): LEARNING, Psychology of; LEARNING strategies -- Study &
teaching; MARYGROVE College (Detroit, Mich.); MYERS-Briggs Type Indicator;
PERSONALITY & academic achievement
Source: Education, Spring2000, Vol. 120 Issue 3, p479, 8p, 4 charts
Author(s): McClanaghan, Mary Ellen
A STRATEGY FOR HELPING STUDENTS LEARN HOW TO LEARN
Engaging in the process of learning how to learn must include awareness of
how one perceives and processes material to be learned. Instructors can
enhance students' awareness by calling their attention to the ways and
means by which they are approaching their subject. Varying teaching methods
in each component of the instructional cycle on a regular basis and then
discussing what each student finds most compelling and most challenging
provides opportunities to raise awareness.
Introduction
Successful people know how to learn. This key to success has never been
more important than it is today in our information-saturated society.
Marygrove College faculty recognize the importance of learning how to learn
and have made "learning to learn," one of seven across the curriculum
emphases. This emphasis is begun in the First Year Seminar, an introductory
course with the overall goal of student success. To achieve this goal we
emphasize self-awareness and learning how to learn. Although there are
several approaches to these objectives, one approach is attention to
learning styles. In his book Powerful Learning, (1998) Ron Brandt states
that attention to individual learning styles is an avenue that leads to
learning how to learn. There are several definitions of learning style but
basically it is an individual's characteristic means of perceiving and
processing information. It is important to first validate a student's
dominant means of learning if we hope to challenge them to work in a style
in which they feel less competent.
Learning Styles
The First Year Seminar or Mg 102 as it is referred to at Marygrove
introduces students to the theory of learning styles. Students take a short
form of the Myers/Briggs Type Indicator (MBTI). This is one of the best
known psychological instruments in the world today. It is based on the
theory of Swiss psychologist, Carl Jung. According to Jung, a persons
attitude or readiness to act is determined by a preference for either
extraversion, which focuses on the external world, or introversion, which
focuses on the internal world. He also identified four behavioral functions
that, in various combinations constitute personality type: sensing,
intuition, thinking, and feeling. Myers and Briggs built on Jung's theory
by adding a judging / perceiving scale. The judging and perceiving scale
indicates if a person has a stronger attraction toward one of the
perceiving functions (sensing and intuition) or one the judging functions
(thinking and feeling). One component of Jung's theory that has a parallel
to the teaching and learning process comes from Jung's theory of human
development, which identifies two major objectives of psychic development'
perfection and completion. The perfection objective involves the human need
to develop one's own natural strengths and abilities to the maximum.
Completion continues the development process to strengthen also the less
dominant but potential abilities (Hanson and Silver, 1996). The value of
understanding one's learning style is first to develop one's natural
approaches to learning and then to develop the capacity to learn in ways
that may require more attention and effort. Learning how to learn in
different ways will assist students to be life long learners who are
capable of learning in various settings and situations. If students can be
successful by learning in ways that are most natural to them they are more
likely to take on the challenge to move toward Jung's concept of
completion.
Action Research
The faculty who teach The First Year Seminar, Mg 102, commit to meet
together several times during the semester to discuss student progress and
to continually seek to improve our service to our new students. As a result
of these meetings the idea surfaced to track our students' learning styles
in order to offer better instruction and support services.
Objectives of the study were to:
• work with the Student Support Service tutors and Career Services to
empower them to build on the introduction that the students receive
regarding learning style theory in the First Year Seminar course.
• provide workshops for students with very strong learning preferences to
assist them in developing their weaker styles.
• study and report the results of the learning style profiles of new Mary
grove students to identify any possible clustering of styles in our
population.
• Offer on-going support to the First Year Seminar faculty to help them
make better use of the information to assist their students' in monitoring
their own learning style development.
Several researchers and educators have adapted the theory of the MBTI and
developed instruments for specific uses. One example is "The Thoughtful
Education Model"' developed by J. Robert Hanson and Harvey F. Silver. Their
work centers on four learning skills identified by Jung: Sensing/Intuition
and Thinking/Feeling and offers very valuable application for educators. To
test the consistency of the short form of the Myers/Briggs instrument four
groups of students completed either "Learning Style Instrument for Adults,"
developed by Hanson and Silver or the Form G of the MBTI in addition to the
short form.
The following characteristics of the learning styles is based upon the
research of Hanson and Silver (1996) as reported in course materials
produced by Canter Educational Associates for Marygrove College's Master in
the Art of Teaching Program (1996).
ST / Sensing-Thinking Learning Style In the sensing-thinking learning style
(ST), students want concrete, specific information and need to know what is
right and wrong. They need a structured environment and lose interest if
things move too slow or don't seem practical. They learn best from
repetition, drill, memorization and actual experience. They need immediate
feedback.
NT / Intuitive- Thinking Learning Style In the intuitive-thinking learning
style (NT), students are skeptical, analytical and logical. They trust hard
evidence and reason. They prefer to work independently; they understand
things and ideas by breaking them down into their component parts. They
want to be challenged and allowed to be creative, and are concerned with
relevance and meaning. They have great patience and persistence if their
attention is captured.
SF / Sensing-Feeling Learning Style In the sensing-feeling learning style
(SF), students process information based on their personal experience. They
respond to collegiality, trust, respect and learning cooperatively. They
view content mastery as secondary to harmonious relationships. They are
very sensitive to approval or disapproval. They learn best by talking and
like group activities.
NF/Intuitive-Feeling Learning Style - In the intuitive-feeling learning
style (NF), students are looking for possibilities and patterns, and
connections with prior learning. They look for uniqueness, originality and
aestheticism. They learn best in a flexible and innovative atmosphere. They
have difficulty planning and organizing their time. They need to see the
big picture. They are bored by routine and rote assignments.
With these categories in mind information was collected on 207 Marygrove
students between the years 1995-1998. Of these 207 students 167 were
female. Although the age of the students was not documented it should be
noted that the average age of Marygrove's undergraduate student is 32.
These results are quite different from other studies. According to the
research of Hanson and Silver which does not specify age level:
Intuitive Feelers make up about 10% of all students.
Sensing Feelers make up about 35% of all students.
Intuitive Thinkers make up about 20% of all students.
Sensing Thinkers make up about 35% of all students,
The Marygrove research was compared to another study of college students
conducted by Mary Todd and Daniel Robinson at Bunker Hill Community College
in Boston, Massachusetts in 1995. Bunker Hill has approximately 6,000 day
and evening students. The MBTI preferences of 1007 students were collected
over a ten-year period (1985-1995). This research was reported at the
Center for Application of Psychological Type Conference held in Orlando,
Florida in March of 1998. The study reported scores by racial
identification.
The racial composition in the Mary grove study was the opposite of the
Bunker Hill study. Of 725 students in the Bunker Hill study 63% were
Caucasian and 20% were African American. In the Marygrove study, of the 204
students whose racial background was reported 87% were African American and
11% were Caucasian.
The most obvious difference in the Marygrove results is in the high percent
age of Intuitive Feelers (NF) students. Keirsey and Bates (1984) report
that only 12% of the general population are NFs. This is much closer to
what Hanson and Silver and Todd and Robinson (Bunker Hill) report.
Insight into why Marygrove may attract a higher percentage of NF students
may be in the characteristics of the college itself. Marygrove is a small
(approximately 1,000 undergraduate students) Catholic liberal arts college.
The literature boasts of small class size and a warm and personal
atmosphere. Fairhurst and Fairhurst (1999) describe NF students as
preferring small group discussions and one on one instruction. They want a
personalized learning setting. They seek harmony and demonstrate
sensitivity and caring for others. Personal values are very important to
them. If we place the stated characteristics of Mary grove College, as
described by the mission statement and college catalog, along side the
characteristics of the Intuitive Feeler learner the results may not be so
surprising.
Conclusions and Future Action
Adult students bring a consumer mentality to higher education. They will
seek out learning environments that offer them the best chance at success.
This study pro vides Marygrove faculty and support staff with a closer look
at the students who choose Marygrove as their ticket to the future. A great
percentage of these students are looking for a personal environment that
will allow them to unleash their unique creative potential. Affirming this
natural preference for learning provides an important variable that
contributes to success. Successful students are more likely to develop
abilities that might not have been tapped. These students bring with them
many years of life experience in which they have developed habits and
attitudes toward learning. Some of these habits and attitudes must be
transformed if these students are to graduate and move on to a successful
future.
This study was undertaken as an action research project that does not seek
correlation beyond the population studied. However, faculty at other
institutions could easily conduct their own study to ascertain the profile
of their student body. The value is in determining if there is a dominant
student profile at the institution. If there is, faculty and support staff
have a better opportunity to begin working with the students" most natural
style. Research has suggested that knowing one's preferred learning style
enhances a student's ability to achieve academic success. The knowledge
that there are different styles for achieving .success is in itself an eye
opener for many students.
Some studies have indicated that academically successful students have
fewer strong learning style preferences than do low achievers. The
challenge is to assist students in perfecting their natural learning style
while providing the incentive to develop less dominant styles they will
need in the workforce and other areas of their lives. Engaging in the
process of learning how to learn must include awareness of how one
perceives and processes material to be learned. Instructors can enhance
students' awareness by calling their attention to the ways and means by
which they are approaching their subject. Varying teaching methods in each
component of the instructional cycle on a regular basis and then discussing
what each student finds most compelling and most challenging pro vides
opportunities to raise awareness.
Hanson and Silver offer the following suggestions for what they call
teaching around the wheel. Each aspect of instruction offers opportunities
to reach the variety of styles by changing teaching methods on a regular
basis.
Anticipatory Sets (Introductions that prepare for the lesson or a unit)
ST Give facts, details
NT Raise issues & potential problems
SF Relate to students' experiences, feelings & prior knowledge
NF Suggest new and original possibilities
Questions
ST Who, what, where, when
NT Explain, compare, identify cause and effect
SF Ask: What has been your experience? What do you know about ?
NF Ask: What might happen if or ask for an application
Tasks
ST Organize factual information, practice for recall
NT Create a problem solving mode where students must sort out data, analyze
and draw conclusions
SF Provide for group work or a task that involves the affect
NF Provide choices for completing assignments and projects or assign tasks
that involve imagination, innovation
Setting
ST Traditional rows or pairs; teacher at focus
NT Teams that will create a debating atmosphere; teacher moves from team to
team.
SF Groups or pairs for collaboration; teacher meets students at eye level
NF Learning centers, student arranged for interest; teacher is a resource
Feedback
ST Frequent, quick, short/need to know if they are right NT Infrequent but
with explanation of why they received the grade they did
SF Frequent, quick with an emphasis on the amount of effort that is
evidenced
NF Infrequent but with emphasis on its value' its uniqueness and creativity
Homework
ST Provide a model of what a complete and accurate assignment will look
like, practice and drill
NT Problem solving, analyzing work, it too must be modeled
SF Opportunities for articulating ideas, learning from others, develop
skills of collaboration designed to convince students they have knowledge
NF Projects or opportunities to create new or different ways of looking at
material, important to set criteria
Assessment
ST True and false, fill in the blanks, any measure that allows students to
recall factual material
NT Critical essays, debates, research projects which mea sure the ability
to see relationships
SF Interviews in and out of class. Let the students question you
NF Anything that can show what the student can do with what they have
learned
Helping students learn how to learn may be the most important lesson
faculty can teach students. Life-long learners, capable of learning and
working in diverse settings, are vital to the 21century society. Assisting
students in achievement of this goal puts a demand on faculty to take the
time to teach around the learning style wheel. The reward for this effort
will be more students who are engaged in at least some aspect of the
learning process. Going a step further and talking with students about how
they experience learning when instruction or tasks call on styles that are
not natural for them, raises awareness of their own approach to learning.
Students may believe that what comes natural to them is all that they can
do well and they are doomed to failure in all other areas. Unless we sup
port students to develop under developed aspects of their styles they are
unlikely to have lifelong success. An important task of learning how to
learn is to develop an awareness of oneself as a learner. Students need to
reflect on their experience of learning in order to take charge of the full
development of their abilities. The ultimate goal of higher education can
not be content learning alone. Content may become obsolete. The U. S.
Department of Labor has identified the ability of knowing how to learn as
the most fundamental skill for the next century (Camevale, 1988). Selfawareness and then self-monitoring are essentials for learning how to
learn. Faculty and support staffs who nurture this type of learning are
helping develop tomorrow's workers. The kind of workers who are needed for
the learning organizations that will fuel our global economy.
Results of Marygrove study
Introverts
68%
Feelers
66%
Intuitive Feelers (NF)
41%
Sensing Feelers (SF)
25%
Intuitive Thinkers (NT)
18%
Sensing Thinkers (ST)
16%
Bunker Hill results reported by racial group
Style
African American (20%)
Introvert (I)
Feeler (F)
Intuitive Feeler (NF)
Sensing Feeler (SF)
Intuitive Thinker (NT)
Sensing Thinker (ST)
63%
29%
07%
21%
12%
60%
Marygrove results reported by racial group
Style
African American (87%)
Introvert (I)
Feeler (F)
Intuitive Feeler (NF)
Sensing Feeler (SF)
Intuitive Thinker (NT)
69%
63%
40%
23%
19%
Caucasian (63%)
46%
50%
24%
26%
19%
31%
Caucasian (11%)
43%
78%
39%
39%
13%
Sensing Thinker (ST)
18%
09%
Comparison of Intuitive Feeler (NF) data
Keirsey & Bates
Hanson & Silver
Bunker Hill
12%
10%
African Amer
07%
Caucasian
24%
Marygrove
African Amer
40%
Caucasian
39%
Marygrove College Characteristics
Small, warm and friendly environment
Religiously oriented: Catholic and very ecumenical in approach
Committed to the liberal arts
Especially noted for the helping professions and the arts; committed to
valuing diversity
Strives to develop graduates who exemplify competence, commitment and
compassion
Intuitive-Feeler (NF) Learner
Learn best in a nurturing environment
Have a keen interest in other belief systems and enjoy discussing moral
dilemmas
Needs to explore creative potential and find ways to express her/his ideas
and beliefs and share this inspiration
Are inspired be sensitive, supportive, humanistic teachers who show them
they care about them as individuals
Looks for similarities among people and encourages cooperation and harmony
References
Brandt, R. (1998). Powerful learning. Alexandria, VA: ASCD.
Canter Educational Productions (1996). Learning styles and multiple
intelligences. Santa Monica, CA.
Carnevale, A., Gainer, L & Meltzer, A. (1988). Workplace basics: the skills
employers want. Washington, D.C.: U.S. Department of Labor.
Fairhurst, A. & Fairhurst ' L. (1995). Effective teaching and effective
learning: making the personality connection in your classroom. Palo Alto,
CA: Davis-Black Publishing.
Hanson, J.R. & Silver, H.F. (1995). Learning styles & strategies.
Princeton, NJ: Hanson Silver Strong & Associates.
Keirsey, D. & Bates, M. (1984). Please understand me: character and
temperament types. Del Mar, CA: Prometheus Nemesis.
Lawrence, G.D. (1979). People types and tiger stripes: a practical guide to
learning styles.. Gainsville, FL: Center for Application of Psychological
Type.
Todd, M & Robinson, D. (1998). "Students of Color at an Urban Community
College." Gainsville, FL: Center for Application of Psychological Type.
~~~~~~~~
By Mary Ellen McClanaghan, PH.D., Marygrove College 22461 Revere St. Clair
Shores, Michigan 48080
Adapted by PH.D.
------------------------------------------------------------------------------Copyright of Education is the property of Project Innovation and its
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However, users may print, download, or email articles for individual use.
Source: Education, Spring2000, Vol. 120 Issue 3, p479, 8p, 4 charts.
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Title: Students' Learning Styles in Two Classes.
Subject(s): LEARNING strategies; EDUCATION
Source: College Teaching, Fall99, Vol. 47 Issue 4, p130, 6p, 1 chart, 1
graph
Author(s): Diaz, David P.; Cartnal, Ryan B.
STUDENTS' LEARNING STYLES IN TWO CLASSES
Online Distance Learning and Equivalent On-Campus
The idea that people learn differently is venerable and probably had its
origin with the ancient Greeks (Wratcher et al. 1997). Educators have, for
many years, noticed that some students prefer certain methods of learning
more than others. These dispositions, re ferred to as learning styles, form
a student's unique learning preference and aid teachers in the planning of
small-group and individualized instruction (Kemp, Morrison and Ross 1998,
40). Grasha (1996) has defined learning styles as "personal qualities that
influence a student's ability to acquire information, to interact with
peers and the teacher, and otherwise to participate in learning
experiences" (41).
Blackmore (1996) suggested that one of the first things we teachers can do
to aid the learning process is simply to be aware that there are diverse
learning styles in the student population:
There are probably as many ways to "teach" as there are to learn. Perhaps
the most important thing is to be aware that people do not all see the
world in the same way. They may have very different preferences than you
for how, when, where and how often to learn. [online]
Although many of us are aware that different learning styles exist, the
application of this knowledge is often inconsequential. Some faculty simply
opt to use a wide variety of teaching activities, hoping that they will
cover most student learning preferences along the way. This method, though
expedient, may not be the most effective way to address student learning
preferences. Further, many teachers think that the same teaching methods
that work in their traditional classes will also work for distance
learning. The underlying assumption is that students who enroll in distance
education classes will have the same learning preferences as those in
traditional classes. Faculty often assume that teaching styles, and
accompanying classroom processes, are like a "master key" and thus
appropriate for any setting.
There is not an overabundance of re search on learning styles and distance
education. Most of the studies focus on the discovery of relationships
between learn ing styles and specific student achievement outcomes: drop
rate, completion rate, attitudes about learning, and predictors of high
risk.
One of the most popular learning style inventories, which is often used in
distance learning research, is the Kolb Learning Style Inventory (LSI)
(Kolb 1986). Kolb's LSI measures student learning style preference in two
bipolar dimensions. Over time, learners develop a preference for either
concrete experiences when learning or a preference for engaging in abstract
or conceptual analyses when acquiring skills and knowledge. They also may
emphasize interest in turning theory into practice by active
experimentation, or they may prefer to think about their experiences by
reflective observation (Dille and Mezack 1991, 27).
James and Gardner (1995) described Kolb's LSI as a cognitive learning style
mode. Cognitive processes include storage and retrieval of information in
the brain and represent the learner's ways of perceiving, thinking, problem
solving, and remembering (20).
Dille and Mezack (1991) used Kolb's LSI to identify predictors of high risk
among community college telecourse students. Successful students had lower
scores on their preferences for concrete experiences than did the
unsuccessful students. Thus, because distance learning courses often lead
to social isolation and require greater reliance on independent learning
skills, students with less need for concrete experience in learning may be
expected to be better suited to the distance format. People with higher
scores on concrete experience tend to exhibit a greater sensitivity to
feelings and thus would be expected to require more interactions with peers
and the teacher.
Successful telecourse students also preferred to look for abstract concepts
to help explain the concrete experiences associated with their learning.
That is, they wanted to know "why" certain things happened in conceptual or
theoretical terms. This more abstract approach clearly favored success in
the telecourse. Dille and Mezack concluded that students who needed
concrete experience and were not able to think abstractly were more highrisk in a telecourse.
Gee (1990) studied the impact of learning style variables in a live
teleconference distance education class. The study ex amined the influence
of learning style preferences of students in an on-campus or remote
classroom on their achievement in the following: course content, course
completion rates, and attitudes about learning. Both distance and on-campus
groups were taught simultaneously by the same teacher, received identical
course content, and met weekly. Gee administered the Canfield Learning
Styles Inventory (CLSI) (Canfield 1980).
Students in the distance learning class who possessed a more independent
and conceptual learning style had the highest average scores in all of the
student achievement areas. People with the lowest scores in the distance
learning course had a more social and conceptual learning style. Students
with both a social and applied learning style performed much better in the
on-campus class. The outcomes of the Gee study suggested that successful
distance education students favored an independent learning environment,
and successful on-campus students preferred working with others. The
relatively small sample of twenty-six students suggested that additional
research is needed.
An important question, however, is raised by such research: Are there
differences in learning styles between students who enroll in a distance
education class and their on-campus counterparts? That question, no matter
how it is answered, is vital for anyone interested in students' success. If
there are no differences in learning styles, it is likely that faculty can
transfer the same types of teaching/learning activities that have worked in
the traditional environment into the distance setting with similar success.
That is probably true, if enough sensitivity and thought have been given to
learning styles and to how these methods will be transferred to the
distance education en vironment using current communications technologies.
On the other hand, if there are differences in learning styles between
groups of students, then faculty must use learning style information to aid
their planning and preparation for distance education ac tivities. Sarasin
(1998) noted that professors should be willing to change their teaching
strategies and techniques based on an appreciation of the variety of
student learning styles. "[Teachers] should try to ensure that their
methods, materials, and resources fit the ways in which their students
learn and maximize the learning potential of each student" (2).
If optimal learning is dependent on learning styles, and these styles vary
be tween distance and equivalent on-campus students, then faculty should be
aware of these differences and alter their preparation and instructional
methods accordingly. In any case, the first step in using learning style
information in distance education is to determine students' learning
styles.
Selecting a Learning Style Instrument
As educators consider transplanting their traditional courses into distance
learning, they should assess the learning styles of the students who
enroll. With a variety of learning style instruments in use, it is
important to select one according to the unique requirements of the
distance learning context. Three important factors to consider when
selecting a learning style instrument are defining the intended use of the
data to be collected, matching the instrument to the intended use, and
finally, selecting the most appropriate instrument (James and Gardner
1995). Other concerns include the underlying concepts and design of the
instrument, validity and reliability issues, administration difficulties,
and cost (22).
One of the distinguishing features of most distance education classes is
the ab sence of face-to-face social interaction between students and
teacher. Thus, an inventory used in that setting should address the impact
of different social dynamics on the learning preferences of the students.
An example of this can be seen in Gee (1990), who employed the Canfield
Learning Styles Inventory (CLSI). The CLSI demonstrated merit in distance
learning studies because it at tempted to measure students' preferences in
environmental conditions, such as the need for affiliation with other
students and instructor, and for independence or structure.
Those varied social dynamics are one of the main differences between
distance learning and equivalent on-campus environments. However, in our
opinion, both the Canfield Inventory and Kolb's LSI create a narrow range
of applicability for learning styles by limiting learning preferences to
one or two dimensions. Al though this learning style "stereotyping" may be
convenient for statistical analysis, it is less helpful in terms of
teaching students about weaker or unused learning preferences. Further, the
Kolb LSI, which has been widely used, is primarily a cognitive learning
preference instrument, which does not specifically take into a ccount
social preferences that are the key distinction between distance and
traditional classrooms.
Of the different learning style instruments, the Grasha-Reichmann Student
Learning Style Scales (GRSLSS) seem ideal for assessing student learning
preferences in a college-level distance learning setting. The GRSLSS
(Hruska-Riechmann and Grasha 1982; Grasha 1996) was chosen as the tool for
determining student learning styles in the present study based on criteria
suggested by James and Gardner (1995). First, the GRSLSS is one of the few
instruments de signed specifically to be used with senior high school and
college students (Hruska-Riechmann and Grasha, 1982). Second, the GRSLSS
focuses on how students interact with the instructor, other students, and
with learning in general. Thus, the scales address one of the key
distinguishing features of a distance class, the relative absence of social
interaction between instructor and student and among students. Third, the
GRSLSS promotes an optimal teaching/ learning environment by helping
faculty design courses and develop sensitivity to students' needs.
Finally, the GRSLSS promotes understanding of learning styles in a broad
context, spanning six categories. Students possess all six learning styles,
to a greater or lesser extent. This type of understanding prevents
simplistic views of learning styles and provides a rationale for teachers
to encourage students to pursue personal growth and development in their
underused learning styles.
Only a brief definition of each is provided here in order to assist the
reader with the interpretation of the information from this study.
1. Independent students prefer independent study and self-paced instruction
and would prefer to work alone rather than with other students on course
projects.
2. Dependent learners look to the teacher and to peers as a source of
structure and guidance and prefer an authority figure to tell them what to
do.
3. Competitive students learn in order to perform better than their peers
and to receive recognition for their academic ac complishments.
4. Collaborative learners acquire in formation by sharing and cooperating
with teacher and peers. They prefer lectures with small-group discussions
and group projects.
5. Avoidant learners are not enthusiastic about attending class or
acquiring class content. They are typically uninterested and are sometimes
overwhelmed by class activities.
6. Participant learners are interested in class activities and discussion
and are eager to do as much class work as possible. They are keenly aware
of, and have a desire to meet, the teacher's expectations.
The styles described by the GRSLSS refer to a blend of characteristics that
apply to all students (Grasha 1996, 127). Each person possesses some of
each of the learning styles. Ideally, one would have a balance of all the
learning styles; however, most people gravitate toward one or two styles.
Learning preferences are likely to change as one matures and encounters new
educational experiences. Dowdall (1991) and Grasha (1996) also have
suggested that particular teaching styles might encourage students to adopt
certain learning styles.
Problem and Purpose
Students' performance may be related to their learning preferences or
styles. Students may also self-select into or away from distance learning
classes. As a result, success in distance learning classes may ultimately
depend on understanding the learning styles of the students who enroll.
Because more online courses will in variably be offered in the future, some
as surance must be provided to the college, the faculty, and the students,
that distance education will meet expectations for a good education. Not
only will students expect an education that is equal in quality to that
provided by traditional offerings, they will expect a student-centered
learning environment, designed to meet their individual needs.
There have been few studies on the relationship of learning styles to
student success in a distance learning environment, and none that we are
aware of have used the GRSLSS. The purpose of this study was to compare the
student learning styles of online and equivalent on-campus, health
education classes, by using the GRSLSS.
The population for the current study in cluded health education students in
a medium-sized (8,000--9,000 enrollment) community college on the central
coast of California. The distance education sample included students in two
sections of health education offered in an online format (N = 68). The
comparison class was selected from four equivalent on-campus sections of
health education (N = 40) taught by the lead author.
The online distance students were taught according to the same course
outline, used the same textbook, covered the same lecture material, and
took the same tests as the on-campus students. Three main differences
between on-campus and online groups were the delivery mode for the
lectures, the mode of teacher/student and student/student communication,
and the mode for the assignments.
The distance classes reviewed multimedia slides (Power Point presentations
converted to HTML) and lecture notes online, while the equivalent classes
heard the teacher's lectures and participated in face-to-face discussion.
The distance class made heavy use of a class Web site and used a listserv
and e-mail for communication/discussion with other students and the
instructor. Assignments for the distance class were almost entirely
Internet-based and independent, while the equivalent class completed some
online assignments but participated most frequently in classroom
discussions and other traditional assignments.
All 108 participants first reviewed the student cover letter that explained
the na ture of the research and provided opportunity for informed consent.
Next, the authors distributed the GRSLSS and re viewed the instructions for
completion of the inventory. The GRSLSS was administered in a group setting
during the second week of classes. Thus, we used the General Class Form to
assess the initial learning styles of the students. Students self-scored
the inventory, and we obtained raw scores for each of the learning style
categories. Inventories were reviewed by the researchers for compliance
with di rections and for accuracy of scoring.
Research Outcomes
The present study compared social learning styles between distance
education and equivalent on-campus classes using the GRSLSS. The average or
mean scores of the distance learning class and the equivalent health
education class on each of the six categories are shown in figure 1.
Relatively larger differences in the average scores of the two classrooms
oc curred for the independent and the de pendent learning styles. Compared
with those students enrolled in the traditional classroom, the students in
the distance class had higher scores on the independent learning style
scale and lower scores on the dependent scale. A statistical test (a t
test) was used to determine if the differences in the scores between the
independent and dependent learning styles were due to chance.
The variations in average scores between the two styles were found to be
statistically significant and thus not likely due to chance (p < .01). The
variations in average scores between the two classrooms on the avoidant,
competitive, collaborative, and participant learning styles were relatively
small, and a statistical analysis using a t test revealed that they were
not statistically significant.
To ascertain the patterns in the relationships among the learning styles
within each class, we examined the associations among different
combinations of styles. This was done by calculating the correlation
coefficients associated with the combinations of the six learning styles.
The outcomes of this analysis are shown in table 1 for the distance
learning and traditional classroom groups. For reading this table, we
remind the reader that a correlation coefficient varies from -1, 0, to +1,
and that the degree to which it deviates from zero in either direction
reflects the strength of the relationship between the two variables. The
asterisks with some of the values indicate that the size of the correlation
was statistically significant and thus not due to chance.
Correlational analysis within the on line group showed a negative
relationship between the independent learning style and the collaborative
and dependent styles. In other words, people who were more independent in
their learning styles also tended to be less collaborative and dependent. A
second important relationship (positive correlation) was found be tween the
collaborative learning style and the dependent and participant learning
styles. That is, students who were more collaborative in their learning
styles also were more dependent and participatory in their approach to
learning.
In the equivalent on-campus group, significant positive correlations were
found between the collaborative learning style and the competitive and
participant styles. That is, on-campus students who were collaborative also
tended to be competitive and participatory in the classroom. Finally, a
positive correlation be tween the competitive and participant styles of
learning also was observed. Students who tended to compete also were "good
classroom citizens" and were more willing to do what the teacher wanted
them to do.
Discussion
Gibson (1998) has challenged distance education instructors to "know the
learner" (140). She noted that distance learners are a heterogeneous group
and that in structors should design learning activities to capitalize on
this diversity (141). Be cause the dy namic nature of the distance
population pre cludes a "typical" student profile (Thomp son 1998, 9), we
should continually assess students' characteristics.
A professor using the present data could plan learning opportunities that
would emphasize the learning preferences with each of the commonly
preferred learning styles (independent, de pendent, collaborative, and
participant), thus matching teaching strategies with learning styles.
Of particular interest were the significant differences between the groups
in the independent and dependent categories. The distance students more
strongly favored independent learning styles. It is not surprising that
students who prefer independent, self-paced in struction would self-select
into an online class. It may be that they are well suited to the relative
isolation of the distance learning environment. In his research, Gee (1990)
noted that successful telecourse students fa vored an independent learning
style. James and Gardner (1995) suggested that students who favored
reliance on independent learning skills would be more suited to a distance
format.
As a result of these significant differences, teaching strategies in the
distance class should emphasize relatively more independent and fewer
dependent learning opportunities. This approach has practical significance
given that professors often complain of too little class time to devote to
learning objectives. Armed with learning style data, we can more
efficiently allocate instructional time to various learning types.
Not only were online students more independent than the on-campus students,
but their independent learning preferences were displayed in a way that was
negatively related to how dependent and collaborative they were. That is,
the independence of online learners was not tied to needs for external
structure and guidance from their teacher (dependence) or a need to
collaborate with their classmates. The online students can be described as
"strongly independent," in that they match the stereotype of the
independent learner in terms of autonomy and the ability to be selfdirected.
Self-direction and independence were facilitated in the online course by
offering students flexible options to shape their learning environment. The
lead author, Diaz, used self-paced, independent learning activities that
allowed students to choose from a menu of online "cyber as signments" based
on their personal interests and the relevance of the assignments. Students
completed their chosen assignments by deadlines posted at the class Web
site.
In contrast, students in the equivalent on-campus class were significantly
more dependent learners than the distance group. Because dependent learners
prefer structure and guidance, it is not difficult to understand why they
might view the isolation and need for self-reliance in a distance education
environment with some apprehension. The low level of in dependence
displayed by on-campus students was not related to any other aspects of
their styles as learners. Thus, independence was clearly a weaker learning
preference for traditional class students.
The online students also displayed collaborative qualities related to their
need for structure (dependence) and their willingness to participate as
good class citizens (participant dimension). Thus, although online students
prefer independent learning situations, they are willing and able to
participate in collaborative work if they have structure from the teacher
to initiate it. In his online class, Diaz has used listservs and "threaded
discussion" areas to promote collaboration among distance students.
In the past, he designed collaborative activities that required students to
initiate peer contact and conduct the collaboration with a minimum of
structure and support from him. Based on the findings of the current study,
it is apparent why this strategy failed: Online students will apparently
respond well to collaborative activities, but only if the teacher provides
enough structure and guidance. Diaz's mistake was that he assumed that
online students would be self-directed, and autonomous, regardless of the
type of learning activity.
In contrast, the traditional class students had collaborative tendencies
related to their needs to be competitive, and good class citizens. In other
words, they were interested in collaboration to the extent that it helped
them to compete favorably in the class and to meet the expectations of
their teachers. Thus, collaboration was tied to obtaining the rewards of
the class, not to an inherent interest in collaboration.
Average avoidant and competitive learning style scores indicated that these
learning preferences were favored to a lesser degree by both groups. It was
interesting that, though we live in a highly competitive society, neither
the online or equivalent on-campus students really preferred a competitive
learning environment. However, the on-campus students ap peared to favor
competitiveness if it was clear that it was expected (i.e., thus the
relationship of competitive and participant styles).
We can also use learning style data to help design "creative mismatches" in
which students can experience their less-dominant learning style
characteristics in a less-threatening environment (Grasha 1996, 172).
Designing collaborative as signments for independent learners, or
independent assignments for dependent or collaborative learners, is
appropriate and even necessary. Strengthening less-preferred learning
styles helps students to expand the scope of their learning, be come more
versatile learners, and adapt to the requisites of the real world (Sarasin
1998, 38).
Learning styles were not the only differences between the distance and
comparison groups in this study. Demographic data indicated that the
distance group had a higher percentage of females (59 percent, 49 percent),
students currently enrolled in under 12 units (66 percent, 50 percent),
students who had completed 60 or more college units (12 percent, 1
percent), students who had completed a degree (12 percent, 7 percent), and
students above 26 years of age (36 percent, 6 percent). These
characteristics agree with the general profile of distance students as
reported by Thompson (1998). Although it is tempting to identify and depend
on a "typical" distance student profile, it is likely that the dynamic
nature of distance education in general will keep student characteristics
fluid. Thus, distance education instructors should continually monitor
students' characteristics.
Conclusions
We have concluded that local health education students enrolled in an
online class are likely to have different learning styles than equivalent
on-campus students. We found that online students were more independent,
and on-campus students were more dependent, in their styles as learners.
The on-campus students seemed to match the profile of traditional students
who are willing to work in class provided they can obtain rewards for
working with others and for meeting teacher expectations. Online students
ap peared to be driven more by intrinsic mo tives and clearly not by the
reward structure of the class.
One of the limitations of this study was the use of a non-probability
(convenience) sampling technique. Non-probability sampling is used when it
is impossible or impractical to use random sampling techniques. That is the
case in a large portion of educational research. Although still valid, the
results should not be overgeneralized. We have demonstrated a real and
substantial difference in learning styles between distance and equivalent
on-campus health education students at our college.
Before faculty rush to find out the effects of learning styles on student
outcomes, they should first address the issue of whether learning style
differences exist at all. The results of this study should send an
important notice to faculty who are teaching their traditional courses in a
distance mode, that there may be drastic differences in learning styles, as
well as other characteristic differences, between distance and traditional
students.
As the World Wide Web becomes an important medium for education delivery,
more and more courses will be offered in an online format. Though faculty
may attempt to use the same teaching methods in a distance environment that
they would employ in an on-campus class, the data from the current study
suggest that faculty will encounter significantly different learning
preferences as well as other different student characteristics. Professors
may want to employ learning style inventories, as well as collect relevant
demographic data, to better prepare for distance classes and to adapt their
teaching methods to the preferences of the learners.
Faculty should use social learning style inventories and resulting data for
help in class preparation, designing class delivery methods, choosing
educational technologies, and developing sensitivity to differing student
learning preferences within the distance education environment. Future
field-based research should replicate the current study in different in
stitutions and disciplines.
ACKNOWLEDGMENT
The authors would like to express their thanks to Tony Grasha, whose
encouragement, guidance, and ediorial comments were in strumental in
bringing this article to fruition.
Table 1.--Intercorrelations between Learning Style Scales for Online and
Equivalent On-Campus Students
Legend for Chart:
A
B
C
D
E
F
G
-
Scale
1
2
3
4
5
6
A
B
C
D
E
F
G
Online students (N = 68)
1. Independent
--
-.08
2. Avoidant
--
3. Collaborative
-.36(**)
-.37(**)
.07
-.12
-.03
.12
-.02
-.58(**)
--
.37(**)
-.04
.28(*)
4. Dependent
--
.08
.24
5. Competitive
--.12
6. Participant
-Equivalent on-campus students (N = 40)
1. Independent
2. Avoidant
3. Collaborative
4. Dependent
5. Competitive
--
-.20
--
.10
-.37(*)
--
-.12
.13
.09
-.12
-.01
-.67(**)
.27
.51(**)
.52(**)
--
.15
.31
-.46(**)
6. Participant
--
Note: (*) p < .05, two-tailed. (**) p < .01, two-tailed.
Figure 1. Comparison of Average Group Ratings for Each Learning Style
Legend for Chart:
B - Control group
C - Distance group
A
Independent
Avoidant
Collaborative
Dependent
Competitive
Participant
B
3.25
2.49
3.80
3.84(*)
2.46
3.79
C
3.56(*)
2.57
3.58
3.55
2.38
3.77
(*) Significant at .01 level
Grasha-Riechmann Learning Styles
REFERENCES
Blackmore, J. 1996. Pedagogy: Learning styles. Retrieved September 10, 1997
from the World Wide Web: http://granite.cyg. net/~jblackmo/diglib/styla.html
Canfield, A. 1980. Learning styles inventory manual. Ann Arbor, Mich.:
Humanics Media.
Dille, B., and M. Mezack. 1991. Identifying predictors of high risk among
community college telecourse students. The American Journal of Distance
Education, 5(1), 24-35.
Dowdall, R. J. 1991. Learning style and the distant learner. Consortium
project extending the concept and practice of classroom based research
report. (ERIC Document Reproduction Service No. ED 348 117)
Gee, D. G. 1990. The impact of students' preferred learning style variables
in a distance education course: A case study. Portales: Eastern New Mexico
University. (ERIC Document Reproduction Service No. ED 358 836)
Gibson, C. C. 1998. The distance learners academic self-concept. In
Distance learners in higher education: Institutional re sponses for quality
outcomes, ed. C. Gibson, 65-76. Madison, Wisc.: Atwood.
Grasha, A. F. 1996. Teaching with style. Pittsburgh, Pa.: Alliance.
Hruska-Riechmann, S., and A. F. Grasha. 1982. The Grasha-Riechmann student
learning style scales. In Student learning styles and brain behavior ed. J.
Keefe 81-86. Reston, Va.: National Association of Secondary School
Principals.
James, W. B. and D. L.Gardner. 1995. Learning styles: Implications for
distance learning. (ERIC Document Reproduction Service No. EJ 514 356)
Kemp, J. E., G. R. Morrison, and S. M. Ross. 1998. Designing effective
instruction (2nd ed.). Upper Saddle River, N.J.: Prentice-Hall.
Kolb, D. A. 1986. Learning style inventory: Technical manual (Rev. ed.).
Boston, Mass.: McBer.
Sarasin, L. C. 1998. Learning style perspectives: Impact in the classroom.
Madison, Wisc.: Atwood.
Thompson, M. M. 1998. Distance learners in higher education. In Distance
learners in higher education: Institutional responses for quality outcomes,
ed. C. Gibson, 9-24. Madison, Wisc.: Atwood.
Wratcher, M. A., E. E. Morrison, V. L. Riley, and L. S. Scheirton. 1997.
Curriculum and program planning: A study guide for the core seminar. Fort
Lauderdale, Fla.: Nova Southeastern University. Programs for higher
education.
~~~~~~~~
By David P. Diaz and Ryan B. Cartnal
David P. Diaz is a professor of health education, and Ryan B. Cartnal is a
re search analyst at Cuesta College, San Luis Obispo, California.
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Title: Can computer-aided instruction accommodate all learners equally?
Subject(s): LEARNING strategies; HUMAN-computer interaction; EDUCATIONAL
technology
Source: British Journal of Educational Technology, Jan99, Vol. 30 Issue 1,
p5, 20p, 1 graph
Author(s): Ross, Jonathan; Schulz, Robert
CAN COMPUTER-AIDED INSTRUCTION ACCOMMODATE ALL LEARNERS EQUALLY?
Abstract
This exploratory study investigated the impact of learning styles on humancomputer interaction. Seventy learners who were enrolled in a large urban
post-secondary institution participated in the study. The Gregorc Style
Delineator Trademark was used to obtain subjects' dominant learning style
scores. Results indicated that patterns of learning indices did not differ
significantly based on subjects' dominant learning style. Five of the six
measures indicating human-computer interaction behavior were not
significant at the p < 0.05 level. However, learning styles significantly
affected learning outcomes, as indicated by a significant main effect, as
well as an interaction effect between dominant learning style and
achievement scores. It would appear that Abstract Random learners may be
at-risk for doing poorly with certain forms of computer-aided instruction.
Based on the review of literature and results found in this study, it was
concluded that computer-aided instruction may not be the most appropriate
method of learning for all students.
One of the most powerful features of computer-aided instruction (CAI) is
its capacity to individualize instruction to meet the specific needs of the
learner (Rasmussen and Davidson, 1996). Self-paced instruction, the ability
to present content in a variety of ways (eg, text, video, sound, graphics),
and features such as hypertext make CAI an effective learning medium.
The use of CAI in education has burgeoned in recent years (Price, 1991;
Nelson and Palumbo, 1992; Hawkridge, 1995). Faced with increasing class
sizes and heavier work loads, teachers are looking towards CAI as a means
of supplementing classroom instruction. In addition, CAI software continues
to improve in its ability to engage learners and provide realistic and
stimulating learning environments (Price, 1991). Learners can now choose
from a variety of educational software packages designed to augment the
curriculum (Dwyer, 1996).
As the use of CAI systems continues to grow, research in the area of humancomputer interaction is becoming increasingly important. Currently, a
select few studies examine individual differences and their effects on CAI
(Marquez and Lehman, 1992; Nelson and Palumbo, 1992; Reed, 1996). Findings
generally indicate that while CAI has tremendous potential to individualize
instruction, a number of learner characteristics such as motivation,
learning styles, and background knowledge may affect the quality and
effectiveness of a CAI instructional session.
This exploratory study examines the influences of cognitive learning styles
on both achievement levels and human-computer interaction behaviors.
Findings from this study indicate that certain forms of CAI may not
accommodate all learners equally (see Ross, 1997). Educators should,
therefore, remain cautious when using the computer as a learning tool. Just
as teachers need to use a variety of approaches to meet the diverse needs
of their students, so educators should be aware that CAI may not be the
learning medium of choice for all students.
Literature review
The Gregorc Style Delineator Trademark
According to Gregorc (1979): "Learning style consists of distinctive
behaviors which serve as indicators of how a person learns from and adapts
to his environment. It also gives clues as to how a person's mind operates"
(p. 234). Designed to assess learning styles, The Gregorc Style Delineator
Trademark is a self-scoring battery which focuses on two types of mediation
abilities in adult individuals: perception (the means through which one is
able to grasp information), and ordering (the means in which one arranges,
systematizes and disposes of information). The two dimensions of ordering
are referred to as sequential and random; the two qualities of perception
are known as abstractness and concreteness (Gregorc, 1982 a).
Abstractness allows the individual to comprehend that which is not visible
to the senses. Data can be mentally visualized, grasped, and conceived
through the faculty of reason. Individuals who are strong in concreteness
use the physical senses to comprehend and mentally register data.
Sequential individuals perceive and organize data in a linear, methodical
fashion, and can express themselves in a precise manner. Furthermore,
discrete pieces of information can be categorized naturally. In contrast,
randomness disposes the mind to organize information in a nonlinear and
multidimensional fashion. This quality enables individuals to deal with,
and process, multiple data simultaneously.
Gregorc combines these abilities to create four mediation channels of mind
styles: concrete sequential (CS), concrete random (CR), abstract sequential
(AS) and abstract random (AR). Gregorc believes that individuals have, to a
certain degree, characteristics of each category, but most individuals tend
to show a stronger orientation toward specific channels.
The inventory's scores are obtained by ranking four words at a time ('1'
indicating "least like me", '4' indicating "most like me"). Ten categories
of four words determine the scores for each of the four mind-styles. Each
word corresponds to a particular mediation channel, and when summed, give a
measure of a person's propensity for operating within specific learning
channels.
Gregorc (1982a) divides the scores received on The Style Delineator
Trademark into three levels:
1) Strong orientation towards qualities associated with the particular
channel (or pointy-headedness), indicated by a score of 27-40;
2) Moderate ability, indicated by a score range of 16-26 on any one
mediation channel: and
3) Minimal capacity (stubby pointedness), indicated by a score of 10-15 in
a specific channel. According to Gregorc (1985) approximately 60% of the
channel's characteristics are observed in people with a score of 27 or
over; hence, 27 has been selected as the cut-off point for "pointyheadedness". Another major cut-off point, 15, has been identified as an
indication of "stubby pointedness" because very few of the channel's
characteristics are observed in people with scores below 15 (Gregorc,
1982a).
Learner characteristics
(Unless otherwise stated, information presented in this section is cited
from Gregorc's book An Adult's Guide to Style, 1982b)
People who are dominant CS are usually practical, thorough, well-organized
and prefer quiet, structured environments. CS individuals tend to perceive
reality as the concrete world of the physical senses, and think in a
sequential and orderly fashion. The CS can detect the most minute details,
working with the exactitude of a machine (Gregorc, 1982a). The CS student
is a perfectionist and prefers being told what to do. These learners do not
like to go against the norm, view work as a job assignment, and enjoy being
physically involved and active in lessons.
AS people consider themselves as evaluative, analytical, and logical
individuals with a preference for mentally stimulating, orderly, and quiet
environments. The AS has an academic-type mind which is driven by a thirst
for knowledge. To an AS, "knowledge is power", and the ability to
synthesize and relate concepts enables the AS to transmit ideas (both
through the spoken and written word) intelligibly and eloquently. AS
learners thrive on teachers who are experts in their area of interest,
learning well through lecture-style teaching.
AR individuals are highly focused on the world of feeling and emotion, and
are sensitive, spontaneous, attuned, person-oriented people. Thought
processes of AR individuals tend to be nonlinear, multidimensional,
emotional, perceptive, and critical. AR people prefer active, free, and
colorful environments. ARs thrive on building relationships with others
and, as learners, dislike extremely structured assignments.
Finally, CR individuals process information in three-dimensional patterns
and think intuitively, instinctively, impulsively, and independently. CR
people prefer competitive, unrestricted, and stimulus-rich environments.
CRs can be risk-takers and can easily jump to conclusions, often correctly.
Such individuals are divergent thinkers, thriving in environments which
engender exploration. CR learners do not need many details to solve a
problem, instead operating according to personally constructed standards.
Overall, everyone has the capacity to learn within each of the above
channels; no one is a "pure type" (Gregorc, 1982b, 41). Therefore, The
Style Delineator Trademark is a tool which:
"provides an individual with a key to understand better the subtle and
potent qualities of the mind, (their) behavior, the behavior of others and
the demands placed upon individuals by their environment." (Gregorc, 1982b,
41)
Learning styles and CAI: An overview
CAI and learner profiles
A study conducted by Friend and Cole (1990) discovered that sensingthinking individuals (dimensions correlated with CS and AS) responded more
favorably to CAI than did intuitive-feeling types (dimensions which are
correlated with AR). Friend and Cole postulated that intuitive-feeling
types require more human interaction to achieve desired learning outcomes,
and that CAI may not be suitable for all learners.
Enochs et al. (1985) found that concrete learners (as determined by Kolb's
Learning Style Inventory) learned better from a CAI session than did
abstract learners. Pritchard (1982) gives further support for the claim
that CAI may not accommodate all learning styles equally. In his article on
educational computing, Pritchard explained that CAI is suited best for
individuals with an affinity for accuracy and attending to detail.
Moreover, the researcher claims individuals with certain learning styles
may be more partial to learning from computers than would others, and that
people who have a preference for CAI usually enjoy working alone (see also
Wood et al., 1996).
In keeping with CAI and learner profiles, Hoffman and Waters (1982) stated
that CAI is suited best for individuals who: "...have the ability to
quietly concentrate, are able to pay attention to details, have an affinity
for memorizing facts, and can stay with a single track until completion"
(p. 51).
Dunn and Dunn (1979) reported that certain students may only achieve
through selected instructional methods (eg, CAI, whole-group instruction,
etc.), and that matching can significantly improve academic achievement.
Dunn and Dunn asserted that students who are motivated, require specific
instructions, are sequential, and enjoy frequent feedback generally do well
with programmed learning such as CAI. However, students who are
kinesthetic, peer-oriented learners (ie, AR learners) may not be engaged
adequately by the same method of instruction.
The computer as a matching tool
Although the idea of matching instruction to students' learning styles has
been supported in the literature (eg, Butler, 1984; Hettiger, 1988), it can
be difficult for educators to match teaching and learning styles in the
traditional classroom. It has been argued that effective CAI can correct
for many teachers' inability to meet the needs of all learners (Schlechter,
1991). Yet, CAI may not be the preferred mode of learning for all students.
According to Gregorc (1985), sequential students (CS and AS) tend to prefer
CAI because the computer is seen as an extension of the sequential person's
mind. Random individuals (CR and AR) require environments which are
flexible and provide opportunities for multidimensional thinking (Butler,
1984). AR individuals, in particular, are inherently social and enjoy
learning with others (Butler, 1984). It is apparent that traditional CAI
does not always provide such an environment for this group of learners.
Unlike the teacher who may be able to troubleshoot and modify lessons to
meet the specific learning needs of the student, the computer is only as
good as the program that has been created for it; and, as Gregorc (1985)
wrote:
"Students who cannot adapt to the demands of the medium are 1) denied
access to the content and goals, and 2) are vulnerable to possible
psychological damage if they cannot free themselves of the medium ....
Children can therefore become victims of a medium which is offensive to
them. They are at the mercy of the machine." (p. 168)
Moreover, because a computer requires sequential thinking in order to gain
access to its content (Gregorc, 1985), many CR and AR individuals may
become flustered and agitated when problems arise with the medium. Gregorc
(1985) warns that problems such as "burnout" and other mental and physical
ailments can arise if individuals are made to accept certain media which
are seen as adversive.
Butler (1984) claimed that technology, in general, places demands on the
learner. The computer is often not inherently flexible, intuitive or
adaptive, and may therefore restrict the behaviors and responses of the
user. As a result, "learners can master such equipment only when they have
mastered its invisible demands" (p. 27). The author concluded that "an all
out movement towards computer-aided instruction is bound to leave many
students behind" (p. 29).
In an effort to ensure that all learners can benefit from computer
technology, Gregorc (1985) recommended that leaders (eg, teachers,
administrators, employers, professors) provide human mediators who can
correct for matching problems that may arise from using an inappropriate
and potentially invasive learning medium.
Further support for the notion of instructional matching was voiced by
Burger (1985). In her opinion, CAI may be overused to a certain degree:
"Requiring all students to use [Computer-Aided Instruction[ may not be in
the best interest of the student. The matching of the teaching style of the
specific computer program and the learning style of the student must be
considered." (p. 21)
Inasmuch as the computer can be a powerful learning medium, the machine is
limited in its capacity to modify instruction to meet individual needs
(Enochs et al., 1985; Gregorc, 1985). While there have been advances in the
area of intelligent tutoring and adaptive interfaces (see Steinberg and
Gitomer, 1992; Mills and Ragan, 1994), some of the software interfaces that
are currently available are unintuitive and unnecessarily complex (Mitta
and Packebusch, 1995). Wallace and Anderson (1993) explained "designing
good computer interfaces has proven a formidable challenge" (p. 259).
Hence, many students may be forced to adapt and harmonize with the computer
(ie, style flex) in order to attain desired learning goals.
"These inanimate objects lack empathy. Machines cannot sense the
opportunities, qualifications, fears or problems. Nor can they sense the
pressures from the forced intimacy we demand between learners and the
media. Without compassion, there are no adjustments or alternative
approaches offered. There is no sense of harm or restraint as the frozen
medium makes its learning demands for sympathetic resonance. School
personnel must recognize these facts when purchasing machines." (Gregorc,
1985; 168)
Butler (1984) elucidated the notion of mismatching learning styles and
media discussed by Gregorc (1985). "Instructional technology biases the way
information is presented, and demands, to varying degrees, that we use
certain mediation channels" (p. 237). In other words, the use of technology
may systematically discriminate against certain learners who are unable to
match learning styles with the medium. Just as the lecture approach in
education is best suited to AS learners (Gregorc, 1982b), so the computer
may be better suited to certain learning styles.
Method
Problem
Research suggests that CAI may have a limited ability to accommodate users
with varying learning styles (eg, Butler, 1984; Gregorc, 1985; Hettiger,
1988; Cordell, 1991). Based on the limited number of studies examining the
learning styles and CAI, it would appear that sequential students fare
better with most CAI applications than do random students.
Yet, in any given classroom, one half of students have a propensity for
learning best in the random mediation channel (O'Brien, 1994). When coupled
with the fact that the use of hypermedia information systems with little or
no teacher guidance is increasing in education (Small and Grabowski, 1992),
the need for continuing research in the area becomes apparent.
Specifically, further research in the area of learning styles and human-
computer interaction is needed in order to understand better the influences
of individual differences and CAI.
Research questions
Since this appears to be the first study to investigate the Gregorc
mediation channels and their impact on learning from, and interacting with,
a CAI program, no hypotheses have been made. I, instead, explored the
following research questions:
1. Will learning outcomes differ significantly based on student cognitive
learning styles as measured by The Gregorc Style Delineator Trademark?
2. Will human-computer interaction behaviors (ie, time spent on the
program, navigation, events recorded, video, tools and lesson preference)
differ significantly based on student cognitive learning styles as measured
by The Gregorc Style Delineator Trademark?
3. Will differences in entry level domain knowledge affect learning
outcomes above and beyond that of learning style?
Subjects
Seventy University of Calgary undergraduate volunteers (26 males, 44
females) participated in the study. The following is a breakdown of
students by Faculty: Nursing = 18; Kinesiology = 20; Education = 13; Other
= 19.
Treatment
To investigate differences between participants, learning style groups
received the same treatment. For the purposes of this study, the onerescuer adult CPR procedure was used to collect data. Content for the CAI
program was vetted for accuracy and validated by a three member committee
comprised of experienced CPR Instructor Trainers.
The entire experimental sessions took two hours to complete for each group
of approximately 15 participants. One hour was devoted to assessing and
interpreting learning style scores. The second hour was dedicated to the
CAI session.
Following completion of the workshop, the researcher explained the
interface to the participants so that each learner would be familiar with
the features and options available to them during the CAI session.
Participants then completed the on-line questionnaire (comprised of six
demographical questions measuring participants' age, year of program,
gender, comfort level with CAI, and CPR confidence level), the 20 question
pre-test and the tutorial program.
No time restriction was imposed on the learners during the CAI session, as
time was a variable under investigation. It was imperative that learners
did not feel rushed to complete their learning in a stipulated time limit;
similarly, a time restriction may have forced quicker learners to stretch
out the CAI session to meet the time restriction. Participants worked
independently on the computer, using headphones to listen to audio
information.
CPR is a psychomotor skill requiring knowledge of theoretical principles,
procedural steps and performance principles and practices. The computer
program instructs and tests both theory and understanding of procedures
necessary to perform CPR, leaving motor performance instruction and
evaluation to a certified CPR instructor.
Both the pre-test and the post-test were comprised of ten knowledge-type
questions, five comprehension questions and five application questions (20
multiple choice questions in total). Questions covered one-rescuer
Cardiopulmonary Resuscitation (CPR) guidelines and procedures as stipulated
by the Heart and Stroke Foundation of Canada's Emergency Cardiac Care
Committee.
Construct validity for the test items was determined by a three member CPR
instructor trainer review committee. The test-retest reliability alpha
coefficient for the examination was determined to be 0.86 for the pre-test
and 0.89 for the post-test.
Instrument
The Gregorc Style Delineator Trademark is a widely-used measure of
assessing cognitive learning styles (O'Brien, 1994). The assessment tool
was selected, in part, for the following reasons:
• Easy to administer
• Easy to interpret
• Self-scoring battery
• Relatively quick to administer and complete
• Inexpensive
• Discrete, easily reportable scales
• The only inventory available with a technical manual for administrators
• Validity and reliability measures have been supported by research (eg,
Gregorc, 1982a)
Joniak and Isaksen (1988) examined the internal consistency of The Style
Delineator Trademark. The data revealed alpha coefficients raging from 0.23
to 0.66, below that which was reported by Gregorc (1982 a). O'Brien (1990)
found similar results. Using a sample size of 263 undergraduate students,
O'Brien reported alpha coefficients ranging from O. 51 for the AS scale to
0.64 for the CS scale, but concluded that internal consistency scales meet
minimal requirements for factor definition (O'Brien, 1990).
Gregorc (1982a) reported test-retest alpha coefficients of 0.85 to 0.88. In
addition, Gregorc (1982a) published internal consistency reliability
coefficients ranging from 0.89 for the AS scale to 0.93 for the AR scale,
and predictive validity correlations ranging from 0.55 to 0.76 (all figures
significant at the p < 0.001 level). Results were based on a sample size of
110 participants.
Quality of material
With any CAI program, quality of material presented is always an issue.
According to Rushby (1997), three factors are essential to ensuring that a
CAI program meets acceptable standards: content is accurate and up to date,
the program is rigorously tested to ensure minimal running errors, and the
program is free from typographical errors.
The program used for the study meets all three quality standards. A CPR
committee verified the accuracy of content ensuring that it was up to date
and a reflection of current practices in CPR. The program went through a
lengthy six month beta testing stage at which time all errors were found by
a focus group and addressed by the programmer. To this date, we have had no
error reports from any Nursing schools who have purchased the program. In
terms of typographical accuracy, an editor with 10 years in the area was
used to verify the textual consistency and grammatical structure of the
program lessons and narration tracks.
Independent measures
Learning styles--Subjects' highest learning style scores (as determined by
The Style Delineator Trademark) were treated as a measure of dominant
learning style. The following is a breakdown of subjects by dominant
learning style score: CS = 20; CR = 20; AS = 14; AR = 16.
Domain knowledge level--Pre-test scores were used as a measure of domain
knowledge levels and for learning outcome achievement analysis.
Dependent measures
A program audit trail file was created for the purposes of this study to
track participants' patterns of learning. Together with the pre-test score,
learning style scores, and the preliminary survey information, the audit
trail file also stored detailed information (eg, which tools and video
options were accessed on which screens, and continuous time reports). The
term "patterns of learning", referred to in a study by Liu and Reed (1994),
are used in this study to describe human-computer interaction indices.
These indicators are listed below:
Total time (in minutes) to complete the tutorial--Participants were given
no time restriction to move through the tutorial program; hence, time
scores varied from subject to subject.
Navigation trend--Participants' patterns of movement through the tutorial
were determined by a numerical score. The tutorial consisted of 15
instructional screens detailing the discrete steps in performing CPR. The
audit trail recorded navigation by assigning a '+1' value when the next
screen button was selected, and a '-1' value when the previous screen
button was selected. For example, if a subject were to move through the
procedures in a linear fashion, a score of 14 would be assigned (14 'next
screen' selections x 1). If a subject were to go back three screens while
covering all 14 steps, a net score of + 11 would be assigned by the audit
trail file ({14 'next screen' selections x 1} + {3 'back screen' selections
x -1}).
Total number of tools used--The frequency with which the subject accessed
the program tools (note pad, search tool, index tool and glossary) was
reflected by this measure.
Total number of video events--The number of times the user accessed video
controls 'Play', 'Pause', 'Rewind', and 'Volume' was indicated by this
total. It is important to note that the video, by default, played
automatically upon moving to a new screen; hence, the score reflected in
this category indicated the number of video events above and beyond the
standard score of 15 (or 15 video play options).
User preference for instructional sequence--The tutorial program was
comprised both of a 15 step tutorial sequence and a video review section.
The video review section summarized all video steps covered in the
tutorial. Learners could choose to watch the review video prior to, or
following, the tutorial. A code of '1' was assigned to those participants
who chose the 'Review Video' option first; an indicator of '2' was assigned
for learners who chose to move through the tutorial first.
Total number of events--This measure indicated the level of user
interaction. This number was derived by adding the total number of tools
used, videos accessed, and navigational events. A low number reflects user
passivity.
Post-test results--Learners completed a 20-question multiple-choice posttest. The results from the post-test were used as a dependent variable for
the purposes of achievement analysis.
Results
The alpha level representing statistical significance was set at the p <
0.05 level. Results that have lower or higher p values will be reported as
such. Data were analysed using SPSS 6 and BMDP IV.
Learning outcomes
To explore whether learning outcomes were influenced by dominant learning
style groups, a two-way ANOVA (2 x 4 factorial analysis) was conducted. The
data revealed a significant main effect for the pre-test and post-test
means over time (F[sub (1,66)] = 57.91, p < 0.001). There was also a
significant interaction between learning style and learning outcome (F[sub
(3.66)] = 20.11, p < 0.001). Figure 1 depicts the interaction between
dominant learning style and learning outcome.
The mean test scores reveal that three of the four dominant learning style
groups showed gains from the pre-test to the post-test. The AS group
increased an average of 3.64 points (or 18%), displaying the highest gain
of the three groups. CS and CR groups increased an average of about 2
points (or 10%). Interestingly, the AR group decreased from pre-test to
post-test by an average of just over 2 points (or 10%).
In summary, the results indicate that there were significant differences in
achievement between the four dominant learning style groups. Dominant
learning styles, it would appear, affected the magnitude and direction of
the differences in the pre-test and posttest results.
Human-computer interaction
To investigate the effects of dominant learning styles on human-computer
interaction, a MANOVA was conducted using six patterns of learning as the
dependent variables and dominant learning style as the independent
variable. Results indicated that there was not a significant effect for
patterns of learning by dominant learning style (1 = 0.6 6, F = 1.51, p =
0.09).
The data suggest that only one pattern of learning, navigation style,
differed significantly at the p < 0.01 level. Results of a post-hoe Scheffe
test indicated that the AS and CR group means were significantly different
from the AR group mean. It would appear that the AR group was the least
linear of the four dominant learning style groups, recording a mean score
of just over 10 points. Table 1 delineates the mean scores for the six
patterns of learning.
Results suggested that AR participants spent less time with the program,
used less video and made fewer interactions with the computer than did the
other three dominant learning style groups. In contrast, AS subjects tended
to spend more time with the program, used a higher number of tools and
interacted to a higher degree with the computer than did the other three
groups. Although not statistically significant, mean scores do suggest some
interesting differences between dominant learning style, to be explored in
future studies.
The overall lack of significant differences between dominant learning style
and patterns of learning measures of time, total events, tools, video and
lesson preference suggests that learning styles, as measured by The Gregorc
Style Delineator Trademark, did not significantly affect the way in which
learners interacted with the computer-aided instructional software.
Domain knowledge
Examination was also conducted to ascertain whether content knowledge
affected learning outcomes above and beyond that of learning styles.
Pre-test results indicated that there were disparities
domain knowledge participants possessed. An ANCOVA was
identify the influences of learning style on post-test
controlling for (or equalizing) differences in pretest
demonstrated by the four learning style groups.
in the entry-level
conducted to
scores, while
knowledge
The ANCOVA showed a significant effect for pre1test (b= 0.79; t = 8.4: sig
t = 0.001). However, learning styles still retained a significant influence
on post-test scores (F[sub 14, 65] = 19.58, p < 0.001). Furthermore, the
adjusted r2 value of O. 52 suggested that dominant learning styles alone
explained 52% of the variance in post-test scores, after controlling for
the influences of pre-test scores.
Discussion
Learning outcomes
In terms of learning outcomes, the data suggests that, as a
participants showed an increase from pre-test to post-test,
significant at the p < 0.001 level. This would suggest that
program led to gains in Cardiopulmonary Resuscitation (CPR)
group,
statistically
the tutorial
knowledge.
Once subjects were distilled into their dominant learning style groups,
however, the data revealed a significant interaction effect between
learning style group and achievement levels. In short, learning styles
significantly affected both the magnitude and direction of achievement
levels. The AS group made a gain of close to four points, while the CS and
CR groups made modest gains of about two points from pre-test to post-test.
Interestingly, the AR group decreased an average of more than two points
from pre-test to post-test, a result which has significant implications for
CAI if findings are supported by future studies. The question of why the AR
group decreased from pretest to post-test will be discussed further.
Theoretical explanations for achievement differences
According to Gregorc (1982b), individual learning styles influence
preference for method of instruction. Butler (1984) and Gregorc (1985)
believe that dominance in CS and AS mediation channels predisposes the
individual to having a preference for working with computers (be it in the
capacity as a computer programmer, or as a learner using CAI software).
Randoms are said to find working with computers frustrating (see Gregorc,
1985, 202,203). In this exploratory study, CR learners did well with the
computer program, but ARs did not. Although some of the content covered in
the tutorial program required linear processing, CR individuals did well
compared with AR learners.
Hence, one cannot argue successfully the point that the content, and not
the computer medium, was responsible for the differences in learning
outcomes. It would appear that possessing the abstract and random qualities
together made for a less successful computer-aided learning session. Butler
(1984) explained that AR individuals prefer human contact throughout the
learning process, and enjoy tasks requiring verbal, multidimensional
responses; certain forms of CAI, therefore, may be unsuitable for these
learners.
Results from the present study are consistent with results reported by
Davidson et al. (1992). The researchers found that AR individuals, enrolled
in a computer applications university course, showed significantly lower
achievement levels than did the other three Style Delineator Trademark
groups. AS individuals showed the highest gains in the course, indicating
their ability to work well with computer technology. The only significant
differences in methodology between the two studies are that Davidson et al.
utilized course assignments as a measure of success, whereas this study
used pre-tutorial and post-tutorial results as an indicator of achievement
levels.
Further exploration: differences in pre-test group scores
It is interesting to note that the pre-test means were different between
the four learning style groups. While the AS group had a mean pre-test
score of just 10, the AR group had a mean pre-test score of 15. Such a
sharp contrast may be explained by a number of factors.
a) Varied CPR background: It appears that the CPR course background varied
between the four learning style groups (CS = 2, CR = 1.4, AS = 1.5, and AR
= 2.8). An ANOVA was conducted to investigate whether the differences
between groups were significant. Results from the ANOVA indicate that
differences in CPR course backgrounds were statistically significant (F[sub
(3.66)] = 5.16, p < 0.01). A post-hoc Scheffe test was used to ascertain
which groups were significantly different. The AR group's mean CPR course
background score was deemed significantly different from the CR and AS
groups' score. Such contrastingly different group course backgrounds may
explain why the AR group had such a high pre-test score.
b) CPR confidence: The data suggest that the four dominant learning style
groups differed in CPR perceived confidence. As may be recalled, the
preliminary survey asked participants to rate their CPR level of confidence
(using a Likert-Type Scale; 1 being very confident, 5 being not confident
at all). Group mean scores (CS = 3.3, CR = 3.4, AS = 4.1, AR = 2.7) were
significantly different (F[sub (3.66)], = 2.71, p < 0.05), indicating
differences in CPR confidence by learning style groups. Scheffe post-hoc
analysis shows that the AS group mean was significantly different from the
three other groups.
The majority of CS and All group participants indicated that they were
pursuing Nursing degrees; hence, regular CPR certification is required, and
may explain the variation in learning style groups' scores. It is not
surprising, then, to see disparities in the mean pre-test scores across
groups. The AS group, the majority of whom were from Education or other
faculties, had taken the least number of CPR courses of the four groups,
and had the lowest confidence level in their skills. This group also
displayed the lowest pre-test score. Similarly, the All group recorded
taking the most number of CPR courses of the four groups, and reported the
highest CPR confidence levels.
The question of entry level differences in domain knowledge
It can be argued that there were obvious background disparities between the
four groups upon entering the study. While it is true that groups did
differ based on their pre-test scores, the ANCOVA showed that groups still
differed significantly on the posttest when controlling for pre-test
differences in knowledge. Hence, it would appear that achievement in the
CAI session was affected most significantly by cognitive learning styles.
Although three of the four dominant learning style groups learned from the
CAI lesson, AR learners consistently did not (two dominant AR subjects
increased scores from pre-test to post-test, nine decreased scores, and
four showed no change).
Patterns of learning
Patterns of learning, indicating human-computer interaction behaviors, were
not significantly different between dominant groups. Although five of six
patterns of learning were not significant at the p < 0.05 level, three
indices showed some interesting between-group differences.
The mean scores revealed in Table I show some interesting differences
between groups. It would appear that the All group spent, on average, less
time in the program, used less video, and recorded fewer events than did
the other three dominant learning style groups. The AS group showed
diametrically opposite behaviors, spending more time in the program,
interacting to a higher degree, and using more video than did the other
three groups.
Furthermore, the AR group recorded a significantly different mean
navigation value (significant at the p < 0.001 level) from the other
groups. AR participants, on average, recorded a mean value of around ten
points, indicating some degree of non-linear movement (either moving
backward to review previous screens, or using the index tool to jump from
step to step). The other learning style groups showed values which hovered
around the expected level of 14.
Patterns of learning as indicators of achievement
Upon closer inspection of the audit trail print-outs, it would appear that
many AR participants missed entire screens while traversing from step to
step. One participant missed five screens, jumping from step 3 to step 9,
moving through the remaining six steps, and then finishing back with step
8. It is not known if the subject knew the content covered by the missed
screens; however, it is clear that such an approach to learning CPR--a
procedure that requires linear movement through a pre-determined sequence
of steps--may interfere with current learning, and may very well interfere
with previous learning.
One knowledge-type test question, for example, asked participants to put
the steps of CPR in order. A correct response for this question required
the learner to have moved through the program in a linear fashion. Skipping
steps and moving to previous screens may have interfered with the learning
required for a correct answer to these types of questions.
When teaching CPR in the traditional classroom setting, it would be
detrimental for the instructor to move from step 1 to step 12, and then
back to step 2. Regardless of the type of learning style one has, certain
materials require sequential processing. Excessive and inappropriate use of
the index tool--a tool that allows the user to jump from any given step to
another--may have contributed to cognitive interference in many of the AR
subjects.
According to Milheim and Azbell (1988) cited in Small and Grabowski (1992),
systems that give the user control over the learning process are empowering
for some and destructive for others. Small and Grabowski warn that too much
user control can lead to navigation decisions resulting in either skipping
pertinent content or leaving the tutorial program before all content has
been thoroughly covered (also see Schroeder, 1994). Castelli et al. (1996)
discovered that many users of hypermedia "get lost" in hyperspace. The
notion of becoming disoriented due to incessant "jumping around" is
consistent with findings from Hammond (1989).
The overall lack of interaction recorded by AR subjects (based on low
events score, video use and time in program) may have resulted from a lack
of interest in the CAI session. Attitudes towards computers can be a
significant indicator of student achievement with the computer (Brudenell
and Stewart, 1990). A breakdown of the computer attitudes survey question
by dominant learning styles indicated that close to 60% of AS subjects
reported being comfortable with using the computer. In contrast, only 36%
of AR subjects felt comfortable with computer technology. Over 50% of CS
subjects and 55% of CR subjects felt comfortable with using the computer.
Hence, AR subjects were less likely to be comfortable with using the
computer than were the other learning style groups.
Motivation is also the key to any type of self-paced CAI session, according
to findings from Keller (1968). Keller, in his essay on computers in the
school, warned of the dangers of leaving important instructional decisions
to students. Students may neither have the metacognitive abilities nor the
motivation to select appropriate paths for achieving desired learning
goals. Small and Grabowski (1992) found that high motivation levels led to
subjects spending more time with the computer program, and subsequently
contributed to higher learning outcomes. Low motivation levels had an
inverse effect.
In direct contrast to the AR group, the data revealed that AS subjects were
highly engaged in the CAI lesson. Although not statistically significant,
all patterns of learning appeared to indicate that these subjects
interacted to a high degree with the program. Such enthusiasm and diligence
may have contributed to the higher achievement levels observed.
In terms of patterns of learning, Liu and Reed (1994) also found that,
overall, human-computer interaction measures were not significantly
affected by learning styles under investigation in their study. However,
field independence (a propensity for thinking analytically and logically)
was linked to using the index tool, and field dependence (thinking in a
more global way) was correlated with using more video. In addition, field
dependent subjects used the courseware significantly more than did field
independent participants. (It should be noted that comparisons cannot be
made between field dependence/independence and Gregorc's mediation
channels. There is no research to support relationships between these
dimensions of cognitive learning styles.)
Recommendations for the responsible use of technology in education
The following recommendations are meant to be used as guidelines for the
successful implementation of computer technology, and are based on findings
from this study. It remains essential for a clearly stated list of
recommendations, outlining proper computer use, to be published. In this
way, all individuals are afforded the right to learn in the way that suits
them best.
1. Educators should closely monitor--and mediate where necessary--all
computer instruction. Students should have clear and identifiable tasks to
complete, and learning outcomes should be measured periodically. This is
consistent with the views expressed by Greenberg and Pengelby (1989).
2. Students should be asked to express their views towards CAI through the
use of a teacher-constructed survey. Furthermore, if teachers have an
interest, they should ascertain the learning styles of their students, and
provide insight on how learning styles influence students preferences for
instruction. Learning style scores could be used in conjunction with
preference surveys to identify potential matching problems.
3. Opportunities for group work should be given to those students who are
hesitant to work on the computer alone. Research shows that AR students
enjoy working with others and sharing ideas during the learning process
(Ross, 1998). Since the focus shifts from being intimate with a machine to
working collaboratively with a group, the potentially negative effects of
CAL for these individuals may be masked and/or lessened (Ross, 1998).
4. Government Departments of Education should remain cautious with sweeping
decisions to convert entire curricula onto electronic media (as was
mentioned in the article by Dwyer, 1996). The goals of such a process
should be weighed against the potential problems (eg, alienating certain
learners).
5. To avoid alienating a certain learning style group, educators should
continue to incorporate a number of different teaching strategies into
their lessons. If a particular student is unable to learn from the
computer, instructors should provide alternative ways for content to be
delivered.
Conclusion
CAI is rapidly becoming one of the most influential media of instruction in
educational environments. However, findings from this exploratory study
indicate that CAI, as an instructional methodology, may not be suitable for
all learners. While computer-aided instruction has tremendous potential to
provide teachers and industry with a powerful educational tool, educators
must be cognizant of inherent differences which exist between learners-differences such as cognitive learning styles. Results from this
exploratory study suggest that some learners (AR learners in particular)
may have difficulty adapting to certain forms of computer-mediated
learning.
Studies continue to support the need to critically evaluate this ubiquitous
tool which has permeated the classroom and homes more quickly than most
other technologies have in the past (see Schlechter, 1991). As more
research is conducted in the area of CAI, information regarding appropriate
and educationally sound uses for the CAI will become available.
It remains essential, then, that the computer continue to be used as a tool
for supplementing classroom instruction. Some learners may need greater
support and guidance from the teacher, while others may be able to learn
from the computer relatively independently. Thus, teachers should not
assume every student will automatically benefit from computers in the
classroom. There remains the need for interpersonal contact and guidance to
ensure that all students attain their learning potential.
Limitations of study and opportunities for further research
Results from this study have some significant implications for computeraided instruction, if supported by further research. If replicated, a
number of considerations should be followed to improve the generalizability
of the results.
1. This study used the traditional goals--tutor--test approach to gather
data from participants. A study should be conducted with a computer program
adhering to a different learning model (eg, discrimination learning,
simulation, intelligent tutoring system, etc.). If results prove to be
consistent with those of this study, then it can be more conclusively
argued that CAI may not sufficiently accommodate all learners equally.
2. It is not clear whether low motivation, as indicated by AR subjects'
patterns of learning, was due to the computer or to the content presented
in the program. Since the majority of AR subjects were in a Nursing
program--and are assumed to enjoy medical procedures and training--it is
questionable that the CPR content, in and of itself, led to disparities in
pre and post-test scores. However, further research may shed some light on
the question of subject matter versus computer instruction.
3. This study used content that was familiar to most, if not all, subjects
(as indicated by the relatively high pre-test mean). Inasmuch as it was
desirable to have subjects who had varying levels of domain knowledge for
the purposes of exploring one research question (namely, domain knowledge
as a measure of individual differences), further research should be
conducted using a subject area that is unfamiliar to all participants. In
this way, learning outcomes could be more accurately measured.
4. Subjects were expected to interact with the CAI tutorial program for a
relatively short period of time (about 30-45 minutes). Further research
should explore the effects of learning styles and other individual
differences on CAI using a one week to one month study time frame.
5. This study used a program requiring the learner to move though content
in a somewhat linear way. This study should be replicated using a program
with content that can be learned in a non-linear manner.
6. Further research should explore the impact of group learning on learners
who may be pre-disposed to encountering difficulty with the computer. AR
learners matched with other AR learners may be more successful when using
the computer to learn. Research should determine what type of cooperative
groupings work best for "imperiled" computer users.
The previous recommendations for future research have a common theme: there
remains a need for more research in the area of learning styles and humancomputer interaction. The literature suggests that there are definite
learning preferences which are consistent with learning style profiles. It
follows, then, that CAI may not be suitable for all learners.
Unfortunately, the relationship between learning styles and computermediated learning needs to be explored in greater detail before more
conclusive statements can be made.
Table 1: Mean pattern of learning scores by dominant learning style score
Legend for Chart:
A
B
C
D
E
-
Learning
Style
N
Mean
SD
A
B
C
D
E
CS
CR
AS
AR
20
20
14
16
28.95
26.85
30.64
23.31
7.06
9.27
13.37
9.10
CS
CR
AS
AR
20
20
14
16
11.95
14.05
14.42
10.06
3.56
3.87
4.14
4.74
CS
CR
AS
AR
20
20
14
16
53.70
60.60
60.71
44.06
20.86
32.27
28.00
15.08
CS
CR
AS
AR
20
20
14
16
1.35
1.35
1.64
1.37
0.49
0.49
0.50
0.50
CS
CR
AS
AR
20
20
14
16
5.10
5.80
7.00
4.00
5.92
7.43
8.18
3.06
Time
Navigation
Events
Lesson
preference
Tools
Video
CS
20
8.00
8.78
CR
20
16.65
21.64
AS
14
11.57
10.97
AR
16
7.81
6.97
GRAPH: Figure 1: Interaction between tutorial effect and dominant learning
style group
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~~~~~~~~
By Jonathan Ross and Robert Schulz
Jonathan L. Ross is a doctoral candidate in Educational Technology at the
University of Calgary. He is also a senior instructional designer with
Media Learning Systems in the Faculty of Education. His web site address is
http://www.ucalgary.ca/~jross. Robert Schulz is a Professor in the Faculty
of Management at the same university. Address for correspondence: Faculty
of Education, The University of Calgary, 2500 University Drive NW, Calgary,
Alberta, Canada T2N 1N4. Tel: + 1 403 220 6490; email:
jross@acs.ucalgary.ca
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Title: Intuition-analysis cognitive style and learning preferences of
business and management students.
Subject(s): LEARNING strategies; STUDENTS -- Attitudes; COGNITIVE styles;
INDUSTRIAL management
Source: Journal of Managerial Psychology, 1999, Vol. 14 Issue 1/2, p26,
13p, 4 charts, 1 diagram
Author(s): Sadler-Smith, Eugene
Abstract: Studies the cognitive style and learning preferences of business
and management education students. Methodology used on the study; Results
and discussion.
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INTUITION-ANALYSIS COGNITIVE STYLE AND LEARNING PREFERENCES OF BUSINESS AND
MANAGEMENT STUDENTS
A UK exploratory study
Keywords Human resource developmenl, Learning styles, Managemenl education,
Training, United Kingdom
Abstract The study is an attempt to provide empirical elaboration, in the
context of business and management education, for the "onion" and cognitive
control models of cognitive style. Using, a sample of 226 business and
management undergraduates the research explored the relationship between
cognitive style measured using the cognitive style, index and learning
preference. Using principal components analysis, three, categories of
learning preference were discerned (active, reflective and individual.
Correlational analysis and one way analysis of variance revealed
statistically significant relationships between preferences for reflective
and individual methods and cognitive style. The results provide some
support for the "onion" and cognitive control models; the implications for
business and management education, training and development are discussed.
Background
Introduction
Curry (1983), in her "onion" model, argued that learning style and
cognitive style constructs may be grouped into three main types or layers
resembling the skin of an onion. At the onion's core is the "central
personality" dimension, remote from external influences and stable over
time. Overlying this central core are:
(1) "cognitive personality style": a relatively permanent and stable
characteristic measured by instruments such as the embedded figures test
(Witkin, 1962);
(2) "information processing style"' a relatively stable set of responses to
acquiring and assimilating information in a given learning situation
(measured by means of instruments such as the learning styles Inventory
(Kolb, 1984));
(3) the outer layer of the onion represents the behavioural manifestations
of the interaction between these inner layers and the external environment
through the expression of, for example, preferences for particular types of
teaching and learning methods, such as self-direction, collaboration and
dependence (Grasha and Reichmann, 1975) and specific approaches to learning
in given environments and within particular assessment regimes, such as
deep versus surface approaches to studying (Entwistle, 1988; Marton and
Saljo, 1976).
Riding (1997) presents a "cognitive control" model (a theoretical
elaboration of Curry) consisting of primary sources (knowledge,
personality, gender and cognitive history), cognitive control (the wholistanalytical and verbal-imagery dimensions of cognitive style) and cognitive
input (perception) and output (learning strategies). Like the onion model,
it is an attempt to unify the relationship between apparently similar
constructs. The aim of this paper is to examine, in the context of business
and management education, the implicit proposition in the onion and
cognitive control models, that learning preference is related to cognitive
style. This has clear implications for:
• the planning and design of business and management education;
• training and development in organisational contexts through the matching
(or mismatching) of teaching and learning methods to the cognitive style of
the learner;
• the development of stylistic versatility (by complementing style with
strategies).
Learning preferences
Learning preferences may be defined as an individual's propensity to choose
or express a liking for a particular teaching or learning technique or
combination of techniques (Sadler-Smith, 1996). From the work of Reichmann
and Grasha, (1974) and Renzulli and Smith (1978) it is possible to
synthesise three groups of learning preference:
(1) dependence: preference for teacher-directed, highly structured
programmes with explicit assignments set and assessed by the teacher;
(2) collaboration: discussion-orientation and favouring group projects,
collaborative assignments and social interaction;
(3) independence: preference for exercising an influence on the content and
structure of learning programmes within which the teacher or instructor is
a resource (Sadler-Smith and Riding, 1999).
The learning preference construct has not been as widely researched as
learning style, approaches to studying or cognitive style. Learning
preferences represent the outer skin of the "onion" and as such they are
the most easily accessible but least stable of the constructs and are the
interface between the internal world and external learning environment.
Like "learning styles" and "approaches" (which may be considered as
varieties of learning strategy), preferences are ways of dealing with the
external world (see Figure 1). They differ from learning strategies in that
the latter are ways in which the individual acquires and assimilates
information, whereas the expression and operationalisation of learning
preferences are the ways by which the learner attempts (by accommodating
her/his preferences) to adapt to or cope with the demands of the external
learning environment.
Figure 1 Learning preferences, styles, strategies and cognitive style
Sadler-Smith (1997) found statistically significant correlations between
learning preferences and learning style (learning styles questionnaireHoney and Mumford, 1992) and approaches to studying (revised approaches to
studying inventory - Entwistle and Tait, 1994) but not between learning
preference and cognitive style (cognitive styles analysis -Riding, 1991).
The present study will explore the latter, using an alternative model of
cognitive style.
Cognitive style
Messick (1984, p. 5) described cognitive style as "consistent individual
differences in preferred ways of organising and processing information and
experience". Steinberg and Grigorenko described it as representing "a
bridge between what might seem to be two fairly distinct areas of
psychological investigation: cognition and personality" (Steinberg and
Grigorenko, 1997, p. 701). A number of assumptions relating to cognitive
style may be identified:
(1) it is concerned with the form rather than the content of information
processing;
(2) it is a pervasive dimension that can be assessed using psychometric
techniques;
(3) it is stable over time;
(4) it is bipolar;
(5) it may be value differentiated (i.e. styles describe "different" rather
than "better" thinking processes) (Sadler-Smith and Badger, 1998).
One model of cognitive style which satisfies these criteria for a
"cognitive style" and lends itself to research in a business and management
context is the intuition-analysis dimension (Allinson and Hayes, 1996). The
style models of Allinson and Hayes and Honey and Mumford may be traced back
to their origins in Jungian psychological types. Hurst et al. (1989) in a
useful, but concise, summary, described the "types" in terms of information
gathering modes (intuition versus sensation) and information evaluation
modes (feeling versus thinking) to give four basic types (intuitingfeeling; intuiting-thinking; sensing-feeling; sensing-thinking). Intuition
was defined by Bunge (1983, p. 2A8) as "that ill-defined ability to spot
problems or errors, to "perceive" relations or similarities ... in short to
imagine, conceive, reason or act in novel ways". Analysis, on the other
hand, is often presented as the antithesis of intuition: "to analyse ... is
to exhibit [an object or system's] components, environment (or context) and
structure (organisation)" (Bunge, 1983, p. 219). Hurst et al. (1989) went
on to speculate that differences in preferences for each type of thinking
may be related to hemispherical differences in the brain: "sensing and
thinking are left hemisphere related and intuition and feeling right
hemisphere related" (Hurst et al., p. 91). This echoed the views of
Mintzberg (1976): "in the left hemisphere of most people's brains the
logical thinking processes are to be found ... in contrast the right
hemisphere is specialised for simultaneous processing; that is it operates
in a more holistic ... way". More recently, Mintzberg (1994a, p. 114) has
re-stated these ideas in the context of strategic planning, arguing that
the planning function in organisations is populated by two types of person:
the analytic ("left-brained') thinker and the creative ("right- brained")
thinker. He expressed the view that organisations need both types in
"appropriate proportions" (see also Leonard and Strauss (1997) on the
"whole-brained organisation"). Like Hurst et al. (1989) and Mintzberg
(1976), Allinson and Hayes (1996) speculated on hemispherical differences
in the brain as a possible basis for cognitive style differences (stemming
from the work of Sperry and others - see Nebes and Sperry (1971); they too
use the term "intuition" to describe "right brain" thinking (i.e. immediate
judgement based on feeling and the adoption of a global perspective) and
"analysis" for "left brain" thinking (i.e. judgement based on mental
reasoning and a focus on detail). "Style" in this context is the dominance
of one mode of thinking over the other and describes "different" rather
than "better" approaches to learning, problem solving, etc.
It should be noted that the attribution of differences in analytical versus
intuitive behaviour to hemispherical differences in brain functioning
should, in the absence of firm neuro-physiological evidence, be treated
metaphorically rather than literally (see Riding et al. (1997) for a neurophysiological study of cognitive style). Finally, Allinson and Hayes'
intuition-analysis dimension of style may be considered to be broadly
equivalent to the wholist-analytical dimension (Riding, 1991) and the
adaptor-innovator dimension (Kirton, 1994), though there is a pressing need
for concurrent validity studies.
Style, preferences and performance
Hayes and Allinson (1996) reviewed 19 studies which investigated the
effects of matching styles to learning method and found that in 12 studies
there was some support for the proposition that matching style and method
contributed to improved learning performance. Fox (1984, p. 72) argued that
"continuing educators must develop programmes that meet the needs of
learners" and suggested that some participants do not "fit" with certain
activities. Smith and Renzulli (1984) argue that congruence of style and
method can have an effect on learner motivation and "investment" in the
learning material. Equally important, matching can "help eliminate barriers
to learning which arise when we [educators] fail to address the affective
response various teaching modalities elicit from students" (Smith and
Renzulli p. 74). Dunn (1984) reviewed several studies in which she found
that where students were placed in academic situations where they were
taught and/or tested in ways that matched or mismatched their self-reported
preferences, those who were matched performed better than those who were
mismatched. This led her to conclude that "their preferences must be their
strength" (Dunn, 1984, p. 13). Miller (1991) took a somewhat different
view: he argued that the analytic-holist model of style allows the
possibility of individuals who are skilled at both analytical and holistic
functioning- referred to as "versatile". He went on to discuss the issues
surrounding attempts to engender "versatility" in those already not
predisposed towards it. However, his conclusions are that to do so in all
students is a waste of time and is potentially damaging and dangerous
(given that styles may be forms of psychological defence). He argued that
extremely specialised students should be left alone but that teaching
should be accommodated to these styles and that versatility is a reasonable
goal in those who are already disposed to it. The challenge as far as
Miller was concerned was to identify the specialised and the "protoversatile" (Miller, 1991, p. 236). The "versatility" argument (perhaps
through the mismatch) is echoed in the pleas from Mintzberg (1994) for
balance in strategic planning teams and Leonard and Strauss (1997) to
harness the "energy released by the intersection of different thought
processes" to propel innovation (p. 121). The challenge, therefore, for
business schools and human resource development practitioners, is to
acknowledge the differences that exist between individuals and use the
differences constructively, for example, by giving careful consideration to
when to "match", when to "mismatch" and how to engender cognitive
"versatility".
At a more superficial level, the onion and cognitive control models suggest
that cognitive style may exert some influence over preferences for
different learning methods (for example role play versus lectures). Riding
(1991) has argued that style may affect social behaviour, which may suggest
that intuitives will tend to be dependent and gregarious and prefer
collaborative ' learning situations, while analysts may be isolated and
self-reliant. Hence, it may be expected that different business and
management teaching and learning methods, with their varying degrees of
social interaction and autonomy, would be viewed more or less favourably by
different cognitive style groups. Similarly, with respect to the cognitive
aspects of learning, Allinson and Hayes (1996) argued that analysts may
prefer to pay attention to detail, focus on "hard" data, adopt a step-bystep approach to learning and are self-reliant. This suggests that analysts
may prefer learning methods which allow opportunities for independent work
with the opportunity to analyse data and reflect on information and
experiences. Leonard and Strauss (1997) suggested that abstract thinkers
(who share some of the attributes of analysts) will prefer to assimilate
information from a variety of sources such as books, reports, videos, etc.
Conversely, Allinson and Hayes (1996) argued that intuitives are less
concerned with detail, adopt a global perspective and take an actionoriented approach to learning and problem solving. These "experiential"
individuals will prefer to get information from "direct interaction with
people and things" (Leonard and Strauss, 1997, p. 113). This may lead one
to suggest that intuitives may prefer learning methods which are active,
participatory and gregarious rather than analytical, reflective and selfreferential. Sadler-Smith and Riding (1999) in a study of learning
preferences and cognitive style (using the cognitive styles analysis
(Riding, 1991)), found that wholists expressed a stronger preference for
collaborative methods (role play and discussion groups) than did analytics.
They attributed this to the gregarious nature and social dependence of the
wholists. Clearly, one challenge for research in this field is to build on
a growing empirical base.
The study
The study aimed to investigate the relationship between learning
preferences and the intuition-analysis dimension of cognitive style in the
context of business and management education and provide empirical
elaboration for the onion and cognitive control models.
Sample and data collection
The sample consisted of 226 undergraduates studying a range of business and
management degree programmes at a university business school in the UK. The
sample was an opportunity sample and participation in the research was
voluntary. Data were collected by means of a questionnaire which consisted
of three sections:
(1) the cognitive style index (Allinson and Hayes, 1996);
(2) a learning preferences inventory;
(3) respondent data.
Cognitive style. This was measured by means of the cognitive style index
(CSI) (Allinson and Hayes, 1996). The CSI is a paper and pencil inventory
consisting of 38 questions scored on a three point "true-uncertain-false"
scale. The theoretical maximum score is 76; the higher the score the more
analytical is the respondent's style.
Learning preferences. Because of the limitations of existing measures a new
questionnaire, the learning preferences inventory (LPI), was developed for
the purposes of this study and is an extension of exploratory work reported
in Sadler-Smith (1997) and Sadler-Smith and Riding (1999). The Reichmann
Grasha (1974) instrument, the Rezler and Resmovic (1981) and Dunn et al.
(1989) questionnaires appear to conflate notions of style and preference.
The LPI consists of 13 items (see Table I); respondents are requested to
indicate which teaching and learning methods they prefer in general
according to a fivepoint Likert scale ranging from "definitely like"
(scored five), through "neither like nor dislike" (scored three) to
"definitely dislike" (scored one). The instrument's psychometric properties
are discussed below.
Respondent data
Respondents' were requested to give their age, gender and programme of
study and were assured of anonymity and confidentiality.
Results
Characteristics of the sample
The sample consisted of 128 (56.64 percent) males and 98 (43.36 percent)
females; the mean age was 21.00. Respondents were a second year cohort in a
single higher education institution in the UK; it is acknowledged
therefore, that the characteristics of the sample are likely to introduce
severe bias. This is compounded from an international perspective since the
subjects have in the main experienced the UK's primary and secondary
educational systems, which are likely to exert a considerable influence
over their learning preferences (see Figure 1).
Item and factor analysis
The CSI has previously demonstrated construct validity through confirmatory
factor analysis and correlational studies (see Allinson and Hayes, 1996).
Its level of internal consistency is high, ranging from 0.84 to 0.92 and
Allinson and Hayes (1996) report test re-test reliabilities of 0.90.
The LPI's factor structure was investigated by mean of a principal
components analysis. Examination of the scree plot (Cattell, 1966)
suggested that three factors (accounting for 42.2 percent of the variance)
should be extracted. The three extracted factors were rotated to simple
structure by means of a varimax rotation (the three factors were not intercorrelated). The resultant factor matrix with loadings of less than 0.4
suppressed is shown in Table I.
Factor I consists of methods which are active (for example role play
exercises, workshops and practical classes) and participatory (for example
giving presentations and seminars). Factor I was labelled "active". Factor
II consists of methods which are reflective and didactic (for example,
lectures) and self-directed (for example computer based and self-study
methods). Factor II was labelled "reflective". Two items had high loadings
(> 0.5) on Factor III individual work loaded positively and group work
loaded negatively. Factor III was labelled "individual".
Descriptive statistics
Cognitive styles. The level of internal reliability for the CSI was high
(see Table m). CSI scores by gender are shown in Table II. Hayes et al.
(1998) argue that gendered stereotypic thinking "suggests that intuition is
a feminine characteristic whereas analysis is a masculine characteristic"
and go on to test this view. In a comparison of style and gender, using a
sample of under-graduate business and management students, they found
highly significant gender differences (p < 0.001) in cognitive style, with
females (43.84; SD, 14.02) being more analytical than males (M, M, 36.33;
SD, 15.56). This was the converse of the stereotypical view of "female
intuition". Although in the present study females did generally score
higher than males the differences were only marginally significant and
hence style and gender may be considered independent in this context (see
Table II).
There is some ambiguity in gender-related style differences. For example,
Riding and Rayner (1998) argued that style is independent of gender.
Complementary work using the CSI in a professional development context
appears to suggest that while style and gender are independent they appear
to interact in their effect on learning preferences. There is a need for
further research into the relationship between style and gender and their
combined effect on learning and workplace behaviours.
Learning preferences. The mean scores for each of the three learning
preference scales identified were computed and are shown along with their
inter-correlations in Table III. The levels of internal consistency
(coefficient x) were as follows:
(1) active (0.50);
(2) reflective (0.59);
(3) individual orientation (0.81).
While the latter was satisfactory, the x's for active and reflective were
low but considered acceptable for use in this exploratory study.
The three factors were not correlated among themselves. The general
preference was in favour of reflective methods (M = 3.53; SD = 0.63), while
individually-oriented methods were least preferred (M = 3.32; SD = 0.74),
however, the observed differences were small.
Cognitive style and learning preferences
The lack of any important differences in the preferences expressed by the
sample as a whole compounds the potential importance of any style-related
differences, especially from the point of view of the planning and design
of business and management education. The relationship between CSI score
and learning preferences was explored by means of simple linear
correlations. There were statistically significant correlations between CSI
score and:
(1) reflective methods (r = 0.32; p < 0.001);
(2) individually oriented methods (r = 0.25; p < 0.001) see Table III.
The effect of style was further investigated by means of a one way analysis
of variance in order to test for any non-liner relationships. The sample
was divided into three cognitive style groupings: intuitives (0 < CSI <
39); intermediates (39 CSI 48); analysts (48 < CSI < 76). Mean preferences
for the three methods for each of the style groups are shown in Table IV.
The intuition-analysis model of style leads one to anticipate stronger
preferences for active methods on the part of the intuitives. However,
there were no significant differences in this regard, therefore the
assertion that intuitives will prefer active/participatory methods was not
supported. The model also leads one to anticipate that for:
(1) reflective methods the analysts would express the strongest preferences
and the intuitives the least strongest;
(2) individually-oriented methods the analysts would express the strongest
preferences and the intuitives the least strongest
These data support both of these assertions (see Table IV). A two way
analysis of variance (style by gender) did not reveal any statistically
significant main effects for gender or interactions of gender and style in
their effect on learning preferences.
Discussion
The onion model and cognitive control models (Curry, 1983; Riding, 1991)
infer a relationship between cognitive style and learning preferences,
albeit with the latter influenced by the learning environment and context.
The present study has lent some support to the notion of learning
preference being a correlate of cognitive style. With respect to analysts,
the assertion that they would prefer reflective and individually oriented
methods received support. With respect to the intuitives the assertion that
they would express a dis-preference for reflective and individuallyoriented methods also received support. Therefore, these data would suggest
that there is a relationship between cognitive style and preferences for
reflective and individually oriented methods. This may suggest that
cognitive style manifests itself in learning situations as a preference for
those methods which the learner unconsciously or consciously perceives as
matching their preferred way of organising and processing information.
Under such circumstances the learner may anticipate a benefit which may
have a concomitant effect on motivation. The majority of empirical studies
(Dunn, 1984; Hayes and Allinson, 1996) present evidence in favour of
matching style and method. However, as noted earlier, some have argued that
it is beneficial for the learner to consciously expose themselves to
methods which do not match their preferred style in order to develop a
wider range of learning skills ("learning-to-learn") (Entwistle, 1988;
Honey and Mumford, 1992) and gain a "meta-cognitive advantage". The
empirical evidence in favour of the mismatch of method and style is less
robust than that which supports the concept of matching (Hayes and
Allinson, 1996), although the latter is hardly unequivocal. It could be
argued that mismatching learner and learning method is potentially valuable
in the hands of a skilled facilitator with clearly formulated objectives
and is perhaps one way in which learning-to-learn may be engendered.
The anticipated preference for participatory/active methods on the part of
the intuitives did not receive support. This may suggest that: there is no
simple and direct relationship between style and preference with respect to
participatory/active methods; there are idiosyncrasies in the
participatory/ active methods used in the institution concerned which
intervened to confound any relationship with style; the relevant scale of
the LPI may be a crude and underdeveloped measure (it had the lowest level
of internal reliability) of preference for participatory/active methods.
The latter could be improved by the exclusion of those items which loaded
ambiguously (i.e. "seminars") or had the lowest factor loadings (i.e.
"giving presentations")- see Table I. The relationship between style and
preference is worthy of further investigation, using undergraduate samples
from a broader range of educational institutions, post-graduate and
professional development students and, most importantly, randomly selected
work-based samples. The extension of this work into international contexts
(given the UK bias in the present study) in order to explore the cross
cultural validity of the style and preferences constructs and their interrelationships would also be potentially valuable.
Conclusion
The aim of this study was to examine the validity of the onion and
cognitive control models and it is argued that limited support has been
provided. Two central issues may be identified: the status and validity of
the "matching hypothesis"; and the notion of learning-to-learn. The two
issues are related in that if individuals achieve the latter the former
becomes a redundant concept. A key aspect of learning-how-to-learn is
strategy development. Riding and Sadler-Smith (1997, pp. 204-5) argued that
individuals may adopt a three-stage approach to strategy development based
on the fit between their cognitive style and the demands of the learning
situation. The first stage is sensing the extent to which the learner feels
comfortable with the situation in terms of their own preferences. The
second stage involves them, as they become more metacognitively aware, in
selecting the most appropriate learning methods. The third stage is
strategy development in which individuals attempt to make learning "easier"
by translating, adapting or reducing the processing load imposed on them by
the situation. This suggests that explicit acknowledgement of cognitive
style and learning preferences (along with learning styles and approaches
to studying), perhaps through comprehensive "profiling" of these
attributes, may be an important step forward in bringing learners and
management educators together in an understanding of each other's styles
and their mutual interdependence. This is crucial since one of the keys to
efficient and effective performance in both the classroom and the workplace
is the ability to balance intuition and analysis, since neither is
sufficient by itself.
Table I. Factor Matrix for the LPI
Item
Factor I
Factor II
Group work
Factor III
-0.72
Role play exercises
0.59
-0.47
Lecturer presenting
facts and theories
0.46
Lecturer presenting
Examples
0.47
Self-study
0.76
Texts and journals
0.60
Computer-based methods
0.60
Analysis of cases
0.55
Workshops and
practical cases
0.78
Problem solving
Exercises
0.64
Giving presentations
0.42
Individual work
0.72
Seminars
0.59
0.44
Table II. Cognitive styles scores by gender (*p = 0.05, one tailed test)
CSI
N
128
Males
M
43.27
SD
9.56
n
98
Females
M
45.41
SD
9.69
df
224
t
-1.67*
Table III. Learning preferences means, standard deviations, intercorrelations, reliabilities and relationship with cognitive style
CSI
CSI
0.89
Active
Reflective
Individual
Active
0.05
0.50
Reflective
0.32***
0.10
0.59
Individual
M
SD
0.25***
-0.16*
0.12
0.81
44.25
3.43
3.53
3.32
9.66
0.60
0.62
0.74
Note: Coefficient alphas are shown in bold along the diagonal.
223 [< or equal to] n [< or equal to] 226 ; *p < 0.05; **p <
0.01; ***p < 0.001
Table IV Cognitive style and learning method preferences
Intuitives
(n=71)
M
SD
Active
3.29 0.64
Reflective 3.31 0.66
Intermediates Analysts
(n=65)
(n=89)
M
SD
M
SD
3.54
0.56 3.44
0.59
3.53
0.56 3.70
0.58
df
221
221
F
2.77
8.32**
Individual 3.17
0.76
3.23
0.66
3.49
0.75
221
4.21*
Note: *P < 0.05; **p < 0.01
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~~~~~~~~
By Eugene, University of Plymouth Business School, Plymouth, UK
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Title: Can we generalize about the learning style characteristics...
Subject(s): LEARNING strategies; GIFTED children -- Education; ACADEMIC
achievement
Source: Roeper Review, May/Jun98, Vol. 20 Issue 4, p276, 6p, 3 charts
Author(s): Burns, Deborah E.; Johnson, Scott E.; et al
CAN WE GENERALIZE ABOUT THE LEARNING STYLE CHARACTERISTICS OF HIGH ACADEMIC
ACHIEVERS?
In 1980 Dunn and Price used their Learning Style Inventory (Dunn, Dunn &
Price, 1975) to investigate differences between the learning style
preferences of high academic achieving students and the preferences
expressed by same-age students with average or below average academic
achievement The purpose of the study described in this article was to
determine if and how the learning style preferences of a different group of
high academic achieving students, inventoried at a later date, but with the
same instrument, differed from those identified in the original study. A
discriminant function analysis analyzed the learning styles data obtained
from 500 students in grades 4 - 8. While significant differences (p <.001)
in the preferences distinguished between the average and above average
achieving students groups, there was minimal overlap with the preferences
identified in original investigation. The authors conclude that the
differences within an achievement group may be as great as between groups,
and that it is improper to prescribe instructional methods or categorize
groups of learners by presuming that they have similar style preferences.
During the last 25 years, educators have collected and analyzed a great
deal of information about students' learning styles. Researchers such as
Barbe and Swassing (1979), Dunn, Dunn and Price (1975), Gregorc: and Ward
(1977), Hill (1971), Hunt (1981), Kolb (1978), McCarthy (1980), Myers and
Myers (1980), Renzulli and Smith (1978), and Schmeck (1977), developed
instructional and theoretical models to explain differences in how students
acquire and process information. Although the constructs that underlie
these models vary (Ferrell, 1983), each researcher attempted to develop a
system that allows teachers to identify, formally or informally, the
special learning characteristics of these students and to modify
instructional practices accordingly to improve the effectiveness of
instruction and to increase academic achievement.
Although a number of studies suggest that there may be merit in addressing
students' learning styles as a technique for improving achievement and
attitude toward school (e. g., Cafferty, 1980; Carbo & Hodges 1988; Domino,
1979; Doyle & Rutherford, 1984; Lynch, 1981; Shands & Brunner, 1989; Shea,
1983), some educators remain skeptical about the value of such information
(Kavale & Forness, 1987; Pask, 1988). Weak research designs, the lack of
randomly selected samples, and a "premature rush into print and marketing
with very early and preliminary indications of factor loadings based on one
data set" (Curry, 1990, p. 51) also influence the degree to which findings
can be generalized to broad student populations. In addition, the
questionable reliability and validity of some learning style instruments
(Stahl, 1988), and unanswered questions about the malleability or
durability of students' learning style preferences (Davidman, 1981) still
plague the field.
Despite these problems and a host of competing models and nomenclature
within the field of learning style research, educators agree that attention
to the individual learning style of a given student holds promise as a
technique for improving school performance. This technique, however, may be
an inappropriate and ineffective teaching tool if we assume that
instruments designed to diagnose students' individual learning style
preferences predict the learning styles of specific student groups such as
Native American, reading-disabled, middle school, or high achieving
students.
Learning Styles Research in Gifted Education
In the past, investigators conducted a number of studies to address
possible learning style differences between groups of students. Researchers
in the field of gifted education investigated the degree to which a
consistent pattern of learning style preferences distinguish high achieving
students from the general population. Most of these studies used one of
three diagnostic instruments (Dunn, Dunn & Price, 1975; Barbe & Swassing,
1979; Renzulli & Smith, 1978) appropriate for students in grades K-12.
Stewart (1981) administered Renzulli and Smith's Learning Style Inventory
to a sample of general education and high achieving students in grades 4,
5, and 6, and found academically able students exhibited preferences for
independent study, discussion, and lecture; factors significantly different
from the preferences of the general education students. Wasson (1980) used
the same instrument and found that the grade 4, 5, and 6 gifted achieving
and underachieving students differed in their preferences for teaching
games, independent study, peer teaching and programmed instruction.
Although both of these studies used the same grade level subjects and
instruments, the learning styles identified as significant in the studies
differed in three out of four factors.
Kreitner (1981) and Kirchoff (1980) administered the Swassing-Barbe
Modality Index to assess modality preference (e.g. visual, auditory,
tactile kinesthetic) with two different groups of high achieving subjects.
Kirchoff concluded that modality strength is not a fixed characteristic.
Instead, it usually changes over time, with high academic achievers
demonstrating an integration of modalities at an earlier age. Surprisingly
enough, Kreitner also found that musically talented adolescents did not
demonstrate a preference to learn auditotally, though they did indicate
strong perceptual preferences. In addition, his subjects preferred not to
team through lecture. Again, neither researcher found similar learning
styles when comparing two different groups of high achieving students.
Researchers also used the Dunn, Dunn and Price Learning Style Inventory
(LSI) (1975, 1979, 1985, 1987, 1989) to examine possible differences
between the learning style preferences of academically able students and
the general student population. Dunn and Price (1980) conducted a study
that involved gifted education students. They compared the learning style
preferences of high achieving grade 4 - 8 students with counterparts in the
general population. They concluded that high achieving students perceived
themselves to be more persistent and had stronger preferences for tactile
and kinesthetic learning than students in the general population. In
addition, they found that high achieving students preferred a formal
classroom design and less structure. These students also saw themselves as
less responsible than their peers.
Griggs and Price also administered the 1975 version of the Dunn, Dunn and
Price LSI Inventory to students who were identified for participation in a
gifted education program and to students who were not identified. The LSI
scores for the grades 7 - 9 academically able students indicated that these
students perceived themselves to be less teacher-motivated. They preferred
quiet and a less formal design, perceived themselves to be more persistent,
preferred learning through visual, tactile or kinesthetic means rather than
through auditory means, and preferred to learn alone rather than with adult
or peers.
Ewing and Yong's (1992) study used a 1987 version of the LSI with 125
gifted African-American, Mexican-American and Chinese-American students in
grades 6 - 8. Preferences for noise, light, visual modality, studying in
the afternoon and perceptions of their own persistence significantly
differed for students in the three groups.
The Need for Additional Studies
Although some of the variables associated with the learning style
characteristics of high achieving students seem consistent from study to
study, no clearly defined pattern has emerged. To some degree, the
differences in these studies' findings are related to the differences in
the constructs measured by the three instruments; for example, the Barbe
and Swassing Inventory analyzes modality preferences, while the Renzulli
inventory assesses learner preferences for a variety of instructional
strategies. Yet, even within the groups of studies that used the same
instrument, conflicting sets of significant variables emerge from the
findings.
Some of these differences are explained by the fact that the grade levels
of the students tested were not identical in all studies, nor were the
social, cultural, experiential or economic backgrounds of the students. The
results of other learning styles studies, outside the field of gifted
education, suggest that preference differences between students may be
related to gender, learning disabilities, reading achievement, culture,
age, and a host of other factors. Viewed together, the studies in general
education and the studies in gifted education bring to question whether
differences in learning style preferences are just as great within the
gifted education population as they are between gifted education students
and students in the general population.
Despite the issues raised when generalizing from these studies, some
educators suggest that high achieving students do share similar learning
style preferences (Barbe & Milone, 1982; Dunn, 1993; Griggs & Price, 1980;
Ricca, 1984; Ross & Wright, 1987), and that these preferences may account
for differences in their academic achievement. However, before educators
can generalize from the work of these researchers to the population of
academically able students at large, one must first verify that the
variables identified in the above studies apply to additional, but similar
groups of high achieving students.
Subsequent studies are warranted in light of the many recommendations,
presented in print and in oral presentations, that have been made over the
years regarding the learning style preferences-of all gifted education
students. However, in reviewing more than a dozen studies on the learning
style preferences of gifted education students, no such replication or
extension studies. For these reasons, we attempted to duplicate, as closely
as possible, the sample, the instrument, and the data analysis techniques
used in one of these studies; the 1980 research study by Dunn and Price.
A Description of the Original Study
The Dunn and Price study involved a total of 109 high achieving students
and 160 randomly selected students from the general population of grades 4
- 8, in eight different schools in the Eastern portion of the United
States. Researchers in the original study classified students as gifted if
they possessed an academic aptitude score of 130 or above, or an academic
aptitude score between 120 and 129 with scores in the 95th percentile or
above on the math and/or reading subtests of standardized norm-referenced
achievement tests. The researchers used the Otis-Lennon Academic Aptitude
Test to assess academic aptitude, and the 1975 version of the Dunn, Dunn
and Price Learning Style Inventory to assess learning style preferences.
Step-wise discriminant analysis measured differences in learning style
preferences for the gifted and non-gifted education students in their
study, with the Learning Style Inventory variables serving as predictor
variables. The statistical analysis determined which of the Learning Style
Inventory variables discriminated significantly between the two groups of
students, minimizing Wilks' lambda coefficient. The F for inclusion and
deletion was set at 1.0, with a tolerance level of p < .001. Bartlett's chi
square (52.97, 9 df, p < .001) was significant when Wilks' lambda of .817
was tested for one discriminant function.
The discriminant analysis identified six variables that significantly
differed between the two groups. Gifted students in the Dunn and Price
study preferred to learn through their tactile and kinesthetic senses, and
indicated less of a preference than the non-gifted students for using their
auditory sense of learning. The non-gifted students preferred an informal
design, required structure, saw themselves as more responsible, but not as
persistent, and preferred to use their tactile and kinesthetic senses less
and their auditory senses to a greater extent than the gifted students.
On the basis of these six variables it was possible to predict membership
in the gifted education group with 53 percent accuracy and predict with 81
percent accuracy membership in the non-gifted group. The authors concluded
that gifted education students do possess identifiable learning style
preferences that are different from the preferences of students in the
general population. The significant variables that differed for the two
groups included style preferences related to independence, persistence,
perception and motivation. The authors also suggested that the LSI might be
used as an alternative identification instrument.
Extending the Original Study
In reviewing the Dunn and Price findings, the question arose about whether
these results would be similar with another sample of gifted education
students. The inferred hypothesis was that if these findings were
consistent from sample to sample, then recommendations for modifying
identification or instructional strategies within gifted education programs
might be justified. For this reason, an extension study was conducted in
order to investigate the following questions:
Do high academic achieving students differ from average and low achieving
students with respect to their learning style preferences?
If statistically significant discriminating variables do exist, are they
the same as the variables identified in Dunn and Price's study?
Limitations The sample for this extension study involved 500 students, 54
percent more students than the sample in Dunn and Price's original study.
The ratio of high achieving students to students in the general population
was 19.8 to 100, as compared to Dunn and Price's 40 to 100 ratio. The
procedures used to identify students for the gifted and the non-gifted
groups in both studies involved similar, but not identical, criteria.
Although the students in both samples were enrolled in grades 4 - 8 in the
public schools, they live in different regions of the country. Due to the
constraints common to most research studies conducted in the public
schools, a random selection of gifted students was not possible in either
study.
Subjects The sample involved in the extension study included 99 high
achieving students and 401 students in the general population of grades 4 8. These students were enrolled in the elementary, junior high, and middle
schools of nine public school districts in one Midwestern county of the
United States. Communities within the county included metropolitan,
agricultural, industrial, and suburban settings. Students categorized as
gifted for the purposes of this study had scores at or above the 95th
percentile on the Comprehensive Test of Basic Skills or the Iowa Test of
Basic Skills or academic aptitude quotients of 125 or above on the
California Test of Academic Aptitude. All parents and students received
invitations to participate in the study and did so in order to acquire more
information about the students' learning style preferences. Three local
classroom teachers, two administrators and a gifted education coordinator
received training in the administration of the 1975 version of Dunn, Dunn
and Price's Learning Styles Inventory prior to using the instrument with
students.
Instrumentation Although a 1989 edition of the inventory is now available,
the goal was to eliminate any problems that might arise from analyzing data
from two different versions of the instrument. For this reason, researchers
searched for and used student data from 198 1; data that used the same 1975
version of the LSI as Dunn and Price's 1980 study. This version of the LSI
assessed grades 3-12 students' beliefs about 34 variables within 13
categories related to learning conditions:
noise level - quiet or sound;
light - low or bright;
temperature - cool or warm;
design - informal or formal;
motivation - unmotivated or motivated;
persistence - impersistent or persistent;
responsibility - irresponsible or responsible;
structure - more structure or less structure;
Sociological - learning with peers, alone, in pairs, in a team, with an
authority figure, or varied;
perceptual - visual, auditory, tactile, kinesthetic or combined;
intake - requires food or does not require food;
time - morning, late morning, afternoon, or evening; and
mobility - prefers mobility or no mobility.
Students completed the inventory in a 30 - 40 minute session and responded
to each of the 100 items in the inventory with a dichotomous, true or
false, rating. Students rarely exhibit a strong preference for all
variables. Instead, the inventory analysis usually identifies the 6 - 14
variables of greatest influence to the learning process. The inventory
analysis identifies students' extreme preferences. Scores range from 20 80 with a mean of 50 and a standard deviation of 10. Scores of 60 or higher
indicate a strong preference for a given factor, while scores of 40 or
below indicate a negative preference.
Principal components analysis with the 1975 instrument identified 32
factors, not 34, each with an eigenvalue greater than 1.0, which
collectively explained 62 percent of the variance. The authors report
reliability coefficients on the internal consistency for the various scales
that range from .40 to .84. Subsequent editions of the instrument used a
Likert scale and fewer variables to increase reliability coefficients.
Data Analysis
Researchers used SPSS step-wise discriminant analysis to find the linear
combination of variables that best discriminated between the high achieving
and the general sample of students using the 34 variables on the 1975
Learning Style Inventory. The analysis identified which factors on the
Learning Style Inventory significantly discriminated between the two groups
of students minimizing Wilks' lambda coefficient. The F for inclusion and
deletion was 1.0, and the tolerance level was p < .00 1. Prior probability
for each group was set at .50. Bartlett's chi square (55.031, 14 df, p <
.0001) was significant when Wilks' lambda of .894 was tested for one
discriminant function for high achieving students versus students in the
general population.
Results
In addressing the first research question, "Do high academic achieving
students differ from average and low achieving students with respect to
their learning style preferences?", it was found that of the 34 predictor
variables assessed in this study, 14 variables discriminated significantly
between the subjects in the high achieving group and students in the
general population. Table I summarizes the order in which these variables
entered the discriminant equation, the F to enter, and Wilks' lambda. The
standardized discriminant function coefficients are indicated in Table 2.
The classification procedure correctly classified 68 percent of the
students. Based on the 14 variables that significantly discriminated
between the two groups, 62 percent of the high ability students and 70
percent of the students in the general population were correctly
classified. This yields a discriminant power above chance of 12 and 20
percent, respectively. Table 3 summarizes the classification procedures for
these students. It must be noted however, that, as in the Dunn and Price
study, the jackknife procedure for classification (Tabachnick and Fidell,
1983) was not used and that bias did enter this classification procedure.
The square of the canonical correlation of .326 for the function equation
demonstrated that 10.60 percent of the variance between the two groups can
be accounted for by the significant variables in the discriminant equation.
An analysis of the significant variables and the group means showed that
the high achieving group preferred little structure and an informal design,
accepted sound, low mobility and bright light in the learning environment,
and perceived themselves to be more persistent than their classmates in the
general population. The students in the general population preferred a
quiet learning environment with low light. They perceived themselves as
less responsible, more adult motivated, and preferred to learn alone
through auditory means in the late morning or early afternoon. Structure
and sound preferences accounted for the greatest differences between the
two groups.
Discussion
Although both studies produced a statistically significant function
equation that successfully discriminated between the learning style
preferences of high achieving students and those in the general student
sample, neither study identified the same set of variables. The only
variables consistent for the high achieving students in both studies were
the personality trait of persistence and a preference for little structure.
Students in the general population of both groups showed preferences for
learning through auditory means.
More disturbing, however, is the fact that the two studies produced
contradictory results in two crucial categories. Dunn and Price's high
achieving students preferred a formal class room design, while the students
in this study preferred an informal design. Dunn and Price's high achieving
students saw themselves as less responsible than their peers in the general
population, while this study suggested that the students in the general
population, not the high achieving students, saw themselves as less
responsible. Seven variables; light, mobility, auditory and
tactile/kinesthetic learning, time preferences, adult motivation and the
desire to learn alone, were identified in one study, but not the other.
The discriminant function equation correctly classified the high achieving
students in Dunn and Price's study at a level only 3 percent above chance.
The high achieving students in this study were correctly classified, above
chance, in only 12 percent of the cases. Even though students in the
general population constituted the majority of the research population, and
chance classification alone should have worked in their favor, only 21
percent in Dunn and Price's study and 20 percent in this study were
correctly classified. In all likelihood this occurred because the magnitude
of the difference between the two groups was insufficient to facilitate
predictions much greater than chance.
On the basis of these findings, the low percentage of shared variance, and
the fact that the discriminant equation in this study yielded
classifications only 18 percent above chance prediction, it is difficult to
accept the idea that the population of academically able students share
common learning style preferences. Certainly the current learning
environment of the students tested, the quality of their gifted education
program, their specific educational experiences, attitudes toward school,
and the demographic makeup of their community accounts for some of the
discrepancies noted above. These facts weaken the argument that most high
achieving students share common learning style preferences.
The discriminant function analysis yielded interesting information, but it
should not be taken as clear and irrefutable evidence that consistent
differences in learning style preferences do or do not exist between
achievement groups. Plainly, we are on shaky ground if we continue to
assume that certain learning style preferences are associated with
achievement test score levels. In the wrong hands, this conclusion might be
construed as evidence in favor of a new identification technique,
reminiscent of the characteristics checklists that were popular in gifted
education just a few years ago. The conflicting data between subgroups
suggests at least two possibilities. Either the original instrument was
flawed, or, individual differences between students accounts for more
variance in style preferences than group differences.
Although the 1975 LSI was subsequently revised to improve the factor
structure and the reliability of the instrument, no replication or
extension studies have been found for either the older or newer versions.
At the very least, the findings from this extension study strongly suggest
the need to conduct additional studies with the newer instruments. Until
such time, researchers should not attempt to synthesize the results of
learning styles studies that used more than one version of the LSI
instrument.
The evidence suggests that the learning style preference differences within
an academic achievement group may be as great as the differences between
the groups (Barbe & Milone, 1982). All style preferences may be equally
appropriate (Fischer & Fischer, 1979), and care must be taken to refrain
from placing value judgments on one preference over another. Educators must
recognize the emerging nature of learning style preferences (Hunt, 198 1)
and come to grips with the seemingly topical and temporal nature of such
preferences. Students change. They grow and adapt, and hopefully, become
increasingly adept at functioning with a variety of styles.
The real issue involves educators' ability to modify the learning
environment to deal appropriately with individual preferences. Some
researchers believe teachers should consistently teach to a student's
preferred learning style. Others believe that such modifications should
occur primarily during initial instruction or times of learning difficulty
(Barbe & Swassing, 1979). A third point of view advocates teaching all
students in all style variables in an attempt to foster independence (Hunt,
198 1; McCarthy, 1980). Perhaps the truth lies somewhere between the
extremes of this continuum.
Learning style preferences may or may not account for part of what
identifies a student as academically superior. However, the interaction of
style preferences and the learning environment (Ricca, 1984) precludes a
unilateral approach to instructional modification (Stewart, 1982). We
recognize the fact that learners are different. We also believe that in
general, it is helpful to recognize and accommodate these differences.
Based on our findings however, we have concluded that it would not be
prudent to prescribe instructional methods or categorize groups of learners
by presuming that they have similar style preferences on the basis of
singular research studies.
After additional replication or extension studies, we may find that
learning style inventories should be used as they were originally intended;
as informative diagnostic instruments to measure the learning style
preferences of an individual student. In other words, the instrument should
be used to take a "snapshot" of an individual in a particular situation, at
a specific point in time. It should not be used to take a group portrait.
Within gifted education, this information can be used during curriculum
compacting, content acceleration, or during self-directed student
investigations or research; not as an identification device, nor as a
blanket recommendation to view or to teach all students with similar
achievement levels in the same manner.
Table 1 Summary of the Learning Style Variables That Entered the
Discriminant Equation
Step
F to
Wilks'
Significance
number
Variable entered
enter
lambda
level
1
2
3
4
5
6
7
8
9
10
11
Little structure
Not responsible
Late morning
Needs quiet
Sound acceptable
Adult motivated
Persistent
Bright light
Informal design
Auditory preference
Needs little mobility
11.48
4.87
4.51
8.31
4.28
2.57
2.66
4.50
2.27
2.59
2.28
.966
.951
.941
.934
.928
.923
.918
.914
.909
.905
.902
.0001
.0001
.0001
.0001
.0001
.0001
.0001
.0001
.0001
.0001
.0001
12
13
14
Low light
Learn alone
Afternoon
1.79
1.55
1.12
.899
.896
.894
.0001
.0001
.0001
Table 2 Standardized Discriminant Function Coefficients for the Learning
Style Inventory
Learning Style Inventory
Factor Name
Coefficient
Quiet
Little Structure
Sound Acceptable
Bright Light
Late Morning
Not Very Responsible
Low Light
Persistent
Auditory
Adult Motivated
No Mobility
Informal Design
Alone
Afternoon
-.595
.477
-.437
.430
-.336
.324
.272
.233
.231
.228
.219
.213
-.182
-.149
Table 3 Percentage of Students Properly Classified by Group Using the
Function Equation
Legend for Table:
A
B
C
D
E
-
n
Hits
Misses
Percent of hits using DFA
Percent of hits beyond chance
Group
High
achieving
students
General
population
students
A
B
C
D
E
99
61
38
61.6%
11.6%
401
279
122
69.6%
19.6%
REFERENCES
Barbe, W. B., & Swassing, R. H. (1979). The Swassing-Barbe Modality Index.
Columbus, OH: Zaner-Bloser.
Barbe, W. B., & Milone, M. N. (1982, January - February). Modality
characteristics of gifted children. G/C/T, 5, 2-5.
Cafferty, E. (1980). An analysis of student performance based upon the
degree of match between the educational cognitive style of the teachers and
the educational cognitive style of the students. An unpublished doctoral
dissertation, University of Nebraska.
Carbo, M. and Hodges, H. (1988). Learning styles strategies can help
students at risk. Teaching Exceptional Children, 20, 55-58.
Curry, L. (1990). A critique of the research on teaming styles. Educational
Leadership, 48, 50-55.
Davidman, L. (1981). Learning style: The myth, the panacea, the wisdom. Phi
Delta Kappan, 62, 641-645.
Domino, G. (1970). Interactive effects of achievement orientation and
teaching style on academic achievement. ACT Research Report, 39, 1-9.
Doyle, W. & Rutherford, B. (1984). Classroom research on matching learning
and teaching styles. Theory into Practice, 2, 20-25.
Dunn, R. (1993). Learning styles of the multiculturally diverse. Emergency
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Dunn, R., Dunn, K., & Price, G. E. (1975). Learning Style Inventory.
Lawrence, KS: Price Systems.
Dunn, R., & Price, G. E. (1980). The learning style characteristics of
gifted students. Gifted Child Quarterly, 24, 33-36.
Ewing. N.J., & Yong, F.L. (1992). A comparative study of the learning style
preferences among gifted African-American, Mexican-American and American
Born Chinese middle grade students. Roeper Review, 14, 120-23.
Ferrell, B.G. (1983). A factor analytic comparison of four learning style
instruments. Journal of Educational Psychology, 75, 33-39.
Fischer, B. B., & Fischer, L. (1979). Styles in teaching and teaming.
Educational Leadership, 36, 245-254.
Gregorc, A. F., & Ward, H. B. (1977, February). A new definition for
individual. NASSP Bulletin, 2.
Griggs, S. A., & Price, G. E. (1980). A comparison between the learning
styles of gifted versus average suburban junior high school students.
Roeper Review, 3, 7-9.
Hill, J. (1971). Personalized education programs utilizing cognitive style
mapping. Bloomfield Hills, MI: Oakland Community College.
Hunt, D. E. (1981). Learning style and the interdependence of practice and
theory. Phi Delta Kappan, 62, 647.
Kavale, K.A., & Furness, S.R. (1987). Substance over style: Assessing the
efficacy of modality testing and teaching. Exceptional Children, 54, 228239.
Kirchoff. S. (1980). Modality strengths of gifted students. An unpublished
doctoral dissertation, Washington State University.
Kolb, D. A. (1978). Learning style inventory technical manual. Boston:
McBer & Co.
Kreitner, K. R. (1981). Modality strengths and learning styles of musically
talented high school students. An unpublished master's thesis, The Ohio
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Lynch, P. K. (198 1). An analysis of the relationships among academic
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of eleventh and twelfth grade students identified as initial or chronic
truants in a suburban New York school district. An unpublished doctoral
dissertation, St. John's University.
McCarthy, B. (1980). The 4MAT system: Teaching to learning styles with
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Myers, I., & Myers, P. Gifts differing. Palo Alto, CA: Consulting
Psychologists Press.
Pask, G. (1988). Learning strategies, teaching strategies and conceptual or
learning style. In R. R. Schmeck, (Ed.), Learning strategies and learning
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Renzulli, J. S., & Smith, L. H. (1978). The learning styles inventory: A
measure of student preference for instructional techniques. Mansfield
Center, CT: Creative Learning Press.
Ricca, J. (1984). Learning styles and preferred instructional strategies of
gifted students. Gifted Child Quarterly, 28, 121-126.
Ross, E.P., & Wright, J. (1987). Matching teaching strategies to the
learning styles of gifted readers. Reading Horizons, 28, 49-56.
Shands, R., & Brunner, C. (1989). Providing success through a powerful
combination: Mastery learning and learning styles. Perceptions, 25, 6-10.
Schmeck, R. R. (1977). Development of a self-report inventory for assessing
individual differences in learning process. Applied Psychological
Measurement, 1, 413-431.
Shea, T. C. (1983). An investigation of the relationship among preferences
for the learning style element of design, selected instructional
environments and reading test achievement of ninth grade students to
improve administrative determinations concerning effective educational
facilities. An unpublished doctoral dissertation, St. John's University.
Stahl, S.A. (1988). Evidence to support matching reading styles and initial
reading methods? A reply to Carbo. Phi Delta Kappan, 4, 317-22.
Stewart, E. D. (1981). Learning styles among gifted/talented students:
Instructional technique preferences. Exceptional Children, 48, 134-138.
Stewart, E. D. (1982). Myth: One program, indivisible for all. Gifted Child
Quarterly, 26, 27-29.
Tabachnick, B. G., & Fidell, L. S. (1983). Using multivariate statistics.
New York: Harper and Row.
Wasson, F. R. (1980). A comparative analysis of learning styles and
personality characteristics of achieving and underachieving gifted
elementary students. An unpublished doctoral dissertation, Florida State
University.
~~~~~~~~
By Deborah E. Burns, Scott E. Johnson, and Robert K. Gable
Deborah E. Sums is an associate professor of Educational Psychology and
director of the Three Summer Graduate Program in Gifted Education at the
University of Connecticut. Scott E. Johnson is a principal of a science,
technology and global studies elementary magnet school that serves East
Harford and Glastonbury, Connecticut. Robert K. Gable is a professor of
educational psychology and associate director in the Bureau of Educational
Research at the University of Connecticut.
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Title: Parenting styles and adolescents' learning strategies in the urban
community.
Subject(s): PARENTING -- United States; LEARNING strategies -- United
States; ADOLESCENT psychology -- United States
Source: Journal of Multicultural Counseling & Development, Apr98, Vol. 26
Issue 2, p110, 10p
Author(s): Boveja, Marsha E.
PARENTING STYLES AND ADOLESCENTS' LEARNING STRATEGIES IN THE URBAN
COMMUNITY
The purpose of this study is to examine the relationship, between perceived
parenting styles and urban adolescents' learning and studying strategies.
The results revealed that those adolescents who perceived their parents as
being authoritative tended to engage in more effective learning and study
strategies. Implications are discussed for counselors and teachers using
this information as a fostering tool in their work with urban adolescents.
The family is generally considered an important system that has a heavy
impact on the development of children and adolescents. Some studies have
identified child-rearing behaviors as variables that contribute to selfconcept development in children and adolescents (Mboya, 1995). A critical
component of parenting style is the way in which parents attempt to control
the adolescent. Much research has been conducted with White Americans on
this issue. Even though a clear picture has not emerged, researchers have
identified critical factors that seem to be significant to all adolescents
(Becker, 1994). In studies addressing how parenting practices affect the
development of children, findings have shown that parents who are
accepting, yet controlling (authoritative), have children who measure high
in school-related variables and mental well-being (Dubin, Darling,
Steinberg, & Brown, 1993; Hein & Lewko, 1994; Shucksmith, Hendry, &
Glendinning, 1995). These variables include the effective use of learning
and studying strategies.
Other research has shown that one of the key issues of school drop-out
rates has to do with parents not expressing love (Reich, 1991). The same
research found that adolescent drug abusers came from families in which
there was a greater communication gap between parents and youth and either
a highly authoritarian or highly permissive disciplinary style, all of
which contribute to a student having faulty learning styles and the lack of
opportunity to acquire effective studying strategies. In 1996, Laurence
Steinberg conducted a study (Salmon, 1996) with 20,000 teenagers and
hundreds of parents and educators nationwide and found that children raised
by parents who were authoritative did better in school than adolescents
from authoritarian or permissive homes. Therefore, counselors, teachers,
and parents must consider the impact parenting styles may have on urban
adolescents' ability to learn and study, if interventions are to be
developed to increase students' academic abilities.
However, there has been little research on the possible effects of
parenting styles on the academic performance and achievement among racial
and ethnic minority urban adolescents and how it relates to learning and
studying strategies. In a study done by Reich (1991) with White American
adolescents, it was hypothesized and concluded that perceptions of parental
love and control may be a significant factor in the teen's general school
achievement. There has also been little research on how counselors can
enable urban adolescents to become more aware of their learning styles.
Weinstein and Palmer (1990), who have done research with minority and
majority adolescents, suggested that students can become more aware of
their thinking and can comprehend and
motivated, have good time management,
however, are interventions that allow
identify their thought processes that
retain more information if they are
and can concentrate. What is missing,
adolescents to acknowledge and
affect their learning.
The school and the family provide a network of communication experiences
through which the individual learns the arts of speech, interaction,
listening, and negotiation, all of which are important in an adolescent's
study habits. Urban adolescents, who perceive too much or too little
support and control from their parents regarding the basic family
functions, are likely to be at risk in their intellectual development, thus
reducing their school achievement abilities (Olson, 1981). One
recommendation for attending to this problem has been the development of a
working alliance between parents and the school. This recommendation is
based on the premise that parents play a critical role in the school
behavior of their children. Research studies show that adolescents who have
parents high in demandingness and responsiveness are more social and have
high educational aspirations (Reich, 1991). Similar research results go
back more than 20 years. In their research with White Americans, Balswick
and Macrides (1975) discovered that a very restrictive (authoritarian) home
leads to a cyclical pattern of frustration and aggression. On the other
hand, a very permissive home can lead a youth to not know what the parental
expectations are, which then leads to aggression in search of norms. If
there are no checks on the aggression, an increased Amount of aggression is
expressed. Thus, an "authoritative" type of parenting may be beneficial for
increasing student achievement.
The purpose of this study was to determine if parenting styles affect urban
adolescents' learning and studying strategies and if counselors and
teachers can set up interventions to work with (a) Parents, to have them
incorporate more effective parenting styles, and (b) students, to improve
or strengthen their learning and studying strategies. It was hypothesized
that perceived authoritative parenting would result in urban adolescents'
using more effective learning and studying strategies than would those
adolescents with perceived authoritarian or permissive parenting.
Finally, the literature review and the findings of the current study should
contribute to counselors' knowledge in identifying urban adolescents'
unproductive thoughts and behaviors, thus enabling adolescents to become
more aware of themselves. Counselors and teachers can also use this
information as a tool to help urban adolescents focus on major issues that
are important in high school, such as listening and reading comprehension.
This tool could also entail the development of a measurement to analyze
urban adolescents' learning ability and to see what study strategies work
best for them. Finally, these results can be shared with parents as
parenting tips to help them work more effectively with their children.
METHOD
Participants
The sample was drawn from a population of currently enrolled high school
students (9th to 12th grades) in a large, eastern U.S. city. The total
student population of this school was 800, 60% (n = 480) were female
students and 40% (n = 320) were male students. The racial and ethnic
background of the students was 60% (n = 480) Hispanic American, 20% (n =
160) African American, 15% (n = 120) Asian American, and 5% (n = 40) other.
Of the students enrolled, 63% (n = 504) were in the 10th and 11th grades.
As reported by the school administration, 90% (n = 432) of the Hispanic
students spoke English as a second language (ESOL) and were required to
take ESOL classes. The administration also reported that 95% of the
students received government assistance for lunch. Finally, all the
students attended four 90-minute class periods a day.
I randomly selected 127 high school students. The sample was 56% female
students and 44% male students. The average age of the students was 16
years with an age range of 14 to 19. All grade levels were represented
within the sample: 20.5% (n = 26) were 9th graders, 49.5% (n = 63) were
10th graders, 15.0% (n = 19) were 11th graders, and 15.0% (n = 19) were
12th graders. Of the sample, approximately 60% (n = 76) were Hispanic
American, 24% (n = 30), African American, 13% (n = 4) Asian American, and
3% (n = 4) reported being in the "other" category. Similar to the overall
high school population, 100% of the sample reported receiving government
assistance for lunch.
Data was collected in 12 high school classrooms. Before doing this, I
obtained informed consent from school administrators to conduct this study
in the school to assist the school system in determining how best to attend
to and improve the learning and studying strategies among students.
Students served as voluntary participants, and the confidentiality of the
students, parents, and school identification was assured.
Procedure
I used a causal-comparative design in this study. Research packets were
distributed to all students in each class. Each packet included directions
and a three-part booklet to be completed within 1 hour. Part I was a
demographic profile sheet. Part 2 was a questionnaire that the researcher
modeled after the Perceptions of Parents Actions Questionnaire (PPAQ;
Schaefer, 1965; Streit, 1987). Part 3 was the Learning and Studying
Strategies Inventory-High School Version (LASSI-HS; Weinstein & Palmer,
1990). All sections of the booklet were in English. Students who had
difficulty in understanding questions were assisted by the classroom
teacher for translation and given additional time to complete the booklet;
furthermore, all teachers were bilingual in English and Spanish.
Measures
First, the demographic profile sheet was completed by all participants.
This measure requested participants to indicate sex, age, race and
ethnicity, and level in school.
The second measure was a modified version of the Perceptions of Parent's
Actions Questionnaire (PPAQ; Schaefer, 1965), a 104-item questionnaire that
was designed for adolescents to assess which parent (mother or father)
displayed a certain behavior (permissive, authoritative, or authoritarian)
in specific situations. The 48-item modified version was designed for
adolescents to assess if their parent(s) did or did not display a certain
behavior (permissive, authoritative, or authoritarian) in specific
situations. These revisions were made for two reasons: (a) I was not
concerned about which parent displayed the behavior, but rather, if the
behavior was displayed at all, and (b) there would not have been enough
time for participants to complete all the questionnaire items, thus the
first 16 items related to each parenting style were selected for the
modified version. Three parenting style clusters are assessed in the PPAQ:
permissive, authoritative, and authoritarian. Permissive parenting is
associated with low levels of control, including being neglectful.
Authoritative parenting combines reasoned control with support and concern.
Authoritarian parenting involves rigidly enforced rules allied to low
levels of acceptance. Each questionnaire was divided into the three,
predetermined categories of perceived parenting styles. Categories for each
student were determined based on the number of their responses of yes, no,
or undecided. Schaefer's instrument has been widely used since 1965 and has
proven to be a reliable and valid measuring device (Streit, 1981, 1987).
The LASSI-HS (Weinstein & Palmer, 1990) measures how students learn and
study by presenting statements that fall into 1 of 10 areas: attitude,
motivation, time management, anxiety, concentration, information
processing, selection of main ideas, study aids, self-tests, and test
strategies. The authors provide evidence for the reliability and validity
by indicating that when a test-retest reliability study was conducted on a
preliminary version of the inventory, a correlation of .88 was obtained for
the total instrument. This preliminary version had 130 items; the published
version contains 76 items. No other test-retest reliability data are
reported (Eldredge, 1990). Each inventory was divided into three,
predetermined levels of learning and study strategies: low = 1, average =
2, and high = 3. Categories for each student were determined based on their
responses from a 5-point Likert scale (strongly agree, agree, somewhat
agree, disagree, and strongly disagree) and from six steps: (a) Each
question was categorized in 1 of 10 strategies; (b) each question response
was assigned a numerical value from 1 to 5; (c) all numerical values were
calculated for each category (values ranged from 0 to 40); (d) a total of
10 numbers (1 for each category) was plotted in a table; (e) each table had
three levels of strategies; and (f) depending on the number of scores
within a strategy level, a numerical value of I (low), 2 (average), or 3
(high) was assigned. Construct validity has been established by comparing
LASSI-HS scale scores with other tests measuring similar learner behaviors,
and several of the scales have been validated against performance measures
(Eldredge, 1990).
RESULTS
The analysis of nominal data in this causal-comparative study involved
descriptive and inferential statistics. The descriptive statistics used
were means and standard deviations. Of all the participants, 69% (n = 87)
perceived their parents as being authoritative, 26% (n = 33) of the
students perceived their parents as being permissive, and 5% (n = 7)
reported a perception of authoritarian parenting. The findings also
revealed that 49% (n = 62) of the students reported very limited use of
effective learning and study strategies, 43% (n = 55) reported average use
of strategies, and 8% (n = 10) reported extensive use of such strategies.
The mean score for parenting styles was 2.20 (SD = .52; i.e., authoritarian
= 1, authoritative = 2, and permissive = 3). The mean score for the
strategies was 1.59 (SD = .63; i.e., low = 1, average = 2, and high = 3).
The inferential statistic, chi-square, was used to compare perceived
parenting styles (see Table 1) to examine the relationship between group
frequencies (permissive, authoritative, authoritarian) in parenting and
learning and study strategies. A significant (p < .01) association between
perceived parenting styles and learning and study strategies (n = 127, df =
2) was found. Those participants who perceived their parents as being
authoritative also engaged most often in effective learning and study
strategies. Participants who perceived their parents as permissive were
found to engage least often in such strategies.
DISCUSSION
The results of this study support the initial hypothesis that perceived
authoritative parenting style would be significantly associated with urban
adolescents' use of effective learning and study strategies. This
association has also been documented in other research studies addressing
the link between parenting styles and adolescents' academic achievement
(Dubin et al., 1993; Hein & Lewko, 1994; Shucksmith et al., 1995).
In addition, I believe that it is most important to note the
underrepresentation of students who indicated the use of effective learning
and study strategies. Data from this study indicated that approximately 50%
of the adolescents in this sample reported below average use of effective
learning and study strategies. This was found to be the case even though
more parents were perceived as being authoritative. Such findings suggest
that even though parents are perceived as being high in demandingness and
responsiveness (authoritative), a critical representation of urban
adolescents continues not to engage in effective learning and study
strategies.
Other Factors
The reason for this below average use of effective learning and study
strategies could be due to several factors. First, the relationship between
school systems and parents might not result in effective working alliances.
If parents are setting standards at home and encouraging their children to
do well in school, teachers and counselors might need to be aware of and
responsive to these standards. One way of doing this is for educators to
accommodate students' learning styles in the classroom by becoming more
flexible regarding instruction style (i.e., visual, auditory, hands on,
etc.). This would represent educators as not only setting educational
standards (like parents) but also encouraging the practice of recognizing
student differences. Adolescents can work toward increasing their study
habit only if they are made aware of their learning styles. This parent and
school alliance, as mentioned before, is important if the community,
itself, is going to be authoritative.
Another possible reason for the significant number of low learning
strategies among urban adolescents could be due to adolescents having
learning disabilities. Adolescents with learning disabilities may have
different abilities, strengths and weaknesses, and interests. Parents,
authoritative or not, need to communicate their needs with school personnel
and be decision makers when they participate in the Individual Education
Plan (IEP) process. Counselors and teachers need to have positive attitudes
and instructional priorities; they should also find out what skills an
adolescent will need to function adequately and implement a program for
preparing the child to develop these skills. The other problem is that many
adolescents with learning disabilities are undiagnosed. Many educators and
parents do not recognize the specific learning needs of urban adolescents
and therefore cannot design strategies to meet them. Thus, effective
academic programs need to mandate a higher awareness of adolescents'
learning aptitude variations and to supply educators and parents with
comprehensive knowledge of the structure of learning. With such
information, low learning and studying strategies of urban adolescents can
be changed, so that there is an increase of effective study habits.
A third reason for the limited number of adolescents engaging in effective
learning and studying strategies is the fact that many urban adolescents
may have additional life responsibilities and personal and emotional
challenges that compete with academic competence as a priority. For many
students, school-related activities are secondary or tertiary to work and
family responsibilities.
Recommendations and Limitations
On the basis of the findings and conclusions of this study, I have made
several recommendations for counselors, teachers, and parents of
adolescents. First, counselors, teachers, and parents should take advantage
of the information they have or can get regarding students' home and school
life and should use this information as a fostering tool in working with
urban adolescents. An example is for educators to vary their teaching
styles to accommodate different learning styles. Second, by having a wellrounded body of knowledge about the urban adolescent population, parents
can incorporate or maintain more effective parenting styles. This knowledge
would include parents being informed of their child's learning disability
and would also include how they can be involved in the IEP process. Third,
it is recommended that urban adolescents must first recognize their
strengths and weaknesses to improve their learning and study strategies.
Perceived parenting styles, alone, may not predict a students' academic
success, but an alliance between schools and parents can help these
adolescents determine what they need help with and what they can build on.
In reference to additional research, I made several recommendations. First,
more research needs to be done in the area of how parenting styles affect
urban adolescents' learning and studying strategies. As evidenced here, the
parenting style variable is not the only variable involved. Collaborative
partnership between the school system and parents as well as an awareness
of learning disabilities may also contribute. Second, there needs to be
more research that looks at the urban adolescent population and how these
students compare with other groups of students. Such data could then be
compared for possible relationships or lack thereof. This could help
determine the cultural influences on study habits of all adolescents.
Third, any research with an ESOL population that extends from this study
needs to take into account the data gathering instruments to be used. It is
recommended that to collect representative responses, the researcher needs
to use a "testing language" that is familiar to the sample population. This
would include simple translatable questions and items or different versions
of the instruments basted on the primary language of the participants.
There were two major limitations to this study. First, the sample did not
adequately represent all high school students because the sample was drawn
from only one high school population. This method of sampling was used
because I had difficulty getting permission from other high schools to
enter classrooms. Second, the reliability and validity of the PPAQ has not
been determined, and more studies need to be done to support the
reliability and validity of the LASSIHS. Third, the nature of the data
collection using self-report measures limits interpretations to what was
perceived by participants. Adolescents may report perceptions that do not
always accurately reflect actual parenting styles. Thus, future researchers
should be cautioned to attend to these limitations in designing studies
addressing this topic.
TABLE 1
Two-Way Chi-Square Analysis of Parenting and Strategy Variables
Legend for Chart:
A
B
C
D
E
F
G
-
Variable
Observed
Expected
Residual
chi[sup 2][a]
df
Asymp. Sig.
A
B
Parenting
Authoritarian
-7
C
-42.3
D
--35.3
E
78.677
--
F
2
--
G
.001
--
Authoritative
87
42.3
44.7
Permissive
33
42.3
-9.3
Total
127 126.99
-Strategy
37.622
2
.001
Low
62
42.3
19.7
Average
55
42.3
12.7
High
10
42.3
-32.3
Total
127 126.99
-* 0 cells (0%) have expected frequencies less
cell frequency is 42.3.
------------------------than 5. The minimum expected
REFERENCES
Balswick, J. O., & Macrides, C. (1975). Parental stimulus for adolescent
rebellion, Adolescence, 10(38). 53-56.
Becker, W. C. (1994). Consequences of different kinds of parental
discipline. In M. L. Hoffman & L. Hoffman (Eds.), Review of child
development (pp. 51-84). Chicago: University of Chicago Press.
Dubin, D. A., Darling, N., Steinberg, L., & Brown, B. B. (1993). Parenting
style and peer group membership among European-American adolescents.
Journal of Research on Adolescence, 3(1), 87-100.
Eldredge, J. L. (1990). Learning and Study Strategies Inventory--High
School Version (Lassi-HS). Journal of Reading, 34(2), 146-149.
Hein, C., & Lewko, J. H. (1994). Gender differences in factors related to
parenting style: A study of high performing science students, Journal of
Adolescent Research, 9(2), 262-281.
Mboya, M. M. (1995). A comparative analysis of the relationship between
parenting styles and self-concepts of Black and White high school students.
School Psychology International, 16, 19-27.
Olson, D. H. (1981). Marital and family therapy: A decade review. Journal
of Marriage and the Family, 42, 973-994.
Reich, C. A. (1991). Perceived parental closeness and control in relation
to adolescent general expectancy for success in life and school
achievement. Unpublished master's thesis, University of Maryland, College
Park, Maryland.
Salmon, J. L. (1996, November 24). Firm support for stricter upbringing.
The Washington Post, pp. B1, B5.S
Schaefer, E. S. (1965). Children's reports of parental behavior: An
inventory. Child Development, 36, 413-424.
Shucksmith, J., Hendry, L. B., & Glendinning, A. (1995). Models of
parenting: Implications for adolescent well-being within different types of
family contexts. Journal of Adolescence, 18, 253-270.
Streit, F. (1981). Differences among youthful criminal offenders based on
their perceptions of parental behavior. Adolescence, 16 (62), 409-413.
Streit, F. (1987). The Epac System manual for professionals (Vol. 1). New
Jersey: People Science.
Weinstein, C. S., & Palmer, D. R. (1990). Learning and Study Strategies
Inventory-High School Version. Clearwater. FL: H & H Publishing.
~~~~~~~~
By Marsha E. Boveja
Marsha E. Boveja is a doctoral student of counselor education in the
Department of Educational Psychology at the University of South Carolina,
Columbia. The study was conducted as a master's thesis in counseling at
Bowie State University, Bowie, Maryland. Correspondence regarding this
article should be sent to Marsha E. Boveja, Department of Educational
Psychology, University of South Carolina, Columbia, SC 29208 (e-mail:
mboveja@aol.com).
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Source: Journal of Multicultural Counseling & Development, Apr98, Vol. 26
Issue 2, p110, 10p.
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Title: Urban adolescents' personality and learning styles: Required
knowledge to develop effective...
Subject(s): ADOLESCENT psychology -- United States; MULTICULTURAL
education -- United States; PERSONALITY in adolescence -- United States;
LEARNING strategies -- United States
Source: Journal of Multicultural Counseling & Development, Apr98, Vol. 26
Issue 2, p120, 17p, 1 chart
Author(s): Peeke, Patricia A.; Steward, Robbie J.; et al
URBAN ADOLESCENTS' PERSONALITY AND LEARNING STYLES: REQUIRED KNOWLEDGE TO
DEVELOP EFFECTIVE INTERVENTIONS IN SCHOOLS
Researchers identified personality typologies (Myers-Briggs Type Indicator;
Myers, 1962) among urban African American high school Juniors and seniors
(n = 173). Introverted Sensing Thinking Judging (ISTJ) and the Introverted
Sensing Thinking Perceptive (ISTP) were the two most represented
typologies. Implications for school counselors, teachers, and teacher and
counselor educators are discussed.
Despite of the shared history of sociopolitical disenfranchisement,
discrimination, and limited access to resources and opportunities available
to others, the within-group diversity among African Americans in attitudes,
interpersonal styles, personal preferences, and worldview (Carter, 1995;
Steward, Gimenez, & Jackson, 1995; Steward, Jackson, & Bartell, 1993;
Steward, Jackson, & Jackson, 1990) is well documented in the literature. It
seems that despite of a common exposure to long-term persisting negative
systemic influences, individual differences do occur.
Because in addressing within-group diversity among African Americans
researchers tend to use samples of convenience, what is known about these
differences is somewhat limited to university student populations on
predominantly White campuses. However, studies do exist that address the
different experiences and outcomes among urban African American adolescents
(Barbarin, 1993; Connell, Spencer, & Aber, 1994; Dryfoos, 1990; Ward,
1995). Attention to this population is particularly important for educators
and counselors given that these children not only experience the general
challenges related to being African American in this country, but also the
day-to-day challenges related to the increased probability of exposure to
poverty, hopelessness, crime and violence, and life failure (Barbarin,
1993; Dryfoos, 1990). Nevertheless, even in this population, as in the
university population, evidence of within-group diversity has been noted.
Despite of the surrounding circumstances and environmental influences, some
urban adolescents report positive psychological adjustment, academic
competence, optimism about the future, and well-defined vocational and
career goals (Ward, 1995). The presence of within-group diversity among
adolescents working and playing in reasonably similar environments has also
been documented.
Perceptions of African Americans as a monolithic group persists despite
evidence indicating the contrary. Highlighting the fact of African
Americans' shared experiences and identifying clearly defined, unique
cultural norms have benefited many in this country. An emphasis on culturespecific knowledge has fueled efforts to heighten multicultural awareness
and knowledge of professionals in education and counseling. Disciplines,
such as multicultural counseling (Sue & Sue, 1990) and education (AACTE
Commission on Multicultural Education, 1973; Banks, 1987; Gay, 1991),
currently exist only in response to recommendations to develop culturespecific interventions that would more effectively meet the needs of a
population whose experience has been discounted or ignored. To correct the
outcome of past and ongoing injustices, practitioners and counselor and
teacher educators have altered practices in service delivery and curricula
development to accommodate identified cultural differences between African
Americans and middle-class Caucasian Americans. However, due to the
existence of within-group diversity, the shifts in intervention and
training may not always be appropriate for all members of this group.
Although some interventions may benefit some African Americans, due to the
unknown representation of differences within any given setting, these same
interventions may, in fact, impede the well-being and acquisition of
knowledge by others.
Attending to within-group diversity in the urban school system is
particularly important for counselors, given the significantly higher
representation of students' academic failure (Barbarin, 1993; Dryfoos,
1990). In this country, academic failure is closely associated with life
failure and the inability to become economically self-sufficient. These are
the outcomes that have become closely associated with urban America.
Identifying the representation of the range and faces of diversity that
exist within this population of students can assist in guiding educators
and counselors toward the development and implementation of an appropriate
number of effective interventions in counseling and teaching, toward a
better understanding of the reasons for the failure of current practices
and interventions, and toward a better understanding of within-group
conflicts that occur due to these differences. More specifically, the
identification of personality typologies among urban African Americans, as
measured by the Myers-Briggs Type Indicator (MBTI; Myers, 1962), would
provide counselors and teachers with information about personality
qualities, with information related to students' career and vocational
interests, and with assistance in the identification of the most effective
strategies or combination of strategies for counseling and teaching.
Targeting upper-class high school students can assist university, college,
vocational school, and work setting staff to best understand and
accommodate the needs and strengths of individuals from the predominantly
African American urban community. The purpose of this study is to examine
the representation of the within-group diversity among an urban high school
population.
METHOD
Research Setting
Community High School is located in a predominantly African American
community consisting of Just over 40,000 people on the outskirts of a large
urban area in the Midwest. The community is generally of lower
socioeconomic status, with a mean household income of $8,200 annually.
Nearly 40% of the population lives at or below the poverty level (based on
family size and income). The unemployment rate is 27%, and 63% of the
households below the poverty level are headed by women and include children
under the age of 18 years. The racial composition of the population
surrounding the high school includes nearly 85% African Americans, 13%
Caucasian Americans, and 2% individuals of other racial or ethnic
identification.
Community High School serves approximately 1,250 students. Of the student
body, 97% is African American. The school's staff consists of 65 teachers
and four administrators, most of whom are Caucasian American. The high
school's mean grade point average (GPA) is 1.75. The school's daily
absenteeism rate is 15%, and the overall attrition rate is 20%.
Participants
One hundred and seventy-three African American high school juniors and
seniors participated in the study. Seventy-two (42%) were seniors and 101
(58%) were Juniors; 98 (57%) were female students and 74 (43%) were male
students. Each of the possible 16 MBTI personality profiles was found. Of
the participants, 97% identified attitudes reflected within the
internalization status of racial identity. The mean age was 17 years.
Procedure
After reading a brief description of the study, researchers asked
participants under the age of 18 to take consent forms home to obtain
parents' signature. In addition, all students were asked to provide written
consent by completing and signing an information sheet at the time of
packet completion.
Data collection occurred during normal school hours on 2 consecutive days.
Research assistants and teachers instructed in the methods of administering
the instruments provided each participant with a packet containing a
consent form, a demographic form, the MBTI (Myers, 1962) and the Black
Racial Identity Attitude Scale (RIAS-B; Helms, 1990; Helms & Parham, 1985).
The RIAS-B was included in this study to best understand the qualities of
the population studied. The demographic form included items that addressed
participants' age, sex, and grade.
During packet completion, participants were separated from those students
who did not participate due to grade level, lack of parental consent, or
unwillingness to participate. Researchers read aloud all directions and
answered individual questions. In return for participation, students were
provided with a personality profile and career assessment packet. The
career profile and informational packet consisted of an explanation and
interpretation of the MBTI and its application to identified career
interests. As a result, questionnaire packets were not completed with total
anonymity. Although names remained on the instruments, measures were
secured in a locked file to ensure confidentiality.
Measures
The MBTI (Myers, 1962) is a 94-item forced-choice, self-report inventory
that measures personality variables proposed by Carl Jung's (1971) theory
of conscious psychological type. The MBTI classifies individuals along four
theoretically independent dimensions. The measure consists of four bipolar
scales: Extroversion-Introversion (E-I), Sensation-Intuition (S-N),
Thinking-Feeling (T-F), and Judging-Perceiving (J-P). The dominant process
in each pair is the one on which a person relies the most. The E-I scale
measures interest in things and people or concepts and ideas; the S-N scale
measures tendencies to perceive through direct sensory processes or
indirectly influence through the unconscious; the T-F scale involves the
style of information organization and the preferred mode of forming
judgments; and the J-P scale reflects the dominant preference for dealing
with the surrounding environment. Combinations of the four preferences
exhibited by each respondent determine the 16 possible personality type
combinations. Each type represents qualitatively different patterns of
organization of the basic Jungian variables and defines a unique set of
characteristics and tendencies in behaviors.
Reliability estimates for the individual personality preferences obtained
from the MBTI are .70 to .81 (E-I), .82 to .92 (S-N), .66 to .90 (T-F), and
.76 to .84 (J-P). Stability in type scores was found to exhibit relatively
stable reliabilities over an 8-week period including .69 to .83 (Carlyn,
1975); .86 to .89 (except J-P; Steele & Kelly, 1976). In addition, test-
retest reliabilities of .78 to .87 were found in a duplicate study 2 years
later. The stability of the MBTI personality type scores has been
demonstrated with African American students at a historically Black
university (Levy, Murphy, & Carlson, 1972). The test-retest reliability
estimates for male students were found to range from .69 to .80, and for
female students, from .78 to .83. Therefore, the MBTI has been declared a
reliable instrument capable of assessing specific personality traits of
African American students.
The RIAS-B (Helms, 1990; Helms & Parham, 1985; Parham & Helms, 1981) was
developed based on Cross's (1978) assumption that African American
individuals, as they move from a position of degrading their racial
identity to feeling secure with their racial identity, progress through
four identifiable stages: Pre-encounter, Encounter, Immersion, and
Internalization. The RIAS-B assesses African American persons' attitudes
about themselves. The short form of the RIAS-B consists of 30 attitude
statements with a corresponding 5-point Likerttype response format
(strongly agree to strongly disagree). The RIASB is scored by averaging
ratings for the appropriately keyed items assigned to each of four
subscales. Averaged subscale scores range from 1 to 5, with higher scores
indicating greater endorsement of the attitudes represented by each
subscale. The original version was derived from the responses of 54 college
students attending a predominantly White midwestern university. Additional
normative samples were drawn from both predominantly White and historically
Black universities (Pyrant & Yanico, 1991). Internal consistency
reliability estimates for the RIAS-B are reported for each stage of racial
identity: Pre-encounter .69, Encounter .50, Immersion .67, and
Internalization .79. Cronbach's alpha was used again to compute respective
reliability coefficients: Pre-encounter, .76; Encounter, .51; Immersion,
.69; and Internalization, .80 (Helms & Parham, 1985).
RESULTS
Table 1 presents the sample's MBTI profile results. The types ISTJ and ISTP
were the two most represented typologies: 17.3% and 16.8% of the sample,
respectively. The ESTJ personality type accounted for 14.5% of the sample.
These findings are consistent with Kaufman, Kaufman, and McLean's (1993)
results in which the ESTJ profile was endorsed by 14.1% of the African
American male college students and 13.08% of the female college students.
Of the participants, 56% indicated that their predominant personality and
learning style preference included both a sensing (S) and thinking (T)
component. The prevalence of this typology has been noted in previous
research findings (Kaufman et al., 1993; Levy et al., 1972).
The MBTI personality profiles least represented in the sample were ENFJ (n
= 2), INTJ (n = 2), and INFJ (n = 3). A preference for the intuitive (N)
characteristic (i.e., learning through intuition and imagination rather
than facts) was endorsed by less than 19% of the students. These findings
are also consistent with previous research results in which less than 19%
of the African American participants endorsed items consistent with the
intuitive preference.
In summary, 57% indicated a preference for the style of an introversion (I)
rather than extroversion (E); 66.6% of the sample indicated a preference
for using thinking (T) and logic over feeling (F) and emotions in decision
making; and, 52.1% of the sample indicated a stronger tendency toward an
organized and predictable judging (J) versus a more spontaneous and
carefree perceiving (P) orientation. Eighty-one percent of the sample
endorsed the characteristics for sensing (S) over intuition (N). These
results support previous findings that indicate African Americans'
preference for concrete and logical rather than an intuitive and abstract
integrative process (Kaufman et al., 1993; Levy et al., 1972).
Overview of Descriptions of the Most Represented Personality Typologies
The Sensing-Thinking personality type. The Sensing-Thinking (ST)
individual, in general, tends to be present oriented. The individual relies
on thinking to make decisions and is concerned more about logical
consequences than personal feelings. For the ST individual, perceptions of
the world tend to be based on things tangible to the senses rather than on
abstract ideas, theories, or models. Members of this typology feel most
comfortable in situations wherein personal ideas, plans, and decisions are
based on solid facts verified by logic; facts are the only basis for action
(Hammer & Macdaid, 1992).
The individual with ST preferences tends to be self-sufficient and desires
emotional control and treats feelings objectively. Control in self and
others is highly respected, and individuals in this type are inclined to be
somewhat impatient with ambiguity and uncomfortable with disorder, chaos,
and the unfamiliar. For those with ST preferences, consistency is preferred
to variation, and seeking and needing early closure to questions or
problems are activities of priority. There is great respect for rules, and
these individuals take comfort in having and following procedures (Hammer &
Macdaid, 1992).
The Introverted ST individual is serious, disciplined, reserved, and
thorough and has a capacity for facts and details. Planning is a must and
decision making is taken seriously by them. The provision of structure with
well-defined rules and outcomes is critical. This individual enjoys
quantifying information, measuring things, working with data, and listing
facts. As an employee, this individual will work for long periods of time,
to the extent that the process and outcome make sense. Practical judgment
and memory for detail make this individual conservative and consistent
(Hammer & Macdaid, 1992).
The Extroverted ST is assertive, confident, and energetic. This individual
likes to take charge of others and enjoys getting things organized and
accomplished. Being action-oriented, the individual recognizes what is
necessary and works with speed and economy of effort to complete a task.
Work that results in immediate visible and tangible results is that which
is most enjoyed. Solving problems through trial and error and application
and adaptation of past experiences would be considered to be the most
stimulating activities in play and work. The individual has a natural head
for business and organization (Hammer & Macdaid, 1992).
Overview of Descriptions of the Least Represented Personality Typologies
The Intuitive type. The individual whose responses on the MBTI indicated a
preference for Intuitive typology, as contrasted to the Sensing, tends to
have the following characteristics: interested in ideas; focuses attention
on the future and what can be; interested in possibilities beyond what is
present, obvious or known; prefers to generate ideas rather than be
responsible for putting them into action; is comfortable doing things in
their own way; is patient with complicated situations; trusts inspirations,
visions, and imagination; prefers elaboration, metaphoric expression, and
poetry; works continuously when interested in what they are doing; wants to
achieve important new solutions to long range problems; enjoys learning new
skills more than using them; and works in bursts of energy, powered by
enthusiasm with slack periods in between (Hammer & Maclaid, 1992).
The Extroverted Intuitive Thinker (ENT) is highly verbal and believes that
words are power and may use them as weapons. This individual takes much
satisfaction in conversation and enjoys debating an issue and scoring
points. There is a need for intellectual challenge and a tendency to
dislike routine, which typically results in loss of interest. These
individuals are most comfortable and effective in Jobs that allow tasks
related to planning, conceptualizing, and organizing with someone else
being responsible for the details. There is a preference for finding
solutions to problems rather than carrying out the solutions. The
Extroverted Intuitive-Thinker desires power and is competitive and needs to
believe that a personal impact has been made on what had been accomplished.
The individual is unwilling to accept failure and may be overly critical of
self and others (Hammer & Macdaid, 1992).
On the other hand, the student who functions as an Introverted IntuitiveThinker (INT) is the most individualistic and most independent of all the
types. This individual enjoys dealing with abstract theories and ideas and
may be relatively indifferent to the material world. There is a preference
for being alone and adhering to a strong sense of principle. Furthermore,
there may be a tendency to ignore the views and feelings of those who do
not agree, and such individuals may seem to be detached. This individual
would be good at scientific research, math, and other abstract or symbolic
disciplines. Displaying the characteristics that are associated with this
typology, these individuals are quiet, reserved, and skeptical (Hammer &
Macdaid, 1992).
The introverted intuitive-feeling (INF) person is energetic, enthusiastic,
and imaginative. This individual is flexible and fluctuates in mood from
one extreme to another. These individuals have a high tolerance for
ambiguity and different belief systems. Concerns about the future and the
problems of human welfare predominate in the individual's thinking. There
is a tendency toward idealism, and often individuals object to things as
they are and want to bring about significant changes. Such individuals also
tend to believe in entitlements of rights for all individuals and value
engagement in activities that attend to issues such as these.
Overview of Descriptions of Most Represented Learning Style
The Sensing-Thinking Learning Style is characterized by certain attributes:
works with and remembers facts and details well; speaks and writes directly
to the point; approaches tasks in an organized and sequential manner;
adapts to existing procedures and guidelines; is concerned about utility
and efficiency; is goal oriented; focuses on immediate, tangible outcomes;
knows what needs to be done and follows through; and is concerned about
accuracy (Silver & Hanson, 1982).
These individuals tend to learn by directly experiencing through the five
senses what is being learned; by putting what has been learned into
immediate use or practice; by seeing tangible results from efforts; by
practicing what has been learned; by following directions one step at a
time; by learning in an organized, task-oriented environment; by studying
about practical things that have immediate use; by responding to questions
for which there are correct answers rather than open-ended questions
requiring opinions; by participating in firsthand experiences rather than
reading about them or being told about them; by being active rather than
passive; by having their work checked immediately upon completion to
determine if it has been done correctly; and by knowing exactly what is
expected, how well the task is to be done, and why (Silver & Hanson, 1986).
This type of learner learns best from the combination of techniques: drill,
programmed instruction, demonstration, practice, mastery learning,
convergent thinking tasks, and direct, actual experiences. Motivating
activities include simple, repetitive learning games, concrete exploration
and manipulation, programmed texts, workbooks, making real-life models,
dramatizing important events, opportunities to demonstrate what is known,
and assignments that have clearly defined conclusions. Such students tend
to like doing things that have immediate practical use: being acknowledged
for thoroughness and detail; praise for prompt and complete work; and
immediate feedback such as rewards, privileges, and so forth. (Silver &
Hanson, 1986).
Overview of Descriptions of the Least Represented Learning Styles
Two learning styles were indicated as being the least represented among
members in this sample: the Intuitive-Feeling and Intuitive-Thinking
styles. Intuitive-Feeling individuals tend to be good at interpreting facts
and details to see the broader picture; to be able to express ideas in new
and unusual ways; to approach tasks in a variety of ways or in an
exploratory manner; to adapt to new situations and procedures quickly; to
be concerned with beauty, symmetry and form; to be process oriented and
interested in the future and solving problems of human welfare; to not be
confined by convention; and to be concerned with creativity (Silver &
Hanson, 1982).
On the other hand, the Intuitive Thinker tends to take time to plan and
contemplate consequences of actions; to organize and synthesize
information; to weigh the evidence and risk Judgment based on logic; to
learn vicariously through books and other symbolic forms; to be comfortable
with activities requiring logical thinking; to be able to persuade people
through logical analysis; to retain and recall large amounts of knowledge
and information; and to be interested in Ideas, theories, or concepts
(Silver & Hanson, 1982).
Students with the Intuitive-Feeling temperament learn best from the use of
certain instruction techniques: creative problem-solving activities;
fantasizing; creative writing; creative and artistic activities; open-ended
discussions of personal and social values; self-discovery; free
association; metaphorical thinking; and activities that enlighten and
enhance--myths, human achievement dramas, and so forth. Certain activities
motivate these individuals: solving problems that require imagination and
creativity; solving issues of personal and social importance; expressing
themselves through one or more of the arts; expounding on how to improve
things; talking about meanings, values, and relationships; engaging in
activities that lead to a broader understanding of study material; solving
open-ended and challenging problems or questions; and searching for beauty,
symmetry, and aesthetics. Individuals Intuitive Feelers also have other
preferences: opportunities for contemplation, being allowed to learn
through discovery, opportunity to plan and pursue their own interests,
recognition for personal insights and discoveries, and praise for unusual
solutions to difficult problems (Silver & Hanson, 1986).
Intuitive Thinkers learn best from the use of the following instruction
techniques: Socratic method, problem-solving techniques, systematic
planning, lectures, reading, logical discussions and debates, discovery
through use of the scientific method, games of strategy, projects of
personal interest, and inductive reasoning. Motivating activities include
independent research projects; reading on a topic of personal interest;
self-directed activities; puzzles, math problems, and logic problems;
debating; open-ended questions; planning their own learning activities; and
collecting and interpreting data. Personal preferences include time to plan
and organize work, working independently or with other Intuitive-Thinking
types, developing opportunities to present personal projects or reports,
and working with ideas and things that challenge (Silver & Hanson, 1986).
DISCUSSION
First, the results of this study support the initial hypothesis of withingroup diversity in personality and learning styles among urban African
American adolescents. Even though there tended to be an almost universal
shared sense of racial identity within this well-segregated urban district,
every possible personality or learning typology was represented. Such
findings indicate that even among members who share racial status,
developmental stage, and national and community history may simultaneously
share some attitudes and maintain unique individual ways of being.
Researchers and practitioners should no longer assume points of similarity
or points of distinction among racial or ethnic group members. In
conjunction with those of previous studies, these findings clearly
highlight the importance of assessing and uniquely attending to individual
differences in service delivery and teaching.
Second, results of this study portray the complexity of providing services
to urban adolescent populations and teaching them. Given the limited
resources and personnel as well as student and parent populations that are
struggling with problems of survival and living, professionals are faced
with the challenge of having to effectively manage the heightened withingroup diversity using traditional strategies and models for interventions.
Out of desperation, even teachers and counselors in more progressive urban
school settings can find themselves shifting to more innovative strategies.
In addition, such shifts can often occur first, without assessing student
populations; second, without clearly defining criteria as indicators of
intervention effectiveness; or third, without evaluating with whom the
intervention is effective or ineffective. Attending to each of these is
essential in effective program development whether in counseling or
academic instruction. With even the possibility of the existence of a wide
range of diversity, it becomes apparent that any well-planned theory-based
intervention should be effective. However, it is also a given that the same
well-planned, theory-based intervention will be ineffective with others in
the same student population and in the same school setting and classroom.
One simple example of a unidimensional shift is the avoidance of
traditional, mainstream practices in individual therapy that tends to focus
more on insight, reflection of content and feelings, internal locus of
control and responsibility, and ideas and concepts (Sue & Sue, 1990).
Avoiding these traditional techniques with most of these adolescents in
this setting (i.e., Sensing-Thinking) would be quite appropriate; however,
for the more Intuitive-Feeling and other adolescents, doing so may be quite
inappropriate. Although the results suggest that psychoeducational
interventions might be most appropriate for Sensing-Thinking adolescents
who made up a critical representation of this sample, an assumption that
this might be a standard form of service delivery would be quite erroneous.
Providing multiple alternatives of service delivery and therapeutic
interventions and developing a student population of informed consumers
would certainly increase the probability of students' unique preferences
being matched with the most effective intervention.
Another example of ineffective unidimensional shifting is changing the more
traditional classroom structural arrangement of individual and separated
desks in a row to that of resource tables, with interest centers, and the
absence of any permanent arrangement. However, these findings suggest that
many students in this setting would benefit most from the more traditional
classroom organization.
Attending to differences within one classroom can become quite complicated
for the classroom teacher. Teachers are confronted daily with the challenge
not only of effectively managing conflicts that arise among students due to
differences, but also of instructing all students in a manner in which they
can most easily learn. Findings may support the notion of the development
of criteria for students' contracts for grades. For example, teachers might
provide students with options that represent each of the possible preferred
learning activities for each letter grade. Classrooms might be structured
in a manner that respects this within-group diversity and provides
opportunities for and support of each individual's learning preference
toward task completion. Having a built-in means of identifying students'
success in accomplishing the goals for which they initially contract, this
model also fosters strategy evaluation. Those students who are not
successful in their first contract selection might select another option
the next grading period.
Although some schools may choose to group students by learning style to
more easily and more systematically attend to differences in learning, we
note one critical limitation in doing so. Given that learning styles may
result from earlier learning opportunities, experiences, and exposure to
various parenting styles, preferences for learning can remain stable to be
expanded to include a number of preferences. Urban students' increased
awareness of other options and exposure to others as they pursue their
preferences allow opportunities for observations that might prove to be
useful in addressing future life challenges inside and outside school.
Counselors and teachers may work with students to effectively move them
toward task completion when they are faced with activities that do not fit
their primary learning preference.
Third, extensive diversity in already high pressure settings can exacerbate
otherwise innocuous events, tensions, and interpersonal conflicts.
Relationships among students may be challenged due to the very stylistic
differences that were found in this study. Relationships between teachers
and students might also become problematic due to students' inability to
accommodate teachers' instruction style and teachers' inability or
unwillingness to accommodate students' preferences. Counselors might work
with teachers in developing a better understanding of how to mediate
interpersonal issues related to conflicts due to personality differences
and teaching and learning conflicts. Counselors might provide in-service
training for teachers' increased understanding of the assets and
liabilities of each of the teaching styles.
For example, although the Sensitive-Thinking teacher might be most
effective for a large percentage of the students represented in this study,
there are some concerns that must be monitored with this teacher. For
example, this teacher may tend to overlook the needs of Individual learners
in the push for content or skills mastery. Detail might be overemphasized
to the point that students become bored or discouraged. Concerns with rules
could lead to others' perception of the individual as rigid or unfeeling;
an emphasis on order may result in such regimentation that students get
"turned off." In addition, as the giver of directions, the individual may
suppress any natural leadership tendencies among students. Consequently,
even though most students may feel comfortable with this teacher's style,
the learning of others might be impeded in the teacher's classroom. (Silver
& Hanson, 1982)
Fourth, findings can guide the investigation of career and vocational
decision making among this student population. There are a number of
resources that identify a plethora of professions and Jobs associated with
each of the Jungian typologies (Hammer & Macdaid, 1992). However, the
authors caution against such use of typologies in this setting. In urban
communities, adolescents' exposure to a wide range of occupations may be
limited and opportunities to engage in a wider range of learning
experiences nonexistent. Consequently, typologies might serve only as an
indicator of where counselors might begin in the process of increasing
adolescents' self-awareness of values, interests, and identification of
typology-associated professions. Additional activities that allow students
to experience and become knowledgeable about options represented within
other students' typologies would provide them with assistance in seeing a
more comprehensive vocational map of the full range of possibilities in the
world of work.
The implementation of the above recommendations for intervention requires
professionals who are willing and able to become both cognitively and
behaviorally flexible in fulfilling their professional roles (Steward,
1993). It requires creative and responsive individuals whose primary goal
is the psychological well-being and academic and life success of all
students. Counselor and teacher education academic programs must admit only
students who are open to understanding the importance of accommodating
learning differences, and willing to accept encouragement, guidance, and
challenge to effectively do so. Traditional practice of supporting
counseling and teaching interns' adherence to and competence in only one
theoretical orientation or teaching style might be expanded to requiring
trainees to adopt an eclectic perspective and develop minimal competence in
several mainstream orientations. School administrators must have the vision
to develop new cultural norms and taboo behaviors associated with the roles
of teacher and counselor by (a) providing in-service training to guide,
assist, and evaluate staff as they move toward implementing effective
building and districtwide interventions (Hopkins, 1990; Leithwood, 1990;
Sparks & Loucks-Horsley, 1989); and (b) having the courage to face the
challenge of relocating or removing staff members who are not willing to
accommodate the needs of the population being served (Jones, 1997).
Educational reform will require not only more than marginal individual
changes but also the implementation of strategies that result from
rethinking the current roles and practices of counselors and teachers in
the school and implementing new ways of being (Elmore, 1997).
LIMITATIONS OF THE STUDY
Although we believe that these findings provide a significant contribution
to the literature addressing the urban adolescent population, there are
some limitations. First, data collection occurred only at one site in one
geographical location that might limit the generalizability of the results.
Second, the sample included only upperclass high school students. Given the
exceptionally high attrition rate that had been noted among 9th graders
(45% to 55%), the sample might have represented the learning and
personality styles of only those students who persisted up to the 11th and
12th grades. Those typologies that were least represented in this sample,
might have had greater representation among those students who had changed
schools or who had dropped out from this particular school. Future research
is certainly warranted, to examine the effectiveness of recommendations we
have suggested.
TABLE 1
Myers-Briggs Type Indicator Profile Results (N = 173)
Type
N
ISTJ
30
Percent
17.3
ISFJ
13
7.5
INFJ
3
1.7
INTJ
2
1.2
ISTP
29
16.8
ISFP
12
6.9
INFP
4
2.3
INTP
6
3.5
ESTP
13
7.5
ESFP
9
5.2
ENFP
6
3.5
ENTP
4
2.3
ESTJ
25
14.5
ESFJ
9
5.2
ENFJ
2
1.2
ENTJ
6
3.5
Note. N = Introversion; E = Extroversion; S = Sensation; I = Intuition; T =
Thinking; F = Feeling; J = Judging; P = Perceiving.
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~~~~~~~~
By Patricia A. Peeke, Robbie J. Steward and Judy A. Ruddock
Patricia A. Peeke is a counseling psychologist living in Bethlehem,
Pennsylvania. Robbie J. Steward is an associate professor in the Department
of Counseling and Education at Michigan State University, East Lansing.
Judy A. Ruddock is a high school teacher at Northwestern High School in
Flint, Michigan. Correspondence regarding this article should be sent to
Robbie J. Steward, Michigan State University, 436 Erickson Hall, Fast
Lansing, MI 48824 (e-mail: devine@msu.edu).
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Source: Journal of Multicultural Counseling & Development, Apr98, Vol. 26
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Title: An interview with Rita Dunn about learning styles.
Subject(s): DUNN, Rita -- Interviews; LEARNING strategies
Source: Clearing House, Jan/Feb98, Vol. 71 Issue 3, p141, 5p
Author(s): Shaughnessy, Michael F.
AN INTERVIEW WITH RITA DUNN ABOUT LEARNING STYLES
What are the main components of a person's learning style? A person's
learning style is the way that he or she concentrates on, processes,
internalizes, and remembers new and difficult academic information or
skills. Styles often vary with age, achievement level, culture, global
versus analytic processing preference, and gender.
Dunn and Dunn (1992, 1993) describe learning style in terms of individual
reactions to twenty-three elements in five basic strands that include each
person's environmental, emotional, sociological, physiological, and
psychological processing preferences.
Do we learn differently or do we process information differently? Human
beings process information differently from each other, but information
processing is only one of twenty-three elements in the Dunn and Dunn
Learning Style Model.
How do we know that students achieve more when their teachers teach to the
students' learning styles? A meta-analysis of forty-two experimental
studies conducted with the Dunn and Dunn model between 1980 and 1990 by
thirteen different institutions of higher education revealed that students
whose characteristics were accommodated by educational interventions
responsive to their learning styles could be expected to achieve 75 percent
of a standard deviation higher than students whose styles were not
accommodated (Dunn et al. 1995).
In addition, practitioners throughout the United States have reported
statistically higher test scores and/or grade point averages for students
whose teachers changed from traditional teaching to learning-style teaching
at all levels--elementary, secondary, and college. Improved achievement was
often apparent after only six weeks of learning-style instruction. After
one year, teachers reported significantly higher standardized achievement
and aptitude test scores for students who had not scored well previously.
For example, prior to using learning styles, only 25 percent of the
Frontier, New York, school district's special education high school
students passed the required local examinations and state competency tests
to receive diplomas. In the district's first year of its learning styles
program (1987-88) that number increased to 66 percent. During the second
year (1988-89) 91 percent of the district's special education population
were successful; in the third year (1989-1990) the results remained
constant at 90 percent--with a greater ratio of "handicapped" students
passing state competency exams than regular education students (Brunner and
Majewski 1990).
Two North Carolina elementary principals published similarly startling
gains with the same learning-styles program. One principal brought a K-6
school, whose students were from poor, minority-group families, that had
scored in the 30th percentile on the California Achievement Tests up to the
83rd percentile in a three-year period by responding to students' learning
styles (Andrews 1990). The other principal taught highly tactual learning
disabled (LD) elementary school students with hands-on resources and
allowed them to sit informally in subdued lighting. Based on their
learning-style analyses, the children studied alone, with a classmate or
two, or with their teacher. Within four months, those LD youngsters showed
four months' gain on a standardized achievement test--better than they had
previously done and as well as normally achieving children (Stone 1992).
Finally, a U.S. Department of Education four-year investigation that
included on-site visits, interviews, observations, and examinations of
national test data concluded that attending to learning styles was one of
the few strategies that had had a positive impact on the achievement of
special education students throughout the nation (Alberg et al. 1992).
The gains described here were made by using the Dunn and Dunn model, which
has been researched at St. John's University and more than 110 other
colleges and universities since 1972.
Why should we test for children's learning styles? Teachers cannot identify
students' learning styles accurately without an instrument (Beaty 1986).
Some characteristics are not observable, even to the experienced educator.
In addition, teachers often misinterpret students' behaviors and
misunderstand their symptoms. For example, it is difficult to determine
whether a youngster's hyperactivity is due to a need for mobility, informal
seating, kinesthetic resources, or "breaks," or to nonconformity or a lack
of discipline.
Only a reliable and valid instrument can provide reliable and valid
information, and only a comprehensive instrument can diagnose the many
learning-style traits that influence individuals. Teachers who use
instruments to identify only one or two variables on a bipolar continuum
restrict their ability to prescribe for the many elements other than the
one or two they identified. Learning style is a multidimensional construct;
many variables have an impact on each other and produce unique patterns.
Those patterns suggest exactly how each person is likely to concentrate,
process, internalize, and retain new and difficult information. The
patterns indicate which reading or math method is most likely to be
effective with each student.
Only three comprehensive models exist, and each has a related instrument
designed to reveal individuals' styles based on the traits examined by that
model. During the past two decades, the most frequently used instrument in
experimental research on learning styles, and the one with the highest
reliability and validity, is the Dunn, Dunn, and Price Learning Style
Inventory (LSI), with its subtests for students in grades 3-12 and the
Productivity Environmental Preference Survey for college students and
adults.
Tell us about your test for identifying learning styles. The Learning Style
Inventory (grades 3-12) was developed through content and factor analysis
and is one of the three comprehensive approaches to identifying students'
learning styles. Different grade-level forms permit analysis of the
specific conditions under which students prefer to learn. This easy-toadminister and interpret inventory uses more than one hundred dichotomous
items (e.g., "When I really have a lot of studying to do, I like to work
alone" and "I enjoy being with friends when I study") that are rated on a
five-point Likert scale and can be completed in approximately thirty to
forty minutes.
In an analysis of the conceptualizations of learning style and the
psychometric standards of nine different instruments that measure learningstyle preference, the LSI was rated as having good or better reliability
and validity (Curry 1987).
A series of age-appropriate storybooks is available from the Center for the
Study of Learning and Teaching Styles at St. John's University for primary,
elementary, middle school, and secondary students and adults to clarify the
concept of style and to demonstrate that there is no bad or better style.
Most people can learn anything when they know how to capitalize on their
learning-style strengths.
Describe what the LSI reveals. The LSI assesses individual preferences in
the following areas: (a) immediate environment (sound, light, temperature,
and seating design); (b) emotionality (motivation, persistence,
responsibility/ conformity, and need for internal or external structure);
(c) sociological (learning alone, in a pair, as part of a small group or
team, with peers, or with an authoritative or collegial adult; also, in a
variety of ways or in a consistent pattern); (d) physiological (auditory,
visual, tactual, and/or kinesthetic perceptual preferences; food or liquid
intake needs; time-of-day energy levels; mobility needs); and (e)
indications of global or analytic processing inclinations (through
correlation with sound, light, design, persistence, peer-orientation, and
intake scores).
How does the LSI affect learning? The LSI does the following:
Permits students to identify how they prefer to learn and also indicates
the degree to which their responses are consistent
Suggests a basis for redesigning the classroom environment to complement
students' diverse styles
Describes the arrangements in which each student is likely to learn most
effectively (e.g. alone, in a pair, with two or more classmates, with a
teacher, or, depending on the task, with students with similar interests or
talents; it also describes whether all or none of those combinations is
acceptable for a particular student)
Explains which students should be given options and alternatives and which
students need direction and high structure
Sequences the perceptual strengths through which individuals should begin
studying--and then reinforce--new and difficult information; it explains
how each student should study and do homework (Homework Disc 1995)
Indicates the methods through which individuals are most likely to achieve
(e.g., contracts, programmed learning, multisensory resources, tactual
manipulatives, kinesthetic games, or any combination of these)
Provides information concerning which children are conforming and which are
nonconforming and explains how to work with both types
Pinpoints the best time of day for each student to be scheduled for
difficult subjects (thus, it shows how to group students for instruction
based on their learning-style energy-highs)
Identifies those students for whom movement or snacks, while the students
are learning, may accelerate learning
Suggests those students for whom analytic versus global approaches are
likely to be important
How can schools order the LSI? Discuss purchasing and cost possibilities
with Price Systems in Lawrence, Kansas. When ordering the LSI, stipulate
the grade level and total number of students you plan to test; the cost
decreases when more students are tested. The LSI is available on IBM and
Apple self-scoring discs; if you plan to test three hundred persons or
more, the disc may be considerably less expensive.
How does learning style influence homework? St. John's University's Center
for the Study of Learning and Teaching Styles developed IBM and Apple
software packages that translate LSI scores into prescriptions for how
students should study and do their homework (Homework Disc 1995).
Is it possible to identify the styles of children in grades K-2? For young
children in K-2, use the Learning Style Inventory: Primary Version (LSI:P)
(Perrin 1982), which is obtainable from St. John's University's Center for
the Study of Learning and Teaching Styles. The LSI:P is a pictorial
assessment of young children's learning styles and is accompanied by a
research manual that explains how to administer it. Although there are
decided advantages to having teachers administer the test on an individual
basis--because of all the information each child reveals--the assessment's
questions are written so that an intelligent parent can elicit the same
information and assist the teacher in compiling the hand-scorable data.
How do teachers adapt for each child's style? Teachers do not need to adapt
to each child's style. Rather, they need to do the following:
Understand the concept, its related practices, and its implementation
strategies
Explain learning styles to their students so that the youngsters understand
that there is no such thing as either a "good" or a "bad" style.
Prepare students for taking the LSI (Price Systems interprets the students'
print-outs, and the Homework Disc provides their prescriptions)
Have alternative instructional methods and resources to teach the identical
information differently to students with diverse learning styles
St. John's University has many such resources at varied grade levels and
subjects. They can be adapted or paralleled for a particular classroom. In
addition, many of our books provide directions for developing resources
(Dunn and Dunn 1992, 1993; Dunn, Dunn, and Perrin 1994). We also teach
students to create their own instructional resources.
How do learning-style teachers differ from conventional teachers? Unlike
traditional teachers who teach an entire class in the same way with the
same methods (or the "brain-based" practices where every student is taught
nontraditionally), learning-style teachers actually teach different
children differently. Teachers do two important things: Using the resources
and methods that best match each child, they teach students (1) to
recognize and rely on their personal learning-style strengths and (2) to
teach themselves and each other by using those strengths.
What is a learning-style school like, and how does it differ from
conventional schools? Although students in the same class may be mastering
the same information and skills at the same time, in learning-style schools
they work in those sections of the classroom that best respond to their
environmental and physiological styles. A variety of tactual and
kinesthetic resources are available for mastering the curriculum, but
children work only with those resources that best complement their own
processing, perceptual, emotional, and sociological styles--and students
often will have made the materials they use!
It would be rare to see whole classes engaged in either teacher-directed
instruction or cooperative learning when the students are being introduced
to new and difficult material. Instead, children begin learning alone, with
a classmate or two, in a small, cooperative or competitive group, or with
their teacher through their primary perceptual strengths for the first ten
to fifteen minutes. They then reinforce the new information with a
different resource through their secondary strengths. Students may vary
their choice of resources but are encouraged to begin learning through
their strengths whenever the academic material is complex or difficult for
them.
In learning-style classes, students' strengths are identified and then
transferred to a computer software package, the Homework Disc (1995). That
package generates a personalized, printed prescription for each child that
describes how to study and concentrate through his or her strengths.
Gradually, each child learns how to teach him- or herself or how to work
with a classmate who learns similarly. Children study, learn, complete inclass assignments, and do their homework through their strengths--instead
of as the teacher happens to teach.
What happens when teachers teach in a different style from the way in which
students learn? When students are unable to learn with complementary
resources--such as textbooks, films, or videotapes for visual preferents;
manipulatives for tactual preferents; tapes or lectures for auditory
preferents; or large floor games for kinesthetic preferents--they do not
achieve what they are capable of achieving. Research reveals that the
closer the match between students' learning styles and their teachers'
teaching styles, the higher the grade point average (Dunn et al. 1995).
How do gifted children learn? Although all gifted students do not have the
same style, their styles differ significantly from those of underachievers.
When comparing the learning styles and multiple intelligences of gifted and
talented adolescents in nine different cultures, we found that, regardless
of culture, adolescents gifted in a particular domain--athletics, dance,
leadership, literature, mathematics, and music--had essentially similar
learning styles. Surprisingly, the gifted in each intelligence domain had
essentially similar styles--but those were different from the styles of
other gifted groups and from the styles of the nongifted (Milgram, Dunn,
and Price 1993).
Are there perceptual differences between the gifted and nongifted students?
Although gifted students prefer kinesthetic (experiential/active) and
tactual (hands-on) instruction, many also are able to learn auditorially
and/or visually--although not as enjoyably. On the other hand, lowachieving students who prefer kinesthetic and/or tactual learning can only
master difficult information through those modalities. In addition, low
achievers often have only one perceptual strength, or none, in contrast to
the multiperceptual strengths of the gifted.
Are there sociological differences between gifted and nongifted students?
Gifted adolescents in nine cultures preferred learning either by themselves
or with an authoritative teacher. If those students are representative of
gifted students across nations, cooperative learning and small-group
instructional strategies should not be imposed on them; few wish to learn
with classmates. In addition, when permitted to learn alone, with peers, or
with a teacher based on their identified learning-style preferences, even
gifted first and second graders revealed significantly higher achievement
and aptitude test scores through their preferred styles--and few preferred
learning either via whole-class instruction or with their nongifted
classmates.
Are there chronobiological differences between gifted and nongifted
students? Although some gifted adolescents learned well in the morning,
many more preferred late morning, afternoon, and/or evening as their best
times for concentration. At no educational level (K-12) did we find a
majority of early-morning students, and this is particularly true for poor
achievers. Conventional schooling appears to be unresponsive to the
majority of both gifted adolescents and low achievers, whose best time of
day rarely is early morning.
Are there differences between the processing styles of gifted and nongifted
students? Of the gifted and talented students we tested for processing
style, 19 percent were analytic, 26 percent were global, and 56 percent
were integrated processors who functioned in either style--but only when
interested in the content. Both global and analytic students can be gifted,
but textbooks and teachers' styles tend to be analytic rather than global.
Do the learning styles of able and at-risk students differ? Seven learningstyle traits significantly discriminate between at-risk students and
dropouts, and students who perform well in school. A majority of--but not
all--low achievers and dropouts need (a) frequent opportunities for
mobility, (b) reasonable choices of how, with what, and with whom to learn,
(c) a variety of instructional resources, environments, and sociological
groupings rather than routines and patterns, (d) opportunities to learn
during late morning, afternoon, or evening hours (rarely in the early
morning), (e) informal seating--not wooden, steel, or plastic chairs and
desks, (f) soft illumination (bright light contributes to their
hyperactivity), and (g) either tactual/ visual introductory resources
reinforced by kinesthetic/ visual resources, or kinesthetic/visual
introductory resources reinforced by tactual/visual resources.
Underachievers tend to have poor auditory memory. When they learn visually,
it usually is through pictures, drawings, graphs, symbols, comics, and
cartoons rather than book text. Although underachievers often want to do
well in school, their inability to remember facts through lecture,
discussion, or reading contributes to their low performance in conventional
schools, where most instruction is delivered by teachers talking and
students listening or reading. (Although underachievers learn differently
from high achievers and the gifted, it should also be pointed out that they
can learn differently from each other.)
What role does motivation play in the learning-style construct? Motivation
is one of the twenty-three elements of learning style. Unlike at least
three-quarters of the remaining elements, motivation is not biologically
imposed. Rather it develops as a reaction to each learner's experiences,
interest in the content that is being learned, and the ease with which it
can be mastered.
How does culture contribute to achievement? The Milgram, Dunn, and Price
(1993) study of the learning styles of almost 6,000 gifted and nongifted
adolescents in nine diverse cultures revealed that opportunity influences
individuals' ability to develop specific areas of talents that may
eventually lead to giftedness. For example, if access to creative
activities, information, or role models was not readily available in a
specific culture, few adolescents developed giftedness in that domain.
Thus, in cultures that respected art, higher percentages of artistically
gifted students were identified. The same finding held firm across other
gifted domains--athletics, dance, mathematics, literature, music, and
science--across eight countries (Brazil, Canada, Greece, Guatemala, Israel,
Korea, the Philippines, and the United States) and the culture of the Maya.
It may be important to acknowledge that most communities in the United
States financially support athletics regardless of the state of the economy
but rarely hesitate to eliminate programs in music, art, or drama. Is it
any wonder that most young American boys seem to aspire to becoming
baseball, basketball, or football players rather than scientists or
artists?
How important will learning styles be in the year 2000? Given the
statistically higher reading and mathematics standardized achievement test
scores of previously failing and poorly achieving students in the United
States after their learning styles were addressed, learning styles are
likely to become a mandated prerequisite for schooling within the next
decade. It will only take one class action suit, led by one small group of
angry parent advocates, whose nontraditional children have been demoralized
by the imposition of traditional schooling, to cause that change. And it
will happen, because learning style is not something that affects other
people's children. In every family, mothers' and fathers' learning styles
are dramatically different from each other. Siblings do not necessarily
reflect their parents' styles, and siblings' styles differ significantly.
In most families, one child does extremely well in traditional schooling
and another considers academics dull and uninteresting. A third child may
be extremely different from the first two; thus, one in three is likely to
pursue a path totally different from the parents' and the siblings'. Style
affects everyone. Whether or not we acknowledge that we each learn
differently, certain resources, approaches, and teachers are right for
some--and very wrong for others.
REFERENCES
Alberg, J., L. Cook, T. Fiore, M. Friend, S. Sano, et. al. 1992.
Educational approaches and options for integrating students with
disabilities: A decision tool. Triangle Park, N.C.: Research Triangle
Institute, P. O. Box 12194, Research Triangle Park, North Carolina 27709.
Andrews, R. H. 1990. The development of a learning styles program in a low
socioeconomic, underachieving North Carolina elementary school. Journal of
Reading, Writing, and Learning Disabilities International 6(3): 307-14.
Beaty, S. A. 1986. The effect of inservice training on the ability of
teachers to observe learning styles of students. Doctoral diss., Oregon
State University. Dissertation Abstracts International 47:1998A.
Brunner, C. E., and W. S. Majewski. 1990. Mildly handicapped students can
succeed with learning styles. Educational Leadership 48(02): 21-23.
Curry, L. 1987. Integrating concepts of cognitive or learning styles: A
review with attention to psychometric standards. Ottowa, Ontario: Canadian
College of Health Services Executives.
Dunn, R., and K. Dunn. 1992. Teaching elementary students through their
individual learning styles. Boston: Allyn and Bacon.
------. 1993. Teaching secondary students through their individual learning
styles. Boston: Allyn and Bacon.
Dunn, R., S. A. Griggs, J. Olson, B. Gorman, and M. Beasley. 1995. A metaanalytic validation of the Dunn and Dunn learning styles model. Journal of
Educational Research 88(6): 353-61.
Dunn, R., K. Dunn, and J. Perrin. 1994. Teaching young children through
their individual learning styles. Boston: Allyn and Bacon.
Dunn, R., K. Dunn, and G. E. Price. 1972, 1975, 1979, 1981, 1984, 1989.
Learning Style Inventory. Lawrence, Kan.: Price Systems.
Homework Disc. 1995. Jamaica, N. Y.: St. John's University's Center for the
Study of Learning and Teaching Styles.
Milgram, R. M., R. Dunn, and G. E. Price, eds. 1993. Teaching and
counseling gifted and talented adolescents: An international learning style
perspective. Westport, Conn.: Praeger.
Perrin, J. 1982. Learning Style Inventory: Primary Version. Jamaica, N. Y.:
St. John's University's Center for the Study of Learning and Teaching
Styles.
Stone, P. 1992. How we turned around a problem school. The Principal 71(2):
34-36.
Editor's Note: Rita Dunn, an authority on learning styles, is a professor
in the Division of Administrative and Instructional Leadership and the
director of the center of the study of learning and teaching styles at St.
John's University Jamaica, New York. She has published more than three
hundred articles, chapters, monographs, and research paper on learning
styles and on the results of being taught according to one's preffered
learning style. She was interviewed by mail by Michael Shaughnessy for this
article.
~~~~~~~~
By MICHAEL F. SHAUGHNESSY
Michael F. Shaughnessy is a professor at Eastern New Mexico University,
Pontales, New Mexico.
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Title: Learning styles in a technology-rich environment.
Subject(s): LEARNING strategies
Source: Journal of Research on Computing in Education, Summer97, Vol. 29
Issue 4, p338, 13p, 2 graphs
Author(s): Cohen, Vicki L.
LEARNING STYLES IN A TECHNOLOGY-RICH ENVIRONMENT
Abstract
This study investigated whether learning style would change after a year of
schooling in a technology-rich educational environment dedicated to a
constructivist approach to reaming. The subjects were 15 gifted freshmen
who had been accepted into a "magnet" high school. The subjects were given
Dunn and Dunn's Learning Style Inventory and a questionnaire before and
after the school year. This study could not conclude that learning styles
change after one year; however, there are suggestions that learning styles
are affected by factors within the environment, such as exposure to
technology. Results suggest that a technology-rich environment affects the
written and unwritten curriculum within a classroom, especially impacting
the social context that exists. The use of computers affected the way the
content was explored and presented. A technology-rich environment also
seemed to affect the interaction that occurred between students and
students, students and teachers, and teachers and teachers. A much more
casual social context emerged, which was supportive of exploration and
discourse. After one year, some students displayed low preference for
learning in this environment; the researcher concluded that instruction
must encourage many different forms of learning styles. (Keywords:
computers, constructivism, learning styles, secondary education, social
context.)
The purpose of this article is to document and explain a pilot research
project that was undertaken in the 1994-1995 school year to investigate the
relationship between student learning style and ability to use the computer
as a cognitive tool. The primary objectives of this study were: (a) to
investigate whether students' learning styles would change after a year of
schooling in a unique technology-rich educational environment dedicated to
a constructivist team-oriented approach to learning and (b) to analyze the
study's research questions and methodology, pinpointing revisions that
would need to be addressed in a more comprehensive study.
The study was small in scope, involving a team of 15 freshman from all over
Bergen County, New Jersey, who had been accepted into a "magnet" high
school that emphasizes science, mathematics, and technology. The high
school was conceived in 1990; has approximately 50 students per grade; and,
as of 1994-1995 school year, did not have a graduating class yet. The
school, the Academy for the Advancement of Science and Technology, is
dedicated to a constructivist approach to education. In the constructivist
approach, students are encouraged to construct their own knowledge bases,
and teachers guide students through the process of obtaining new
understandings through the use of discourse, discussion, and questioning.
Constructivist teaching practices help learners internalize, reshape, and
transform new information (Brooks & Brooks, 1993). The academy, therefore,
encourages teachers to keep teacher-directed lectures to a minimum and to
emphasize a hands-on discovery-oriented approach to learning. Technology is
infused into all classes, and every student and teacher is given a computer
to take home. This environment fosters a unique approach to education and
supports any research that is conducted on its premises. Within such a rich
context, a small exploratory pilot study seemed appropriate for the first
year of research. Here, initial research questions could be examined, and
instrumentation and methodology could be tried out on a small number of
students.
It was determined that during the first year of research the researcher
would observe one or two classes in depth and gather data on the overall
social and educational context of the school. By performing this
preliminary research, the author would be better able to determine the best
direction for future research.
BACKGROUND
This study was based on the theoretical assumption that technology is most
effectively used in the classroom when students use technology as a
cognitive tool. In this way, students must apply problem-solving processes
and employ higher order reasoning strategies leading to cognitive growth.
As such, the technology becomes a "mind-extension 'cognitive tool'" (Derry
& Lajoie, 1993, p. 5). When students use technology as a tool that fosters
higher order thinking skills, the ways in which students learn changes;
thus, technology has a direct positive impact upon student achievement
(Cohen, 1995).
Other researchers (Reusser, 1993) maintain that computer technologies can
serve as powerful catalysts for facilitating development of generalized
self-regulatory skills, provided they are appropriately deployed within a
social classroom environment that promotes reflection, discussion, and
critique during problem-solving processes. Cognitive instructional tools
must be used by mindful teachers and learners in a culture of problem
solving in which higher order strategies and control processes are modeled
and students are coached by a mentor who gradually phases out support as
the student gains independence and expertise in demonstrating how to use
these processes (Palincsar, 1986; Reusser, 1993; Vygotsky, 1978).
This project used Dunn and Dunn's Learning Style Inventory (LSI; Dunn,
Dunn, & Price, 1989) to test learning style. A more complete profile of
each student would emerge by using a statistically valid and reliable test
such as the LSI.
Dunn, Dunn, and Price (1989) state that a learning style is
and developmentally imposed set of personal characteristics
same teaching method effective for some and ineffective for
Beaudry, & Klavas, 1989). The LSI obtains a profile of each
following four major areas:
a biologically
that make the
others (Dunn,
student in the
1. Environment, including sound, temperature, light, and design.
2. Emotionality, including motivation, responsibility, persistence, and the
need for either structure or flexibility.
3. Sociological needs, including learning alone, with peers, with adults,
or in a combination of these ways.
4. Physical needs, including perceptual preferences (auditory, visual,
tactile, and kinesthetic), time of day one prefers to study, intake, and
mobility.
This inventory results in an individual profile of a student's preference
toward style of learning. Dunn, Dunn, and Price (1989) feel that classrooms
need to concentrate more upon individual learning style because students
tend to learn and remember better and enjoy learning more when they are
taught in a way that takes into account their learning style preferences
(Dunn, 1990). By using the LSI inventory, teachers should profile each
child's learning style and design instruction based upon individual needs.
When permitted to learn difficult academic information or skills through
identified preferences, children achieve statistically higher test and
attitude scores than when instruction is not supportive of their
preferences (Dunn, Beaudry, & Klavas, 1989).
Dunn, Dunn, and Price (1989) propose that each student has a specific
learning style and that instruction should be designed to best accommodate
that unique way of learning. Their model supports the assumption that
instruction should address individual styles of learning and that some
students learn best through different approaches. The project used this
perspective to explore whether learning styles can change within a
technology-rich environment that encourages one model of instruction.
THE RESEARCH SITE
This study took place at a specialized "magnet" high school in Bergen
County, New Jersey, the Academy for the Advancement of Science and
Technology, which emphasizes science, mathematics, and technology. As an
Apple Academy East, the academy infuses technology into all subject areas,
and the school is committed to a team-oriented project-based approach to
learning. As part of the Coalition of Essential Schools, the academy is
dedicated to educational reform and has developed an environment in which
students can explore, learn, and work together on projects they might
encounter in the real world. One of the academy's goals is to offer
interdisciplinary learning with an emphasis on critical analysis and
expression of ideas.
The academy has many networked classrooms of both Macintosh PowerPCs and
IBM compatible PCs. Every classroom has an overhead projection system, at
least one scanner, and notebook computers to accommodate any overflow of
students. The school also has specialty classrooms equipped for multimedia
production with video capture boards, high-capacity storage drives, and
VCRs and videodisc players attached to workstations for capture of video
images. It also has special PC CAD rooms and specially equipped advanced
scientific equipment attached to computers. In addition, there is a
robotics area for juniors and seniors to work on special industrial
projects. As part of the facility, there is a distance-learning classroom
equipped with two-way interactive television that can transmit to a
consortium of 14 schools throughout the county.
The student body is comprised of those students selected from all over
Bergen County who have demonstrated individual initiative; have interests
in math, science, and technology; perform in the above average to superior
range academically; and have demonstrated a commitment to a longer school
day and school year. It attracts a population of students who would be
classified as "gifted." The population is also very multicultural, and,
with much effort in the area of recruitment, equally divided in male and
female representation.
Another unique feature of this school is the physical layout of classrooms.
There are no "desks" per se; instead there are conference tables, computer
workstations, and informal tables. In the interdisciplinary American
studies program, which encompasses English and social studies, there is an
amphitheater for presentations. This informal design corresponds to the
informal atmosphere that pervades this school.
Because the academy is a new school, some teachers are not consistently
following the approach that the school strongly advocates and still employ
lecture and tests as a major part of their classroom instruction. Other
teachers are very unstructured and interpret constructivism to be minimal
teacher feedback and guidance. Projects are the major focus of the each
class but the success of how this approach is implemented varies from class
to class.
METHOD
Sample
A team of 15 students was assigned to this researcher by the school
administration. The team was chosen based on scheduling arrangements with
the school and the researcher. The team was comprised of 12 male students
and 3 females. Nine of the students were white, five were Asian, and one
was Hispanic. The gender imbalance was seen as a definite disadvantage, but
because of scheduling problems and the nature of this exploratory study,
this arrangement was accepted.
The Measures
The LSI (Dunn, Dunn, & Price, 1989) was administered to the sample of
students. This inventory obtains a profile of each student in 22 areas
that, when identified as relevant areas, represent the way in which that
individual prefers to study or concentrate. These 22 areas include the
following:
1. Noise level.
2. Light.
3. Temperature.
4. Design of study area.
5. Motivation to achieve academically.
6. Persistence to complete tasks.
7. Responsibility to conform or follow through on assignments.
8. Structure in doing schoolwork or preference for doing an assignment his
or her own way.
9. Learning alone or with peers.
10. Preferring to have authority figures present.
11. Preferring to learn in several ways.
12. Auditory preferences.
13. Visual preferences.
14. Tactile preferences.
15. Kinesthetic preferences.
16. Preferring intake while studying.
17. Functions best in evening or morning.
18. Functions best in late morning.
19. Functions best in afternoon.
20. Prefers to be mobile when studying.
21. Parent-figure motivated.
22. Teacher motivated.
The test is designed for Grades 5-12. Students respond on a five-point
Likert scale ranging from Strongly Disagree to Strongly Agree. There are
105 questions, and an individual profile is calculated from a student's
score. The standard score scale ranges from 0 to 80, with a mean of 50 and
a standard deviation of 10. The standard score is calculated based on the
scores of more than 500,000 students who have completed the LSI.
Individuals having a standard score of 60 or higher have a high preference
for that area when they study. Individuals having a standard score of 40 or
lower have a low preference in that area when they study. Individuals
having scores that fall between 40 and 60 indicated that their preference
is neither high nor low in that area. The inventory has gone through
extensive testing and has proven to have high reliability and validity
(Dunn, Dunn, & Price, 1989).
A questionnaire was also administered to the students immediately following
the LSI. The questionnaire surveyed each student's previous knowledge of
computers; motivational interest in technology; and preference for working
on a team, with a partner, or alone.
Procedure
The four major methods of gathering data were weekly classroom
observations, two interviews with each of the students, administration of
the LSI, and administration of the questionnaire. This approach was
selected because a flexible exploratory method was needed that combined
qualitative analysis of observational reports with quantitative data
gathered through the LSI and the questionnaire.
I observed primarily one classroom once a week: the American studies class
that met 8:00-10:20 a.m. every Friday morning. This class, which was
interdisciplinary in approach, was taught by two different teachers: Ms.
Cerrato taught the social studies component, and Ms. Lisa taught the
English component. Although the two teachers worked closely together on
curriculum coordination, these were taught as two separate classes, each
with a distinct curriculum and assignments. Ms. Cerrato utilized the
computer frequently throughout the social studies curriculum, but Ms. Lisa
did not emphasize the use of computers at all, preferring to hold class at
a conference table in seminar fashion and to have individual conferences on
student writing. Students could use the computer for her projects if they
desired, but it was not emphasized.
With the approval of the school authorities and with parental consent, the
LSI and the questionnaire were administered to the 15 students in the first
month of schooling. Questionnaires were given after the LSI. In the final
month of schooling, the LSI and the same questionnaire were administered
again to 14 students; one of the female students had returned to her
district high school.
In the third and tenth months of schooling, students were interviewed to
assess their perceptions of the academy and to determine the level of
satisfaction with the academy's program. The responses were recorded by the
researcher and the students were probed if the response was brief. At the
three-month interview, students received a written profile of their
learning styles to take home. I discussed what each profile meant and how
it could be interpreted and used.
Data Analysis
Descriptive statistics were used to analyze the data quantitatively. After
the LSI inventories were scored, Price Systems, Inc., sent two computer
printouts for group analysis. These reports summarized the elements by
subscale for all individuals in the group having standard scores of 60 or
higher or 40 or lower. The printouts indicate frequency of response and
percent of the group. Differences in group frequencies and percent that
occurred between the two different test administrations were calculated.
The responses on the questionnaire were also coded and analyzed, including
previous knowledge of computers; motivational interest in technology; and
preference for working on a team, with a partner, or alone.
The two interviews were carefully studied and the field notes were coded to
see if any patterns existed in the American studies classroom. These notes
were closely read and coded into three major divisions: (a) social context
of classroom, (b) computer-related activities, and (c) learning style of
students.
FINDINGS
The Field Notes and Interviews
After analyzing both the field notes taken during the year of observation
and the two interviews, a few major points emerge. The first point is that
technology seemed to impact the written curriculum in the American studies
classroom that I observed. New ways of looking at and exploring the
curriculum emerged as these two teachers tried to integrate technology
directly into their courses. Computer literacy was no longer left to the
jurisdiction of the "computer teacher" who existed in a "computer lab."
Each teacher had a unique way of approaching computer literacy as
technology became a tool that impacted each subject area in a unique and
specific way. Databases, the Internet, scanning images, and HyperCard
(1987-1995) were incorporated into the daily routine of the classroom.
Teachers and students were exploring how technology could be used as a tool
that enhances the subject area.
In addition, technology impacted the way the content was presented and
discussed. The subject matter was presented with a much more visual
representation of the concepts, and computer projects sometimes seemed to
determine the direction the class would go. Such a visual emphasis often
seemed to help many of the students who had language problems because they
were not native speakers or were visual/kinesthetic learners. In the
American studies class, one class project was an extensive HyperCard (19871995) stack discussing people who had contributed to or had an effect
during the Civil War. Each card had a description of the person, his or her
contribution, and a scanned image of the person. Three students were
assigned to be project managers; they were responsible for developing a
template that the class could use to standardize the stack. The class spent
a lot of time discussing the stack, how to set it up, how the template
should look and work, and how the buttons should function. Students worked
independently on their research, guided by the way the cards would be set
up. All work was given to the project managers, who would import the
information into the template. One project manager, who had language and
social problems, grew a great deal during this project because of his
responsibility and leadership role. At another time, the class was involved
in a project in which teams were to present each amendment to the U.S.
Constitution using ClarisWorks (1995) or HyperCard. I observed an
interesting discussion on how to visually depict the 26th amendment: One
student raised his hand and said, "I can't find a picture of people
voting!" A serious discussion ensued about what this amendment really meant
and how it could be depicted.
A second major point that emerged from the field notes was that technology
seemed to affect the unwritten curriculum as well as the written
curriculum. The way the teacher managed the discipline and rules pervading
the classroom changed as well. Learning was seen as a much more natural
process, which was not disrupted by conversation and discourse. The
boundaries and rules of the traditional classroom (e.g., never interrupt
the teacher) were replaced by a much more fluid interpretation of how a
classroom should function. Much of this could be attributed to the high use
of technology within the classroom, as traditional ways of accomplishing
tasks, assignments, and lessons were altered.
This can be illustrated by examining the social context that existed at the
academy. In this type of technology-rich environment, social interaction
between students and students, between students and teachers, and between
teachers and teachers was different than what you might find in a more
traditional high school classroom. In the American studies classroom I
observed, the students were given many different types of projects to work
on as teams, including designing databases of historical figures using
ClarisWorks (1995), developing a timeline of the Civil War using HyperCard
(1987-1995) stacks, and developing an interactive game on a novel they had
read. During these projects, the students felt free to work where they
pleased--at tables, at the computers, or in groups in a corner. During this
time I observed them conversing about many things that were on task and off
task: how to use the technology, topics concerning the project, and what
happened last weekend. I often observed a group of students sitting at a
scanner or around a computer, laughing and talking about personal matters
for about 10 min with no disciplinary action being taken. The students
would naturally refocus on their project and continue talking about more
on-task matters. This is not to say disciplinary comments concerning ontask behavior were not made, because, in fact, both Ms. Cerrato and Ms.
Lisa continuously urged the students to quiet down and stay focused on the
task at hand. However, in this environment, interaction between students
was much more frequent, casual, and accepted. Much of the student-student
interaction centered around the technology; students discussed new
technological discoveries, showed how the content could be covered using
the technology, or casually chattered about a new game while sitting
informally around a piece of equipment.
In addition, teachers and students interacted in a much more casual way.
Ms. Cerrato often sat next to a student at the computer and gave individual
help with a project. Within this context, students felt comfortable asking
questions and seeking help at all times. I continuously saw random students
walking into the classroom during the class period and seeking help from
Ms. Cerrato or Ms. Lisa; both teachers freely gave help and encouraged this
type of interaction. Students would frequently show Ms. Cerrato how to do
something, and she often sought help from some of the more capable
students. The interaction between teachers and students was not just in one
direction and for one purpose--to convey information to students. Rather,
the interaction was fluid and fulfilled many purposes.
Teacher-teacher interactions were also different than in a regular
classroom. Team-teaching facilitated the sharing of ideas, jokes, casual
banter, and informal conversations between teachers. Ms. Cerrato and Ms.
Lisa were constantly joking with each other and often substituted for each
other if one came late or had to leave the room for a meeting. Other
teachers frequently walked into the classroom at any time to ask either Ms.
Cerrato or Ms. Lisa questions. The only time this changed was during formal
student presentations, during which absolute silence and attention were
required. In fact, the classroom often seemed chaotic, especially when
guests, sometimes numbering 20 or more, would be ushered into the classroom
to observe. Despite all this seemingly distracting social interaction,
students were actively engaged in learning. Much sharing and discussing
took place: Students became teachers and explorers in a classroom culture
that was very relaxed. The technology and its use seemed to be the factor
that affected the social context and thereby the unwritten curriculum that
I observed.
A third major point that emerged from the field notes is that these gifted
students wanted to be shown how to seek deeper connections when using the
computer. Many of the students were not "enamored" with just using the
computer; they often became frustrated when they felt the project they were
working on was not challenging them to use their minds and seek deeper
connections. A student wrote on the questionnaire that one subject that
used technology extensively "lacks any content quality. We are told that we
are supposed to not learn content but how to think. We have not done
either. I want projects with substance!" Many students made comments in a
similar vein, saying "Get rid of the busywork." Those teachers who acted as
"cognitive mentors" were respected and favored. When the students were not
using computers for problem solving and higher reasoning, they became
frustrated and bored. As one student said,
I like working with the technology [in CAD] and I have a natural talent for
designing and I would like to pursue it. [The teacher] is just the most
knowledgeable teacher about technology in the school and he's helpful and
good humored.
Another student said about the same class:
I am personally very interested in computer engineering, and love the high
level of technology we use to complete various, interesting projects. [The
teacher] is kind and humorous, making class extremely fun.
As a third student summarized, "I want to be challenged!"
The field notes also point out that some students, who preferred working
alone and desired a more structured learning environment, may have felt
some dissatisfaction with the team-oriented approach used. As one student
commented that working in teams "is difficult. If I have a good idea,
nobody ever listens to me. I'm never the leader because I'm not the leader
type." Another student added a very common remark: "When everyone doesn't
pull their own weight, it adds stress." Another said, "I don't like working
in teams, and I find it a problem. Everyone is smart, but I like working
alone. In teams you can't compromise." These comments illustrate the need
to actively teach students how to work on teams effectively.
The interviews gave valuable information about the students' perceptions of
the academy. During the initial interview in November 1994, most of the
students expressed high satisfaction with the program. They were excited
and pleased with their progress to date. They loved using the technology.
Many also expressed stress over the confusion that pervaded some classes
and the fact that some teachers were lecturing. "School could be more
organized. Different teachers don't communicate with each other. Many
teachers are not experienced in this type of environment and this leads to
conflict." Many expressed that they loved working in teams, but others
expressed hesitation and even outright dislike for having to work in teams.
During the second interview in May 1995, most still expressed high
satisfaction with the school. One said "It was the best thing that could
have happened to me educationally. The teaching style is excellent in
basically all of my classes, and I could not be happier." A few others also
expressed disappointment. Almost all expressed deep satisfaction about
working with technology.
The LSI
The results of student responses by subscale on the LSI are summarized in
Figures I and 2. Each subscale has been categorized into high, medium and
low. In the subscale "Learning Alone or With Peers," high represents
preference for working with peers and low represents preference for working
alone.
There are certain trends indicated by the data that the population of
students displays. At the end of the year, the trends showed the following
preferences:
Students seemed less motivated and, therefore, required shorter and less
complicated assignments and more frequent teacher supervision.
Students seemed less persistent and, therefore, preferred short-term
assignments.
Students seemed less responsible and did not seem to want to complete
assignments that they did not find worthwhile.
Students seemed to prefer structured assignments rather than assignments
that allowed them to learn in different ways.
Students were less motivated by the teacher and wanted to work independent
of teacher and other authority figure input.
Students preferred a more formal classroom design, rather than being
allowed to sit on the floor, soft chairs, or pillows when studying.
Students seemed to prefer perceptual means of learning rather than auditory
means of learning.
When analyzing these results as a total picture, the LSI has yielded
somewhat unexpected, if not startling, results. The results show that the
students seemed less motivated, persistent, and responsible toward
schoolwork and assignments after a year at the academy. In September, this
group showed exceptionally high motivation, persistence, and
responsibility. In June, these students showed a marked decrease in these
areas. All these students came from traditional eighth-grade classes in
which they were perceived as the "smart kids." At the academy, they were
suddenly thrust into a much different learning environment, one in which
they were among many gifted students working together on teams. Long-range
team-oriented projects were frequently used during the year, and students
were given independence and latitude in working together on these projects.
After a year, the trend in preference toward less motivation, persistence,
and responsibility, and the trend toward preferring more structured
assignments suggests that this group was reacting to this shift. The
students may not have received the structure, feedback, and guidance they
needed. In previous studies, high IQ students preferred to learn by
themselves rather than with others, tended to be self-motivated, and
preferred to receive continuous feedback from authority figures (Dunn,
1993). If this population of students' learning preferences were not
accommodated during the year, the trend indicated by the data collected
during this study might occur. According to Dunn (1993), "a program
designed for gifted and talented youngsters should capitalize on their
personal sociological preferences and not be determined by persons who
advocate a single approach for all students" (p. 40).
This result may indicate that teachers need to address all learning styles
in their classrooms and that a curriculum must be designed that develops
all forms of intelligence and reaches all types of learning styles
throughout the year. If one lesson stresses teamwork, the next should
emphasize independent research. Teachers may need to be more sensitive to
different learning styles and use many different teaching styles.
Students may also need to be trained about engaging in positive social
interaction during teamwork so that the social and moral implications of
how to work together positively are discussed. If groupwork is a
predominant aspect of the curriculum, the process of working together might
need to be taught as well.
CONCLUSION
After completing this study, I could not definitely conclude that learning
styles change after one year of immersion in a technology-rich environment
dedicated to using a team-oriented, hands-on, and constructivist approach
to education. There are suggestions that learning styles are affected by
factors within the environment, such as exposure to technology, and that
certain areas of preference, such as motivation to succeed, persistence to
complete tasks, responsibility to complete assignments, and structure in
doing schoolwork, are affected by exposure to specific instructional
methodologies. However, this needs to be verified by a larger study.
The results obtained during this study suggest that a technology-rich
environment seems to affect the written and unwritten curriculum within a
classroom. Teachers emphasized the visual nature of the subject matter, and
the use of computers affected the way the content was explored, often to
the extent of determining the directions in which the assignments and
lessons might go. The effect of these changes was that teachers and
students learned how to use technology as a tool that enhances each subject
area in a different and specific way. The interaction between students and
students, students and teachers, and teachers and teachers changed.
Learning was seen as a much more natural process whereby conversation does
not interfere with acquisition or application of knowledge. A looser, more
casual social context that was supportive of exploration and exchange
emerged. This social context was not limited to one classroom; it extended
throughout the school into almost all classrooms I observed.
The results of this study also suggest that the individual learning styles
of gifted students must be addressed. Teachers may need to design a
curriculum that promotes all types of learning styles throughout the year
Gifted students also desire opportunities to use computers as cognitive
tools that explore deeper connections in a subject rather than merely using
the technology as a vehicle to produce "glitzy" presentations without much
depth. They desire to be pushed intellectually and want to be challenged.
In summary, a technology-rich environment impacts the written and unwritten
curriculum of a school. Schools should be sensitive to students' reaming
styles when adopting an instructional methodology that will be used
extensively throughout the curriculum. At a time when educators are
unilaterally embracing change and reform and claiming that "constructivism"
is the panacea for all problems in the classroom, the effects of the
instructional approach upon the student population must be analyzed. This
study could not definitively prove that learning styles change after one
year in a technologically rich environment. A larger, more comprehensive
study with control groups must be used to determine this.
Contributor
Dr. Vicki L. Cohen is an assistant professor in the School of Education at
Fairleigh Dickinson University, Teaneck, New Jersey. She coordinates the
Instructional Technology Certificate Program and teaches graduate classes
in reading, evaluation, and instructional technology. Her research
interests include technology in education, literacy development, and
evaluation and assessment. (Address: Dr. Vicki Cohen, School of Education,
Bancroft Hall, Farleigh Dickinson University, 1000 River Road, Teaneck, NJ
07666; cohen @alpha.fdu.edu.)
Figure 1. Frequency of responses on selected LSI items, September 1994
Figure 2. Frequency of responses on selected LSI items, June 1995
References
Brooks, J., & Brooks, M. (1993). The case for constructivist classrooms.
Alexandria, VA: Association for Supervision and Curriculum Development.
ClarisWorks [Computer software]. (1995). Santa Clara, CA: Claris
Corporation.
Cohen, V. (1995, Fall/Winter). What schools should know about technology: A
review of the research. Record, 28-34.
Derry, D., & Lajoie, S. (1993). A middle camp for (un)intelligent
instructional computing: An introduction. In S. Lajoie & S. Derry (Eds.),
Computers as cognitive tools (pp. 1-11). Hillsdale, NJ: Lawrence Erlbaum
Associates.
Dunn, R. (1990). Understanding the Dunn and Dunn learning styles model and
the need for individual diagnosis and prescription. Reading, Writing, and
Learning Disabilities, 6, 223-247.
Dunn, R. (1993). Teaching gifted adolescents through their learning style
strengths. In R. Dunn & G. Price (Eds.), Teaching and counseling gifted and
talented adolescents (pp. 37-67). Westport, CN: Praeger.
Dunn, R., Beaudry, J., & Klavas, A. (1989). Survey of research on learning
styles. Educational Leadership, 46(6), 50-58.
Dunn, R., Dunn, K., & Price, G. (1989). Learning style inventory manual.
Lawrence, KS: Price Systems.
HyperCard [Computer software]. (1987-1995). Cupertino, CA: Apple Computer,
Inc.
Palincsar, A. S. (1986). The role of dialogue in providing scaffolded
instruction. Educational Psychologist, 21, 73-99.
Reusser, K. (1993). Tutoring systems and pedagogical theory:
Representational tools for understanding, planning, and reflection in
problem solving. In S. Lajoie & S. Derry (Eds.), Computers as cognitive
tools (pp. 143-177). Hillsdale, NJ: Lawrence Erlbaum Associates.
Vygotsky, I. S. (1978). Mind in society. Cambridge: Harvard University
Press.
~~~~~~~~
By Vicki L. Cohen, Fairleigh Dickinson University
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Title:
Learning Styles and Technology in a Ninth-Grade High School
Population.
Subject(s):
HIGH school environment -- New Jersey -- Bergen County; LEARNING
& scholarship -- New Jersey -- Bergen
County; HIGH school students -- New Jersey -- Bergen County
Source:
Journal of Research on Computing in Education, Summer2001, Vol.
33 Issue 4, p355, 12p, 1 chart, 1 graph
Author(s):
Cohen, Vicki L.
Abstract:
This study explores whether a technology-rich environment that
promotes a constructivist approach to learning has
a significant effect on the learning styles of freshmen high
school students. Two high school freshmen classes were
pre- and posttested on a learning-style inventory. One high
school has a technology-rich environment and uses a
project-based approach to learning, while the other school has a
more traditional curriculum that is not technology
rich. Six variables from the inventory were analyzed in this
study. The results suggest that a technology-rich
environment that promotes collaborative, project-based learning
can have an effect on learning style. This study
suggests that the environment contributed to the differential in
effect size that was found at posttest time.
(Keywords: high school and technology, learning style,
technology in education.) [ABSTRACT FROM AUTHOR]
AN:
5078025
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0888-6504
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LEARNING STYLES AND TECHNOLOGY IN A NINTH-GRADE HIGH
SCHOOL POPULATION
Abstract
This study explores whether a technology-rich environment that promotes a
constructivist approach to learning has a significant
effect on the learning styles of freshmen high school students. Two high
school freshmen classes were pre- and posttested on a
learning-style inventory. One high school has a technology-rich environment
and uses a project-based approach to learning,
while the other school has a more traditional curriculum that is not
technology rich. Six variables from the inventory were analyzed
in this study. The results suggest that a technology-rich environment that
promotes collaborative, project-based learning can have
an effect on learning style. This study suggests that the environment
contributed to the differential in effect size that was found at
posttest time. (Keywords: high school and technology, learning style,
technology in education.)
The purpose of this article is to explore the relationship between the
learning styles of high school students and their exposure to a
technology-rich educational environment in their freshman year of
schooling. This article will describe a study that took place in the
1996-1997 school year in which the total high school freshman classes from
two different high schools were assessed on learning
style. One class was from a more traditional high school that does not
infuse technology into its curriculum but is known for its
excellence in education, and the other class was from a "magnet" high
school that specializes in science and math and infuses
technology into every subject area. The objectives of this study were:
1. to determine if there was any significant effect on learning style when
freshmen high school students were working in a
technology-rich environment that promotes collaborative, project-based
learning;
2. to compare two different types of learning environments on high school
students' learning styles; and
3. to determine the effect of specific variables in Dunn and Dunn's
Learning Style Inventory (LSI) (Dunn, Dunn, & Price, 1989) on
freshmen students after a year in two very different high schools.
I contend that although Dunn and Dunn claim that learning style is a
biologically and developmentally derived set of variables that
affect the way one learns and interacts with the surrounding environment
(Dunn, 1990), a student's learning style can, in fact, be
altered and affected through the external conditions set up in the
environment. Therefore, one hypothesis set forth in this study was
that a technologically rich environment that supports a constructivist
approach to learning would change a student's learning style
after a year-long period of exposure.
BACKGROUND
An initial pilot study was undertaken in the 1994-1995 school year to
investigate the relationship of student learning style and ability
to use the computer as a cognitive tool (Cohen, 1997). This was a small
study of 15 students from all over Bergen County, New
Jersey, who had been accepted into the "magnet" high school, the Academy
for the Advancement of Science and Technology
(AAST). At AAST, teacher-directed lectures were kept to a minimum,
technology was infused into each class, and each student
and teacher was given a computer to take home. For the pilot study, two
classes were observed in depth, field notes were recorded,
and a small sample of students were pre- and posttested on the LSI (Dunn,
Dunn, et al., 1989) and a questionnaire I developed. This
study found that the use of technology affected all aspects of the teaching
and learning continuum and demanded new approaches
to the curriculum. New ways of looking at and exploring the curriculum
emerged as teachers tried to integrate technology directly
into the subject matter. However, this pilot study could not definitely
conclude that learning styles changed after one year of
immersion in a technology-rich environment. There were suggestions that
learning styles were affected by factors within the
environment, such as exposure to technology, and that certain areas, such
as motivation to succeed, persistence to complete tasks,
responsibility to complete assignments, and structure in doing schoolwork,
were affected by exposure to specific instructional
methodologies. However, this pilot study suggested that the findings needed
to be verified in a larger study.
The present study was designed to address the suggestions of the pilot by
pre- and posttesting the freshman classes in two
schools--AAST, which comprised approximately 60 students, and Ridgewood
High School (RHS), which comprised approximately
100 students. The study was to investigate the relationship between a
technology-rich environment that fosters a constructivist
approach to instruction and its effect on learning styles.
The study used the LSI (Dunn, Dunn, et al., 1989) to assess learning style,
which is a biologically and developmentally imposed set
of personal characteristics that make the same teaching method effective
for some and ineffective for others (Dunn, Beaudry, &
Klavas, 1989). The LSI obtains a profile of each student in four major
areas:
1. environment, including sound, temperature, light, and design;
2. emotionality, including motivation, responsibility, persistence, and the
need for either structure or flexibility;
3. sociological needs, including learning alone, with peers, with adults,
and/or in several ways; and
4. physical needs, including perceptual preferences (auditory, visual,
tactile and kinesthetic), time of day one prefers to study, intake,
and mobility.
This inventory results in an individual profile of a student's preference
toward style of learning. Dunn and Dunn feel that classrooms
need to concentrate more on individual learning style because students tend
to learn and remember better and enjoy learning more
when they are taught through their learning-style preferences (Dunn, 1990).
When permitted to learn difficult academic information
or skills through identified preferences, children achieve statistically
higher test and attitude scores than instruction that is not
supportive of their preferences (Dunn, Beaudry, et al.).
The Research Sites
The experimental setting for this project was the AAST, a specialized
magnet high school in Bergen County that emphasizes science,
mathematics, and technology. As an Apple Academy East, AAST infuses
technology into all subject areas, and the school is
committed to a team-oriented, project-based approach to learning. As part
of the Coalition of Essential Schools, AAST is dedicated
to educational reform and has developed an environment where students can
explore, learn, and work together on projects they
might encounter in the real world. One of AAST's goals is to offer
interdisciplinary learning with an emphasis on critical analysis
and expression of ideas.
AAST has many networked classrooms of both Macs and IBM-compatible PCs.
Every classroom has an overhead projection
system, at least one scanner, and notebook computers to accommodate any
overflow of students. The school also has:
• specialty classrooms equipped for multimedia production with video
capture boards, high-capacity storage drives, and VCRs and
videodiscs attached to workstations for capture of video images;
• special PC CAD rooms and specially equipped advanced scientific equipment
attached to computers;
• a robotics area where juniors and seniors can work on special industrial
projects; and
• a distance-learning classroom equipped with two-way interactive
television that can transmit to a consortium of 14 schools
throughout the county.
The student body comprises students from all over Bergen County who have
demonstrated individual initiative; have interest in math,
science, and technology; perform in the above-average to superior range
academically; and have demonstrated a commitment to a
longer school day and school year. It attracts a population of students who
would be classified as gifted. The population is also very
multicultural and, with much effort in the area of recruitment, is equally
divided in male and female representation.
Another unique feature of this school is the physical layout of classrooms.
There are no desks per se, but conference tables,
computer workstations, and informal tables to work at. Numerous computers
are noticeable in each classroom, as they dominate the
layout of the room, either by circling the circumference of the room or by
transforming the room into a lab. This informal design
corresponds to the informal atmosphere that pervades this school and
supports the instructional goal of the school: to promote a
constructivist approach to learning. In the constructivist approach,
students are encouraged to construct their own knowledge bases,
and teachers guide students through the process of obtaining new
understandings through the use of discourse, discussion, and
questioning. Constructivist teaching practices help learners internalize
and reshape, or transform, new information (Brooks & Brooks,
1993).
RHS was the control setting for this study. Located in a suburban, uppermiddle-class neighborhood in Bergen County, RHS has
consistently been recognized and honored for its excellence in education.
RHS's total enrollment of students in 1997 was 1,446, with
97% of students taking the SATs and averaging 572 in math and 491 in
verbal. More than 88% of students at RHS plan to attend
college.
RHS has a more traditional approach to education, with classes structured
in rows of seats and a teacher frequently standing in the
front of the room lecturing from an overhead projector. Classes are well
behaved, and discipline is not a major problem. Students are
given a good deal of freedom, especially during lunch, when they sit in the
halls freely talking or wandering about. The atmosphere is
relaxed but strict. Technology is not infused regularly into the subject
areas, and most classrooms are not equipped with more than a
few computers, if any at all. When technology is used, the classes must
move to a lab or media center where there are enough
computers for all students. Instructional methodology is based on each
individual teacher's teaching style, and project-based learning
or constructivism is not promoted throughout the school as part of its
philosophy.
METHOD
Samples
Sixty-six students (out of a total of 70) in the freshman class at AAST
were pre- and posttested; 34 were males, and 32 were
females. It was a multicultural group that had been selected to attend
through rigorous mathematical testing, references, interviews,
and analysis of middle school grades.
Ninety-seven students (out of a total of approximately 120) in the freshman
class at RHS were pre- and posttested; 43 were male,
and 54 were female. The sample included all students whose parents had
returned the advised consent form, regardless of the
student's educational history or special education classification. The
sample reflected a cross-section of the population of students at
RHS, approximately 10% multicultural and 90% white, middle-class students.
Measures
The LSI (Dunn, Dunn, et al., 1989) was administered to the sample of
students. This inventory obtains a profile of each student in 22
areas that, when identified as relevant, represent the way in which the
individual prefers to study or concentrate: (1) noise level, (2)
light, (3) temperature, (4) design of study area, (5) motivation to achieve
academically, (6) persistence to complete tasks, (7)
responsibility to conform or follow through on assignments, (8) structure
in doing schoolwork or preference for doing an assignment
his or her own way, (9) learning along or with peers, (10) preferring
authority figures present, (11) preferring learning in several
ways, (12) auditory preferences, (13) visual preferences, (14) tactile
preferences, (15) kinesthetic preferences, (16) prefers intake
while studying, (17) functions best in evening or morning, (18) functions
best in late morning, (19) functions best in afternoon, (20)
prefers to be mobile when studying, (21) parent-figure motivated, and (22)
teacher motivated.
The test is designed for Grades 5-12. Students respond on a five-point
Likert scale ranging from Strongly Disagree to Strongly
Agree. The LSI has 104 questions, with approximately four items attributed
to each variable. An individual profile is calculated from
a student's score. The standard score scale ranges from 0 to 80 with a mean
or 50 and a standard deviation of 10. The standard
score has been calculated based on the scores of more than 500,000 students
who have completed the LSI. Individuals having a
standard score of 60 or higher have a high preference for that area when
they study. Individuals having a standard score of 40 or
lower have a low preference in that area when they study. Individuals
having scores that fall between 40 and 60 indicated that their
preference is neither high nor low in that area. The inventory has gone
through extensive testing and has proven to have high
reliability and validity (Dunn, Dunn, et al., 1989).
This study focused on six of the variables from the LSI, which were chosen
because of the pilot study: motivation, persistence,
responsibility, preference for working alone or with peers, parent
motivated, and teacher motivated. These six variables, targeted
before the study began, seemed to be the most relevant to this study. Many
of the variables, such as noise level, light preference,
room design, intake, time of day for studying, and others, were not
directly relevant to the research question. It also helped to focus
the study on six specific areas rather than to look at all 22 factors
measured in the LSI.
Procedure
The two methods of data gathering were administration of the LSI and
administration of an interview to a small sample of students at
both AAST and RHS. With the approval of the school authorities and with
parental consent, the LSI was administered to the two
different samples in the first month of schooling. In the final month of
schooling, the LSI was administered again to the same two
samples of students at AAST and at RHS. In addition, in May, 10 students
from each sample were interviewed to determine the
level of technology use for that year and the level of satisfaction with
their high school's program. I recorded all students' responses.
Data Analysis
After the LSI inventories are scored, Price Systems, Inc., sends two
computer printouts for group analysis. These reports summarize
the elements by subscale for all individuals in the group having standard
scores of 60 or higher or 40 or lower. The printouts indicate
frequency of response and percent of the group. Differences in group
frequencies and percent between the two different test
administrations were calculated.
Descriptive statistics were used to obtain information about the mean and
standard deviation. Multivariate tests, Pillai's Trace and
Wilks' lambda, were used to obtain information on effect size for all preand posttest measures. Univariate F tests were performed
on all variables subsequent to performing the multivariate tests. The data
were analyzed and have been presented in Figure 1, which
displays graphs showing the relevant sample means along with the size of
the standard error of the mean for each variable. The size
of the standard errors of the mean provides a direct and intuitive visual
measure of how, precisely, the location of the relevant
population means--and thereby the general pattern of the population means-can be inferred.
RESULTS
Multivariate tests, Pillai's Trace and Wilks' lambda, were used to
determine whether there was a difference between AAST and
RHS at pre-and posttest time. These mulitvariate tests showed that there
was a significant difference between schools on the
pretest, F(6,144) = 3945.106, p < .002. Effect size, given by eta-squared
(Eta[sup 2]), is 0.134. There was a significant difference
between schools on the posttest, F(6, 143) = 3653.237, p < .0005. Effect
size, given by Eta[sup 2], is .222. The effect size was
greater on the posttest time than the pretest.
Table 1 shows the mean scores for pre- and posttest scores for each school
per variable of the LSI. Figure 1 plots error bars for the
results of these selected variables taken at pretest and posttest time
intervals from the two samples of students at the two different
schools. The error bars represent 95% confidence intervals about the mean.
Lack of overlap in the confidence intervals between
schools demonstrates significant difference between schools with respect to
this measure. Lack of overlap in the confidence
intervals from pretest to posttest in one school demonstrates a significant
difference between time intervals for that particular school.
Motivation (Variable 5)
Based on all variables using univariate F tests, AAST and RHS scored
significantly different on the pretest, F(1, 140) = 4.820, p =
.030. AAST students were significantly higher in the Motivation variable
than RHS students on the pretest. Both schools were
significantly different on the posttest, F(1,140) = 4.743, p = .031, with
AAST students being significantly higher than RHS. In looking
at Figure 1, error bars represent 95% confidence intervals about the mean
that there was significant difference within subject for
each school. Motivation was significantly lower at each school from the
pretest to the posttest. In addition, a MANOVA was
performed looking at the within-subjects factor of pre- versus posttest
scores. Significant differences were found for both AAST,
F(1,143) = 452.41, p < .001, and RHS, F(1,143) = 583.68, p < .0005.
Persistence (Variable 6)
Univariate F tests showed that AAST and RHS did not score significantly
different on the pretest, F(1,140) = 1.251, p = .265, or the
posttest, F(1,140) = 1.048, p = .308. However, Figure 1 shows a significant
difference for AAST at the 95% confidence level
between the pretest and the posttest. AAST's scores in this variable were
significantly lower on the posttest than the pretest. RHS's
scores were not significantly lower, although there are suggestions that
the scores did go down. A MANOVA showed a significant
difference from pre- to posttest scores at AAST, F(1,143) = 957.67, p <
.0005, while no significant difference was found for RHS,
F(1,143) = 216.85, p < .050.
Responsibility (Variable 7)
Univariate F tests showed no significant difference between subjects at the
pretest, F(1,140) = .047, p = .829. Both AAST (M =
57.559) and RHS (M = 57.867) scored high in this variable on the pretest.
There was a significant difference between subjects on
this variable on the postest, F(1,140) = 7.092, p = .009. AAST students
scored significantly lower (M = 53.085) than RHS students
(M = 56.639) on this variable. Figure 1 shows a significant within-subjects
difference for AAST students from pretest to posttest in
Responsibility. The MANOVA showed a significant difference from pretest to
posttest at AAST, F(1,143) = 607.50, p < .0005, but
no significant difference for RHS, F(1,143) = 61.20, p < .156.
Working Alone or with Peers (Variable 9)
Univariate F tests showed no significant difference between subjects on the
pretest, F(1,140) = .099, p = .753. There was a
suggestion of a difference on the posttest, although it was not significant
at the .05 level, F(1,140) = 1.678, p = .197. The AAST
scores suggested that they were higher than RHS's on the posttest. There
was no significant difference within subjects for either
group, although there is a suggestion that the AAST scores were higher on
the posttest. A MANOVA showed no significant
difference from pretest to posttest at AAST, F(1,143) = 58.80, p < .214, or
at RHS, F(1,143) = 4.61, p < .727.
Parent Motivation (Variable 21)
Univariate F tests showed significant between-subjects effects on the
pretest, F(1, 140) = 5.452, p = .021, with AAST (M = 48.475)
being significantly lower in this variable than RHS (M = 52.169). There was
no significant difference between subjects on the
posttest, F(1, 140) = .634, p = .427, with RHS students becoming less
parent motivated at the end of the year. Figure 1 shows
significant within-subjects effects for RHS between the pretest and the
posttest, with the scores demonstrating less parent motivation
at the end of the year so that AAST and RHS scores are within a narrow
range at posttest time. The MANOVA showed no
significant difference at AAST from pretest to posttest, F(1,143) = 51.93,
p < .377, but a significant difference for RHS, F(1, 143) =
435.20, p < .004.
Teacher Motivation (Variable 22)
Univariate F tests showed no significant difference between subjects on the
pretest, F(1, 140) = .212, p = .646. There was a
significant difference between AAST and RHS on the posttest, F(1, 140) =
2.791, p = .097, with AAST being much lower than
RHS. Figure 1 shows significant within-subjects effects for AAST between
the pretest and the posttest, with the scores showing
less teacher motivation at the end of the year. There was no significant
effect between pretest and posttest for RHS. The
MANOVA showed a significant difference at AAST from pretest to posttest,
F(1,143) = 452.41, p < .003, but no significant
difference at RHS, F(1,143) = 72.48, p < .226.
Interviews
The following responses were given to interview questions. The responses
reflect what a majority of the respondents quoted to me.
If there were a variety of responses given, these are included below.
How did you like school this year? Both samples said they "liked school a
lot."
What things did you like about this school? Students at AAST said they
liked projects, technology, the material taught, the teachers,
the relaxed atmosphere, lots of choices, the social atmosphere, and the
challenge. Students at RHS said they liked the rotating
schedule so that they do not have classes every day, free periods, the
teachers, meeting new people, sports, classes, and the open
learning environment.
What things did you not like? Students at AAST expressed that they did not
like the long hours, the long commute, the workload, not
being with friends and losing touch with them, the unstructured environment
and not being clear what to do, and the stress over
deadlines. Students at RHS expressed that they did not like some of the
teachers (inflexible, taught things not relevant, boring, strict,
not understanding), they did not like all the lectures, the work was
difficult, and the classes were boring.
In what areas do you feel that you have grown academically? Students at
AAST mentioned math, sciences, and technology.
Students at RHS mentioned math, science, English, world history, and
foreign languages.
In what areas do you feel that you have grown socially? AAST students
mentioned making new friends, meeting new people,
becoming a more confident speaker, becoming more independent, and working
with people better. Students at RHS mentioned
making new friends, meeting new people, becoming more confident, and
participating in sports.
What aspects of this school did you find most difficult? AAST students
mentioned the workload, high expectations of teachers, the
highly competitive environment, working on teams and often not knowing what
to expect, not being able to do sports, and leaving
friends. Students at RHS mentioned the workload, time management,
expectations of teachers, the rotating schedule, and teacher
relationships.
On a scale of 1 to 5 with 5 being the highest, to what extent did you use
technology in your classes? The AAST average was 4.5.
The RHS average was 2.
On a scale of 1 to 5 with 5 being the highest, how often did you work in
teams? The AAST average was 4.5. The RHS average was
3.5.
On a scale of 1 to 5 with 5 being the highest, how stressful was the past
year? The AAST average was 3.5, and the average was
3.5.
What was stressful? AAST students mentioned finishing projects, working
with other people on a team, having all projects due at the
same time, the workload, dealing with new teachers, the long school hours
(from 8:30 a.m. to 4:00 p.m.), and taking three sciences
simultaneously during the year. RHS students mentioned trying to balance
schoolwork and playing sports, completing projects,
maintaining the workload, being involved in extracurricular activities,
adjusting to a new school, and taking midterms and tests.
What would you like to change here? AAST students mentioned the overlap of
concepts being taught in classes, the heavy emphasis
on the sciences with not enough emphasis on the humanities, and teachers
needing to coordinate when projects were due. RHS
students frequently mentioned more use of technology, or they said,
nothing, it's fine here.
How relevant did you find school this year? AAST students said that school
was very relevant, that it was more like the real world
working in teams on projects and learning to apply things. They said e-mail
and technology were very helpful in everyday life, and
that the heavy integration of technology definitely helped. RHS students
said that school "sort of helped for college," but that it was
not directly related to day-to-day life. They said they did not know about
the relevance to the outside world, because that would
depend on which career they will pursue.
Summary of Interviews
As an overall group, the two samples expressed many of the concerns,
frustrations, and insights of typical freshmen in a new high
school who are increasingly becoming more social. Both liked their school,
although their concerns were different, reflecting major
environmental and cultural differences in their schools' climates. AAST
students expressed concern over the emphasis of projects
and how all the projects were due at the same time. They expressed
frustration at the long school days, working in teams, the highly
competitive environment that surrounds them, and the high expectations
placed on them by teachers, parents, and peers. RHS's
concerns were more with the scheduling of the day and the interaction with
specific teachers they found boring, dull, or stimulating.
They expressed concern with projects, grades, time management, and tests
and more tests. An interesting note that emerged in the
interviews with the AAST students was the concern regarding lack of
structure within specific classes and sometimes within the
school itself. Comments were heard about (1) needing to know what to do
when working on projects and (2) coordination between
teachers and among administrators. This comment was never heard from RHS
students. Another interesting point was that AAST
students found technology very motivating, exciting, and relevant to their
lives. RHS students expressed disappointment over the
lack of technology within their subjects. As a result, AAST students saw
great relevance of their education to their everyday life,
while RHS students perceived their schooling as relevant, but only as it
pertained to going to college or pursuing a future career.
DISCUSSION
The LSI
The results from this study suggest that a technology-rich environment that
promotes collaborative, project-based learning can
have an effect on learning style. The two schools were significantly
different in measures of learning style at pretest time; the
difference between the schools on these measures was greater at posttest
time. AAST showed a greater effect on combined
measures from pretest to posttest intervals than RHS did. This suggests
that the environment contributed to the differential in effect
size.
In looking at Variable 5, motivation, both AAST and RHS students'
motivation level decreased equally from the pretest to the
posttest. It is difficult to assess whether this is a function of being a
freshman in each high school studied, a function of the
environment, or a common occurrence that is caused by entering any new high
school. Nevertheless, in this sample of students,
motivation levels decreased at the end of the year. In looking at Variable
6, persistence, the AAST scores were significantly lower
on the posttest than the pretest. RHS's scores were not significantly
lower, although there are suggestions that the scores went
down. Therefore, AAST students had a greater negative effect in the area of
persistence in completing a task. In looking at Variable
7, responsibility, both AAST and RHS scored high in this variable at
pretest time. However, at posttest time, AAST students scored
significantly lower than RHS students on this variable. Looking at these
three variables in the total sample of students, motivation,
persistence, and responsibility significantly decreased at posttest time
for AAST students, while only motivation significantly
decreased at posttest for the RHS students.
The decrease in motivation, persistence, and responsibility for AAST
students could be a direct result of the cultural climate and
academic environment within the school. The teachers use a project-based
constructivist approach to learning. In student
interviews, students responded that working with others on a team and
unclear expectations and goals were stressful. Perhaps the
very nature of constructivism with its unclear goals and outcomes, and an
emphasis on competitive teamwork can have a negative
effect on students' motivation, persistence, and responsibility.
Variable 9, preference for working alone or with peers, shows no
significant difference between groups or within groups for either
AAST or RHS students. There is a suggestion that it increased for AAST
students from pretest to posttest, but it is not a significant
increase. The emphasis on teamwork at AAST would suggest that although
students may find it stressful, they would also grow to
enjoy and appreciate working with peers. No conclusive statement can be
made with regard to this variable except that data suggest
this may be so.
In looking at Variable 21, parent motivation, there was significant
difference between the two schools on the pretest, with RHS
students being more parent motivated. There was no significant difference
between the two schools on the posttest, with RHS
students becoming less parent motivated. There was a significant difference
from pre- to posttest scores for RHS. This could be a
factor of students maturing from middle school youngsters, who are used to
having parents intervene and help with school matters, to
more mature young adults, who now take more responsibility for their own
learning. AAST students travel from a home district to a
distant county school, and they have been preselected as gifted in math and
science. These students might already be more
independent and less parent motivated.
In looking at Variable 22, teacher motivation, the two schools showed very
little difference on the pretest. Scores on the posttest
showed that AAST students were much less teacher motivated than their RHS
peers. This again could be construed as a negative
effect of the constructivist, team-oriented approach in which students are
"turned off" to teachers, or it could be construed as a
positive effect in that AAST students are much more independent at the end
of the year than their RHS peers.
In looking at all six variables studied in-depth, AAST students' learning
styles showed significant change in four of the six variables
during the year of study, and RHS students showed significant changes in
two of the six variables. This study's results suggest that
the school environment can change a student's learning style. This result
needs to be investigated further and brings into question
whether learning styles are biologically and developmentally set or are, in
fact, capable of being manipulated and affected by the
external environment. This study also suggests that an environment that is
actively engaged in many of the reform efforts
promulgated in the literature--such as establishing a technology-rich
school, using constructivist methods of instruction, employing
project-based teams that solve problems, and discouraging the use of
lecture--can have an even greater effect on student learning
style. A major question to be explored is if the change is always in the
direction that is desired or expected. Unintended outcomes of
instituting major reforms in a school need to be examined.
Table 1. Mean Scores for Pretest and Posttest Per School
Legend for Chart:
A
B
C
D
E
F
G
H
I
J
K
L
-
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
Variable
5 Motivation: Pre
5 Motivation: Post
5 Motivation: Pre
6 Persistence: Post
7 Responsibility: Pre
7 Responsibility: Post
9 Alone/Peers: Pre
9 Alone/Peers: Post
21 Parent Motivated: Pre
21 Parent Motivated: Post
22 Teacher Motivated: Pre
22 Teacher Motivated: Post
A
D
G
B
E
H
C
F
I
J
K
L
54.5
49.8
45.8
47.5
50.8
57.6
47.6
49.2
55.3
53.1
48.5
45.7
51.2
51.4
45.4
48.8
47.3
57.9
45.9
49.9
53.8
56.6
52.2
48.4
AAST
RHS
GRAPHS: Figure 1. Error barter the results of selected variables of the LSI
pretest and posttest.
References
Brooks, J., & Brooks, M. (1993). The case far constructivist classrooms.
Alexandria, VA: Association for Supervision and
Curriculum Development.
Cohen, V. (1997). Learning styles in a technology-rich environment. Journal
of Research on Computing in Education, 29(4),
338-350.
Dunn, R. (1990). Understanding the Dunn and Dunn learning styles modal and
the need for individual diagnosis and prescription.
Reading, Writing, and Learning Disabilities, 6, 223-247.
Dunn, R., Beaudry, J., & Klavas, A. (1989). Survey of research on learning
styles. Educational Leadership, 46(6), 50-58.
Dunn, R., Dunn, K., & Price, G. (1989). Learning style inventory manual.
Lawrence, KS: Price Systems.
~~~~~~~~
By Vicki L. Cohen, Fairleigh Dickinson University
Dr. Vicki Cohen is an assistant professor in the School of Education at
Fairleigh Dickinson University. Her areas of research are in
literacy and technology in education. (Address: Dr. Vicki L. Cohen, School
of Education, Fairleigh Dickinson University, NJ 07666;
cohen@fdu.edu.)
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Title: Temperament-based learning styles as moderators of academic
achievement.
Subject(s): LEARNING strategies; ACADEMIC achievement
Source: Adolescence, Spring97, Vol. 32 Issue 125, p131, 11p, 1 chart
Author(s): Horton, Connie Burrows; Oakland, Thomas
Abstract: Examines the hypothesis that students learn best when taught
using strategies consistent with their temperament-based learning style.
Definition of learning styles; Use of analyses of covariances in hypothesis
examination; Examinations of temperament-based learning styles.
AN: 9705295918
ISSN: 0001-8449
Database: Academic Search Premier
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TEMPERAMENT-BASED LEARNING STYLES AS MODERATORS OF ACADEMIC ACHIEVEMENT
ABSTRACT
Considerable interest in applications of temperament theory has led to
proposals of four temperament-related learning styles. The hypothesis that
achievement is higher when instructional strategies utilize methods
consistent with students' preferred learning styles was tested using 417
seventh graders, the majority of whom were from minority and low SES
families. The hypothesis was not supported; instead, student achievement
was significantly higher with instructional strategies designed to promote
personalized learning. The need to extend temperament-based learning styles
by considering additional qualities that are important to learning is
discussed.
Considerable research in education and psychology has been directed toward
identifying the effects of individual differences in learning styles.
Learning theorists generally agree that curriculum and instructional
strategies should be adapted to these aptitudes.
Learning styles have been defined as physiological, cognitive, and
affective behaviors that serve as relatively stable indicators of how
learners perceive, interact with, and respond to learning environments
(Keefe, 1987). Thus, learning styles are thought to be stable and enduring
personal qualities and not easily acquired (Derry & Murphy, 1986). As noted
in Keefe's definition, literature of learning styles has centered on three
main qualities thought to be critical: physiology (e.g., Das & Malloy,
1984; Eppele, 1989; Kane, 1984; Keefe, 1987; Levy, 1984; Millard & Nagle,
1986; Polce, 1987; Shannon & Rice, 1983; Sinatra, 1982; Webb, 1983),
cognition (e.g., Bertini, 1986; Brennan, 1982; Das & Malloy, 1981;
Goodenough, 1986; Kane, 1984; Keefe, 1987; Korchin, 1986; Messick, 1976;
Polce, 1987; Witkin, Moore, Goodenough, & Cox, 1977), and affect (e.g.,
Carrol, 1963; Haring, 1985; Keefe, 1987).
Several ways have been proposed that examine learning styles in terms of
their conceptualized physiological, cognitive, and affective components.
Research designed to study the efficacy of learning style applications
generally consider relatively narrow components (e.g., field dependence)
within the context of aptitude-treatment interactions (ATI). General
support for ATI is lacking (Cronbach & Snow, 1977; Reynolds, 1981; Snider,
1990), and there are few empirically supported guidelines to assist in
grouping students for instructional purposes. Moreover, a meta-analysis of
studies on learning style applications reports little or no achievement
gains when instruction methods match learning modalities (Kavale & Forness,
1987).
Despite this somewhat pessimistic view, considerable interest remains in
uncovering possible applications of learning styles defined in broader
ways. Previous research can be criticized for conceptualizing these styles
too narrowly, thus minimizing opportunities to test fully the effects of
broader and more encompassing learning styles. Some believe temperament
provides this broader perspective. Although the early contributions of
Hippocrates and Galen often are cited, modern interest dates to Jung's
writings (e.g., Psychological Types (1923). The popularization of
temperament type by Myers and Briggs (Myers & McCaulley, 1985) has
generated considerable interest among educators and psychologists. Myers
and Briggs operationally define temperament through four dichotomous
traits: extraversion (E) and introversion (I), sensing (S) and intuition
(N), thinking (T) and feeling (F), and judging (J) and perceiving (P).
Keirsey and Bates (1984) describe four basic temperaments that can be
derived from the interaction of these types of traits, each temperament
having its own primary or core value. SJ students primarily value belonging
through providing service to others (e.g., they value following traditions
and acting responsibly and conservatively). SPs primarily value personal
freedom and spontaneity (e.g., to act on their impulses, to play, and to be
free of constraints). NTs primarily value competence (e.g., a desire to
learn, to know, to predict, and to control). NFs primarily value personal
growth (e.g., to develop fully as individuals, to display authentic
integrity, and to promote harmony).
Golay (1982) and others (e.g., Lawrence, 1982) extended type and Keirseian
temperament theory by describing prominent learning styles exhibited by
students displaying these four temperament types. SJs were described as
learning best when curricular materials were concrete and instruction well
planned and routine (e.g., using repetition and drill through step-by-step
instructions). SPs were thought to learn best through strategies that
highlight variety, action, and entertainment. NT students were described as
interested in developing theories and concepts and preferring strategies
that promoted discovery and experimentation. NF students were thought to be
interested in determining the relevance of learning to their personal lives
and the lives of those important to them, and preferred strategies that
emphasized cooperation and personalized applications of learning.
Despite considerable interest in learning styles derived from temperament,
few studies appear in quality refereed journals that examine the efficacy
of these applications. The purpose of this study was to test the hypothesis
that students learn best when taught using strategies that are consistent
with their temperament-based learning style.
METHOD
Subjects
Four hundred seventeen seventh graders enrolled in social studies classes
in a large metropolitan district of approximately 65,000 students comprised
the sample. Approximately 35% were Mexican-American, 23% were AfricanAmerican, and 42% were Caucasian; approximately 50% were from low-income
families and qualified for the free lunch program.
Instruments
The Myers-Briggs Type Indicator (MBTI), a 126-item forced choice
questionnaire, was used to assess four dichotomous dimensions: Extraversion (E)-Introversion (I); Sensing (S)-Intuition (N); Thinking
(T)Feeling (F); Judging (J)-Perceiving (P). The reliability coefficients of
the MBTI for middle school students generally is in the high .70s while
test-retest studies over 12 months found consistency on each scale also to
be in the .70s (Myers & McCaulley, 1985). Temperament classification
percentages of students in this study are: 7% NF, 17% NT, 49% SP, 27% SJ) - approximate national estimates as reported by Golay (1982) and Keirsey
(1984).
Criterion-referenced measures of Texas history also were employed to assess
content acquisition from two instructional units. All pre- and post-tests
were developed to assess the instructional goals as set forth in the
teacher's edition of Texas, Our Texas and were derived from items contained
in this volume.
Procedure
Four teachers received in service training on temperament and temperamentbased learning styles through readings and attending four dydactic training
sessions. They also completed the MBTI and received information regarding
the implications of their own temperament on their teaching and learning
styles.
Following training, each teacher was assigned to write lesson plans, along
with the senior author, for one of four instructional strategies associated
with temperament. The plans were designed to be consistent with
instructional strategies and lesson plans that Golay (1982) and Keirsey and
Bates (1984) describe for each of four temperaments: Sensing and Judging
(SJ), Sensing and Perceiving (SP), Intuitive and Thinking (NT), and
Intuitive and Feeling (NF). Instructional strategies important to each of
the four types are described below. When possible, teachers were to develop
lessons using the instructional strategy which matched their own
temperament. The first set of lessons, a six-day unit on Texas explorers
was based on Chapter 5 of the Texas, Our Texas social studies text. The
second set of lessons, a seven-day unit on Texas colonization, was based on
Chapter 9 of the text.
SJ lessons were designed to encourage attention to detail, conformity, and
obedience. Loss of structure or expectations of spontaneous participation
were avoided. Teachers reinforced conventional thinking that was consistent
with information presented in the text.
SP lessons encouraged performance, playfulness, and fun, avoiding quiet
seatwork or boring routines. Teachers using this strategy reinforced
participation, involvement, and spontaneity.
NT lessons were designed to encourage independent thinking, problem
solving, and strategizing. Lessons avoided redundancies, inefficiencies,
and an overemphasis on detail. Teachers using this instructional strategy
reinforced competence as well as good ingenious ideas.
NF lessons were designed to encourage cooperation, personal application,
and identification with the historical characters. The lessons avoided
competition and overemphasis on detail. Teachers reinforced unique or
creative ideas, personal growth, and expression of personal experiences and
feelings.
The students' social studies grades for the preceding six-week grading
period, prior to introducing Chapter 5, were collected. The first phase of
the study began at the start of the second six-week grading period.
Students were given a pretest on knowledge important to the Chapter 5
social studies lesson regarding Texas explorers. Following the six days of
instruction, students were given a posttest on the material.
Students remained in their assigned social studies classes and were taught
using a single instructional strategy for the six-day unit. Different
instructional strategies were used by the teachers for other classes during
the school day. One teacher utilized each of the four instructional
strategies in each of her four class periods. Two of the teachers utilized
each of the four instructional strategies in four classes and repeated one
instructional strategy in a fifth class. The fourth teacher had only two
seventh grade social studies classes and used two different methods.
This process was repeated during the second phase of the study which began
at the start of the third six-week session and was based on Chapter 9 of
the text. To ensure treatment integrity, the first author completed
periodic observations of all four teachers, verifying that their
instruction was consistent with the curricula developed for the study.
RESULTS
The purpose of this study was to determine whether students demonstrate
higher levels of achievement when they received social studies instruction
through a teaching style designed to match their temperament-based learning
styles. Analyses of covariances, using pretests as covariates were used to
examine the hypothesis.
The type of instructional strategy used by the teachers significantly
affected achievement among SJ students during both the first [F (3,106) =
15.53, p < .001] and second [F (3,112) = 4.44, p < .01] units of
instruction, among SP students during both the first [F (3,196) = 21.28, p
< .001] and second [F (3,102) = 5.95, p < .001] instructional units, among
the NT students during the first [F (3,68 = 4.37, p < .01] but not the
second [F (3,62) = .51, p = 68, n, s] instructional units, and among NF
students during both the first [F (3,29) = 3.60, p < .05] and second [F
(3,25) = 5.18, p < .01] units of instruction. Students exhibited
significantly higher achievement when NF instructional strategies were used
(see Table 1).
Teacher Effects
The study also explored possible teacher effects, which were examined
through ANCOVA. Students' grades for the first six-week grading period and
the pretest scores from each unit were used as covariates. Teacher was the
independent variable and posttest score was the dependent variable. Results
were significant in both the first [F (3,335) = 14.56, p < .001] and second
[F (3,350) = 26.16, p < .001] units of instruction. The proportion of
variance accounted for by teacher (9% for unit one and 13% for unit two)
reveals that, while significant, the teacher effects did not account for a
large proportion of the variance even as compared to covariates.
Additionally, the implications of teacher effects on the primary hypothesis
were minimal in that the teachers whose students demonstrated the highest
level of achievement utilized all four instructional strategies. Moreover,
all four teachers used the NF method.
DISCUSSION
This study tested assertions made by Keirsey and Golay about relationships
between achievement and learning styles based on student temperament. The
findings provide little empirical support for their theory that achievement
is improved among students who receive instruction that utilizes teaching
strategies which match their temperament-based learning styles. The results
of the current study, combined with the lack of empirical support by
Keirsey and Golay in their own work, together with the paucity of empirical
investigation by others, weakens this assertion.
The present results may be explained if one supports the position that
temperament is only one personal attribute that influences achievement.
Temperament theorists may have become too simplistic in viewing temperament
as the basis of learning/teaching styles and have neglected to integrate
other important schools of thought including learning and developmental
theories, cultural concerns, and cognitive abilities. Learning style should
not be the only factor considered in the design of instruction (Doyle &
Rutherford, 1984). Other variables, including students' age and stage of
development, must be considered (Gregorc, 1979).
In the present study, the NF teaching strategy, designed to personalize
learning, was superior in facilitating achievement among students of all
four temperament types. Current temperament theory cannot fully explain
these results. A simple univariable explanation may not be possible due to
the multiple factors involved; however, the statistical and practical
significance of this finding should not be ignored. Reasons why students
learn more when taught with a personal approach may be attributable to many
factors. Theories of learning and development as well as acknowledgment of
cultural sensitivities may provide useful conceptual frameworks for
understanding these findings.
The personal teaching strategy employed a variety of techniques designed to
enable students to relate to the lessons in personal ways. For example,
students completed a visualization exercise in which they imagined having
the experiences of an early Texas explorer, including the feelings,
motivations, and sensory input the person may have experienced. Students
also made diary entries in the first person as if they were a famous person
during that historic period. In addition, class discussions focused on
relating to the characters, imagining what it would have been like to have
had their experiences and the ways students today are similar to those
historical figures.
Learning theorists also have argued that such personalized approaches can
enhance achievement. Schema theory offers some important explanations of
these results. "A schema is defined as an abstract data structure which
consists of the concepts, relations (conceptual, temporal, and spatial),
and related information that apply to a particular concept, event, or other
data set" (Siebold, 1989, p. 53). Schema theory suggests that students'
understanding of new material is dependent on previous experiences, the
extent of their world knowledge, and the way in which these experiences
interact with the explicit new information (Smith & Smith, 1986). Prior
knowledge and experience with the instructional materials decidedly
influence learning and achievement (Cooper, 1989).
Personalized lessons used prior knowledge to help students develop schemata
at two levels. Lectures and new materials were related to students'
previous experiences. For example, students typically were asked to write
about personal events in their lives. Thus, when asked to write diaries in
the first person as an explorer, the concept was not completely new. In
another class exercise, the personalities of historic characters were
compared to those of movie stars with whom the students were familiar.
Thus, new material was introduced in a way that enabled students to use
their prior knowledge to further develop their schema.
Further, the class exercises provided additional world experiences through
visualization. These experiential activities had a memorable component.
Thus, when questions were asked on the test, students may have been better
able to draw on their prior knowledge and class experiences; students were
likely to be better able to use the schemata they had developed prior to
class, to add to that schemata through additional class experiences, and to
draw on the more developed schemata when asked to recall information on the
posttests.
Developmental theory also may provide useful insights into the
understanding of these results. Because of their personalizing qualities,
the NF lessons may appeal strongly to adolescent narcissism. Since many
adolescents are prone to egocentric thinking (Kimmel & Weiner, 1985)
lessons which are personally focused may capture and hold their attention
during this developmental stage and thus facilitate acquiring and retaining
information. Thus, both age and stage of development are critical factors
in considering learning styles (Gregorc, 1979).
Finally, Trueba (1988) and Rameriz (1982) have underscored the need for
more humane learning environments for minority students. As previously
noted, the majority of students in this study were minority; half were from
economically distressed homes. Personal, feeling-oriented lessons may
provide more nurturing qualities which facilitate achievement in minority
students. Lessons which encouraged them to relate the new material to
personal experiences and feelings also may help those from diverse cultural
backgrounds to sustain interest since they were able to relate their
personal qualities, history, and backgrounds in ways that valued their
diversity. Additionally, the exercises in the NF strategy (e.g., imagine
being a Texas explorer or assume the role of an 1830s colonist) may provide
a welcome respite for students from families experiencing financial and
other stressors.
In sum, while some temperament qualities may contribute importantly to how
students learn, schema theory, developmental considerations, and cultural
sensitivities also should be considered when developing lesson plans
designed to optimally reach students.
CONCLUSIONS
This study provides one of the few empirical examinations of temperamentbased learning styles. While support was not found for using instructional
strategies that match students' temperament-based learning styles, results
did indicate that a strategy which capitalizes on personalization was
superior for students of all types. Thus, it is clear that in addition to
temperament, such factors as type of instruction, teachers, learning theory
principles, developmental concerns, and cultural issues have an impact on
achievement and attitudes. Temperament theorists are therefore encouraged
to integrate, or at least acknowledge, these other schools of thought in
their conceptualizations.
Reprint requests to Connie Burrows Horton, Ph.D., Department of Psychology,
Illinois State University, Normal, IL 61790.
Table 1. Means and Standard Deviations for Achievement
Scores for Four Temperament Types by Four
Instructional Strategies
TEMPERAMENT-BASED INSTRUCTIONAL STRATEGIES
Temperament (SJ) (SP) (NT) (NF)
N
X
X
SD
SD
X
X
SD
SD
p value
SJ Unit 1
112
58
56
22
16
83
54
16
15
<.001
NF>SJ,SP,NT
Unit 2
118
75
78
13
12
88
71
13
15
<.001
NF>SJ,SP,NT
SP Unit 1
102
67
55
11
22
87
60
15
14
<.001
NF>SJ,SP.NT
Unit 2
108
72
76
18
12
88
70
11
14
<.001
NF>SJ,SP,NT
NT Unit 1
74
62
63
19
18
86
66
14
18
<.001
NF>SJ,SP,NT
Unit 2
68
78
84
19
12
88
79
16
16
NS
NF Unit 1
35
52
64
23
19
87
58
17
11
<.001
NF>SJ,SP,NT
Unit 2
31
71
85
18
14
96
73
7
16
<.001
NF>SJ,SP,NT
SJ: Sensing-Judging; SP: Sensing-Perceiving; NT: Intuitive-Thinking; NF:
Intuitive-Feeling
Unit 1 refers to the first phase of the study (Chapter 5 of the Texas, Our
Texas text) which included 323 students.
Unit 2 refers to the replication of the study (Chapter 9 of the Texas, Our
Texas text) which included 335 students.
Scores are based on a possible 100 and were rounded to the nearest whole
numbers.
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~~~~~~~~
By Connie Burrows Horton and Thomas Oakland
Thomas Oakland, Ph.D., Professor, Department of Educational Psychology.
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Title: Towards a categorisation of cognitive styles and learning sytles.
Subject(s): COGNITIVE styles; LEARNING strategies; EDUCATION -- Evaluation
Source: Educational Psychology, Mar-Jun97, Vol. 17 Issue 1/2, p5, 24p, 2
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Author(s): Rayner, Stephen; Riding, Richard
Abstract: Editorial. Discusses the origin and elaboration of learning
style as a concept tracing the influence of a cognition and a learningcentered approach to the psychology of individual difference. Model of
cognitive steel featuring the verbal-imagery cognitive dimension; Model of
cognitive style integrating the wholist-analytic and verbal-imagery
cognitive dimensions; Application of the learning style in educational
practice.
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Section: EDITORIAL ARTICLE
TOWARDS A CATEGORISATION OF COGNITIVE STYLES AND LEARNING STYLES
ABSTRACT
This paper considers the construct, `style', in the study of individual
differences and learning. The origin and elaboration of learning style as a
concept is discussed, tracing the influence of a cognition and a learningcentred approach to the psychology of individual difference. The authors
argue that a contemporary overview of style can contribute to a
rationalisation of the theory and facilitate a greater application of
learning style in educational practice. A case is made for the need to
integrate more fully various models of style into a single construct of
learning style.
The concept `style' is used in a variety of contexts, in high street
fashion, the sports arena, the arts, the media and in many academic
disciplines including educational psychology. It has a wide appeal which
reflects an enduring versatility, but this same appeal can lead to overuse
and often creates a difficulty for definition and understanding. The
concept is nevertheless always associated with individuality and is
invariably used to describe an individual quality, form, activity or
behaviour sustained over time.
The term style may be used, for example, to describe the grace of a
gymnast, or the game of a football team, the manner and cut of a new
fashion on the modelling catwalk, the approach used by a commercial company
to organise itself, or even the way a person may think, learn, talk or
teach! The concept style represents a distinct notion of coherent
singularity--in a variety of context--and might well reflect the need for a
sense of identity which is arguably the essence of individuality.
The `Style' Construct
A `style construct' appears in a number of academic disciplines--in
psychology it has been developed in a number of different areas, for
example: personality, cognition, communication, motivation, perception,
learning and behaviour. Its emergence as a theory entails both separate and
related development, reflecting both a philosophical and psychological
concern for individuality, but, as a result, reinforcing difficulty in
definition or an accepted nomenclature.
Several writers have provided an account of the origin of style in
cognitive psychology. Martinsen (1994) cited Vernon (1973) when he claimed
that antecedents of style can be traced back to classical Greek literature.
Martinsen (1994) and Riding (this issue) referred to James' conception of
individual differences contributing to the style construct James, 1890).
Riding (this issue) also referred to the work of Galton (1883), but more
significantly pointed to the work of Bartlett (1932), who continued with
research on individual differences in cognition. Riding and Cheema (1991)
and Grigerenko and Sternberg (1995) agree that Allport (1937), in work
which developed the idea of `life-styles', was probably the first
researcher to deliberately use the `style' construct in association with
cognition. For a working definition of style, Riding and Cheema (1991),
Miller (1987, 1991) and Riding (this issue) cited Tennant's (1988)
description of cognitive style as a person's typical or habitual mode of
problem solving, thinking, perceiving and remembering.
Vernon (1963) provides an early critique of cognitive style, tracing its
development from work carried out by German `Gestalt' psychologists. She
explained that subsequent work on style flowed from a "considerable number
of experiments ... devoted to studying individual differences in
perception" (1963, p. 221). Vernon, generally, was critical of style
development in the psychology of perception, pointing to a serious problem
with the style construct, which many writers were subsequently to repeat.
She commented that cognitive style had largely evolved from theories
generalised on single experiments and little empirical evidence. One of the
aims of this article is to consider such a deficiency.
An Overview of Style Development
Grigerenko and Sternberg (1995) described three distinct traditions of
`style-based work in psychology'. The first is called the `cognitioncentred approach', the second the `personality-centred approach' and the
third the `activity-centred approach'. Two of these `traditions' correspond
to periods of active development in work on `style'. The first, occurring
mostly in a 30-year period beginning in the 1940s, involved the development
of `cognitive styles', which reflected the work of experimental
psychologists investigating the area of individual differences in cognition
and perception. The second began in the 1970s and involved the activitycentred theories of learning style associated with educationists addressing
environmental and process-based issues related to meeting individual
differences in the classroom. The latter is called the `learning-centred
approach' in this discussion to emphasise the educational perspective
shared by researchers in this tradition. The personality-centred approach
is not considered in this review, partly because there is little evidence
of this tradition influencing the general development of style-based theory
and, secondly, there exists only the Myers-Briggs style model which clearly
and significantly incorporates a personality-centred approach (Myers,
1978).
(A) The Cognition-centred Approach
An early interest in style amongst cognitive psychologists was given
impetus, according to Grigerenko and Sternberg (1995, p. 207) by
frustration with research on ability and intelligence which failed to
"...elucidate the processes generating individual differences". Research
carried out by various workers focused upon cognitive and perceptual
functioning, resulting in the identification and description of several
`abilities', `styles' and `dimensions' of cognitive processing or cognitive
style.
Key work in this cognition-centred approach is shown in Table I. The
categorisation of the models described below have been made on the basis of
an identification of fundamental dimensions of cognitive style which builds
upon the work of Riding and Cheema (1991). They proposed an integration of
`style' models into two cognitive style families: the Wholist-Analytic and
the Verbaliser-Imager. The model of cognitive style proposed by Riding and
Cheema (1991) and Riding (1991) argued that two fundamental dimensions of
cognitive style structure the way in which people, firstly, process
information and take the whole view or see things in parts (WholistAnalytic dimension); and, secondly, represent information or thinking
either in pictures or words (Verbal-Imagery dimension). A third group of
`learning style' models was identified, some of which were associated with
a learning-centred approach to individual differences and considered to
describe more properly models or aspects of learning strategies and so left
outside the cognitive style construct (Riding & Cheema, 1991, p. 196).
If we consider the cognition-centred tradition, it is possible to identify
several models of cognitive functioning which appear to stand centrally in
the development of a theory of cognitive style. They include the following
areas of research and development and are organised into three groups: work
which relates to the Wholist-Analytic style dimension; work which relates
to the Verbal-Imager dimension of cognitive style; and, finally, more
recently developed models which reflect a deliberate attempt to integrate
both fundamental dimensions of cognitive style.
Models of Cognitive Style Featuring the Wholist-Analytic Cognitive
Dimension
Perceptual-Functioning
Workers led by Witkin and Asch (1948a; b) focused initially on perception,
as they identified differences in individuals when locating an upright
object in space. Their work reflected earlier research into perception
completed by the Gestalt school of German psychology. Further experiments
led to the discovery of field-independence and field-dependence as a
perceptual style. The early rod and frame test used to measure field
dependency was refined and converted into a pencil and paper assessment,
the Embedded Figures Test (EFT). This development again reflected earlier
work on the discrimination of shape carried out by Thurstone (1944).
Assessment of field dependency was further developed to include the Group
Embedded Figures Test (GEFT). All three tests measured the ability of
subjects to `dis-embed' a shape from its surrounding field. The theory was
extended to involve a range of functions related to perception called
psychological differentiation (Witkin et al., 1962; Witkin, 1964; Witkin et
al., 1971; Witkin & Goodenough, 1981).
Later studies focused on field dependency in children and learning (Witkin
et al., 1977). Field-independent children were found to have a greater
capacity than field-dependent children for `active analysis' and perceptual
`differentiation'. They were more likely to prefer independent activity,
have self-defined goals, respond to intrinsic reinforcement and prefer to
structure or restructure their own learning. They were also most likely to
develop their own learning strategies. Field-dependent children were found
to have a preference for learning in groups, interact more frequently with
peers or with the teacher, need higher levels of extrinsic reinforcement
and direction, and stated performance goals or established structure in an
activity.
Numerous experiments replicating an exploration of this `style' of
perceptual field response are reported in the literature. A typical example
of such research is the use of field dependency as a basis for
investigating the effect of matching or mismatching teachers and pupils
with specific field-dependent or independent cognitive style (Saracho &
Dayton, 1980; Renninger & Snyder, 1983; Saracho, 1991).
Impulsivity-Reflectivity
This dimension was originally introduced by Kagan and co-workers (Kagan et
al., 1964) and measured by the Matching Familiar Figures Test (MFFT). This
style dimension derived from earlier work investigating conceptual tempo
which measured the rate at which an individual makes decisions under
conditions of uncertainty. Learners fell into two distinct categories: the
first were those who reached a decision quickly after a brief review of
options and were labelled `cognitively impulsive'; the second were those
who would deliberate before making a response, carefully consider all
options and were labelled `cognitively reflective'.
Implications for the teaching and learning process are immediately obvious,
and Riding and Cheema (1991, p. 199) argued that this aspect of cognitive
functioning holds for tasks involved in both academic and non-academic
learning.
Convergent-Divergent Thinking
This dimension of the intellect was proposed by Guilford (1967). The
dimension reflects a type of thinking and associated strategies for problem
solving. The learner will typically attack a problem or task by `thinking'
in a way which is either open-ended and exploratory, or close-ended and
highly focused. The theory was further developed by Hudson and its
implications for the process of teaching and learning explored (Hudson,
1966; 1968). This construct had significant impact upon teacher training
throughout the 1970s.
Holist-Serialist Thinking
This label was introduced by Pask and Scott (1972) as two competencies
which reflected an individual tendency to respond to a learning task either
with a holistic strategy, which is `hypothesis-led', or a focused strategy
which is characterised by a step-by-step process and is `data-led'. This
work by Pask led to a development of `conversational theory', which
emphasised the utility of the learner to `teach-back' learned material
(Pask, 1976).
The Style Delineator (Gregorc, 1982)
Gregorc's learning style construct maintains that an individual learns
through concrete experience and abstraction either randomly or
sequentially. Gregorc identified four styles of learning: concrete
sequential learners who prefer direct, step-by-step, orderly and sensorybased learning; concrete random learners who rely upon trial and error,
intuitive and independent approaches to learning; abstract sequential
learners who adopt an analytic, logical approach to learning and prefer
verbal instruction; and abstract random learners who approach learning
holistically, visually and prefer to learn information in an unstructured
experiential way. This model, although placed in the cognition-centred
approach because it is likely that Gregorc's construct reflects the
Wholist-Analytic dimension of cognitive style, might arguably sit equally
well in the learning-centred approach (Curry, 1987; Griggs, 1991). It is
interesting to note, too, that Grigerenko and Sternberg (1995) prefer to
describe this model as part of a personality-centred approach to style.
The Style Delineator is a self-report measure made up of 40 words in which
the respondent is asked to rank each time which word best describes their
self-perception as a thinker and learner. The measure indicates the
position an individual occupies in the `bi-dimensional channels' of
"learning preferences for making sense of the world through the perception
and ordering of incoming information" (Jonassen & Grabowski, 1993, p. 289).
The Assimilator-Explorer (A-E) Cognitive Style (Kaufmann, 1989)
Kaufmann's work flowed from an interest in problem solving and creativity.
He identified two groups of problem-solvers, assimilators and explorers,
and extrapolated an A-E theory of cognitive style to apply to problemsolving behaviour. Kaufmann developed an A-E Inventory, a 32-item forced
choice self-reporting questionnaire, in which items described dispositions
towards cognitive `novelty-seeking against familiarity-seeking'. Explorers
reflected a higher score on the bi-polar continuum. The instrument was
organised to reflect three factors: novelty against structure seeking, high
against low ideational productivity and opposition against preference for
structure. Martinsen (1994) has continued work in this area, specifically
with respect to the relationship between cognitive style, insight and
motivation in the process of problem solving.
The Adaptor-Innovator Cognitive Style (Kirton, 1976; 1994)
Kirton argued that style relates to the preferred cognitive strategies
involved in personal response to change, and the strategies associated with
creativity, problem solving and decision-making. A second key assumption
made by Kirton was that these strategies were related to numerous aspects
(traits) of personality that appear early in life and were particularly
stable, like cognitive style. The dimension, Adaption-Innovation, was
understood to exist early in an individual's cognitive development and to
be `stable over both time and incident'. The adaptor, therefore, generally
has a preference for `doing things better', while the innovator will tend
to like `doing things differently'. A useful table, listing the
characteristics of each style dimension, is given in Kirton, (1994, pp. 1011). Kirton's A-I theory, in summary, advanced a style construct which is
bi-polar and consists of the adaptor-innovator continuum.
The assessment instrument developed by Kirton to measure the adaptorinnovator continuum was the Kirton Adaptor-Innovator Inventory (KAI), a
self-reporting inventory originally designed for adults with experience in
the work-place and life. Kirton provides a summary of studies utilising
factor analysis to support the reliability and validity of the instrument
(1994, pp. 14-19), which, in turn, is corroborated by other writers (Clapp,
1993; Taylor, 1994; Van der Molen, 1994). The KAI produces a score which
Kirton claims is used to identify an individual's preferred cognitive
style, that is, as an adaptor or an innovator.
The Cognitive Style Index (CSI) (Allinson & Hayes, 1996)
The CSI is aimed at the "...generic intuition-analysis dimension of
cognitive style" (Allinson & Hayes, 1996, p. 119). The authors have argued
that utility of instrument is essential for the operationalisation of
cognitive style in a professional context (in this instance, a business
management context), and the CSI is designed to further research and
development of style in management practice. While the CSI does not purport
to produce a `full' measure of cognitive style, it is focused on a single
universal dimension, which, Allinson and Hayes (1996) argue, reflects the
duality of `human consciousness'--and problem-solving responses which are
either intuitive or analytic.
The CSI is a self-report questionnaire. It is relatively short and produces
a score that reflects an individual's position on an analytic-intuitive
continuum, which, the authors argue, reflects the super-ordinate dimension
of cognitive style. The construction of the questionnaire is described in
some detail by Allinson and Hayes (1996), as part of an attempt to identify
a unitary construct of cognitive style and operationalise the same
construct in the professional context of business management.
A Model of Cognitive Style Featuring the Verbal-Imagery Cognitive Dimension
Verbal-Visual Representation
An interest in the mode or manner of thinking and knowing has involved a
concern for imagery since early work by Galton (1883). Riding and Cheema
(1991) described the early work of Bartlett (1932) and the development of
Paivio's `dual-coding theory' as the basis for further work investigating
the nature of a Verbaliser-Imagery dimension in the cognitive process
(Paivio, 1971). Several assessment measures have subsequently been
developed which incorporate this feature as a fundamental dimension of
cognitive style (Riding & Taylor, 1976; Richardson, 1977; Riding & Calvey,
1981; Kirby et al., 1988; Riding, 1991).
A Model of Cognitive Style Integrating the Wholist-Analytic and VerbalImagery Cognitive Dimensions
Cognitive Styles Analysis (CSA) and Learning Style (Riding, 1991)
Riding's work is dealt with more fully in Riding (1991; this issue) but it
is worth noting that its development reflects a synthesis of previous work
in cognitive style and it deliberately sets out to integrate fundamental
elements of style theory in the development of a learning style model (see
Riding & Cheema, 1991; Riding & Rayner, 1995). The Cognitive Styles
Analysis is a computerised measure which reveals an individual's tendency
to think visually or verbally and to process information wholistically or
analytically (Riding, 1991; 1994).
Summary Evaluation. The impact of the cognition-centred tradition has
varied greatly and much of it attracted a great deal of criticism for a
lack of rigour or reliability (Vernon 1963, 1973; Sternberg, 1987). Later
commentators have repeated this criticism, questioning in particular the
proliferation of style constructs and measures which occurred as part of
this movement while offering little or no psychometric rigour (Freedman &
Stumpf, 1980; Curry, 1987; Tiedemann, 1989; Grigerenko & Sternberg, 1995).
What is significant in more recent work on cognitive style is the attempt
to clarify a coherent theory of cognitive style (Curry, 1983, 1987; Miller,
1987; Riding & Cheema, 1991; Grigerenko & Sternberg, 1995). There is also
evidence of a growing desire to apply the theory in a variety of
professional context and this is reflected in the development of constructs
tied to a specific measure forming a basis for its operationalisation.
Indeed, it is perhaps the latter trend which led to the emergence of the
learning-centred tradition of style theory.
(B) The Learning-centred Approach
This approach is arguably distinguished by three major features: the first,
a greater interest in the impact of individual differences upon pedagogy;
the second, the development of new constructs and concepts of learning
style; and the third, the presentation of an assessment instrument as a
foundation for the exposition of theory. Key work in this learning-centred
approach is shown in Table II. It is organised into three style groups
which reflect common features pointing to the measurement and
conceptualisation of a particular dimension of the learning process.
It is important to note at this point that the following models are
regarded by the authors as key examples of constructs which might
contribute to the clarification and development of learning style theory.
The list is therefore not comprehensive and a wider review of learningcentred models of assessment may be found in Curry (1987).
Secondly, workers in the learning-centred approach very often use the term
`learning style', but this is in a strict sense different to the definition
expressed by Tennant (1988) and adopted by other workers in the cognitioncentred approach (Riding & Cheema, 1991; Kirton, 1994). The categorisation
of style groups is made on the basis of identifying shared features which
may point to additional fundamental dimensions of `learning style' that may
be integrated with those dimensions previously described in our review of
the cognition-centred approach.
Process-based Models of Learning Style
Experiential Learning Style (Kolb, 1976; 1984)
Kolb's learning style construct consists of two dimensions: perceiving and
processing; the first describes concrete and abstract thinking; the second
an active or reflective information-processing activity. These dimensions
are integrated to form a model describing four types of learning style
which are:
--divergers: learners who typically perceive information concretely and
process it reflectively, and who need to be personally engaged in the
learning activity;
--convergers: learners who perceive information abstractly and process it
reflectively, and who need to follow detailed, sequential steps in thinking
in a learning activity;
--assimilators: learners who perceive information abstractly and process it
actively, and who need to be involved in pragmatic problem solving in a
learning activity;
--accommodators: learners who perceive information concretely and process
it actively, and who need to be involved in risk-taking, making changes
experimentation and flexibility in a learning activity.
Kolb's theory proposes that learning reflects the structure of a four-stage
experiential learning cycle, which, in turn, involves the previously
described aspects of learning style. The experiential cycle is used to
extrapolate four adaptive learning modes: concrete experience (CE),
reflective observation (RO), abstract conceptualisation (AC) and active
experimentation (AE). Learning style is therefore construed as an
individual's preferred method of `learning'. Interestingly, Kolb's model
appears to presuppose a mix of `hard-' and `soft-wiring' in an individual's
learning style, but lends greatest weight to the developmental nature of
learning ability and style. The model therefore reflects a less stable set
of individual differences, which can change over time. This is perhaps not
surprising, given Kolb's primary interest in experiential learning and
process-bound learning theory.
The Learning Style Inventory is a nine-item self-reporting questionnaire
which forces the respondent to rank four words, thereby revealing a
specific preference in the identified modes of learning. Two scores are
calculated, reflecting positions along each of the learning style
dimensions: the first is the AC-CE continuum, which shows the degree to
which the individual's style is biased toward abstraction or concreteness;
the second continuum, RO-AE, shows the degree to which the individual's
style is biased towards reflection or activity.
Learning Styles (Honey & Mumford, 1986)
Kolb's model has attracted considerable interest over the last two decades
and has influenced the development of several `new' models of learning
style. Honey and Mumford's model (1992) was representative of the work
which replicated and attempted to apply Kolb's theory in a commercial
context. The pencil and paper Learning Styles Questionnaire (Honey &
Mumford, 1986) was devised to attempt a practical application of Kolb's
theory in the management of the work-place. The questionnaire is used to
explore the implications for management of a four-fold style model
consisting of the following types of learner: activists, theorists,
pragmatists and reflectors.
Approaches to Learning Study (Entwhistle, 1979; 1981)
Entwistle's work on style is a continuation of earlier work which looked at
the processes of learning undertaken by the learner in a learning situation
(Craik & Lockhart, 1972; Marton, 1976). These writers were initially
interested in the duality of levels of processing in an approach to
learning, which reflected either a surface or deep engagement with the
task. The approach also reflects the thinking of Ausubel and Robinson
(1966), who identified two principal types of learning process: rotemeaningful learning and passive-active learning. Entwistle attempted to
link instructional preference to information processing and developed a
model of learning style which consisted of four aspects: meaning
orientation, reproducing orientation, achieving orientation and holistic
orientation. As part of this model of learning style, Entwistle developed
an integrated conception of the learning process, which described a series
of learner actions linked to specific learning strategies identified in his
original model. Thus, a student engaged in `reproductive learning', who is
characterised by `extrinsic motivation', will adopt a style called `surface
approach' and achieve a learning outcome which will consist of `surface
level understanding' (Entwistle, 1979; 1981).
Each of these stages or approaches reflect a range of cognitive control
running from deep to surface `thinking' in the individual student. Further
refinement of this approach attempted to describe learner orientation and
identified specific style features which characterised the `learning
interface'. The aim in this work was to provide formative assessment which
teachers might use to enhance the pattern of study they require of students
in their class (Ramsden, 1979; 1983).
The Study Process (Biggs 1978; 1985)
Biggs (1978) extended Entwistle's work to develop a new measure of learning
strategy. He was interested in the motivation underlying an approach to
learning. Curry (1987) describes these features as motive-strategy
dimensions involving a `surface', `deep' and `achieving orientation'.
Jonassen and Grabowski (1993) described this work as an extension to
Entwistle's operationalisation of the holist-serialist theory of cognitive
style, with previously identified surface and deep processing activities
widened to include motivational factors, which are intrinsic, extrinsic and
achievement orientation. Entwistle subsequently developed an empirical
model of these processes identified as underlying serialist-holistversatile learning (Entwistle, 1981).
Learning Processes (Schmeck et al. 1977)
Schmeck and co-workers elaborated a theory of learning which rests upon the
notion of quality in thinking. The quality of thinking, they argued,
affects the distinctiveness, transferability and durability of memories
that result from the learning event (Schmeck, 1988). They further developed
this theory to produce a `style' construct which consisted of four
subscales, comprising synthesis-analysis, elaborative processing, fact
retention and study methods. Curry (1987) and Grigerenko and Sternberg
(1995) have both commented on the close relationship between this model and
the work of Entwistle (1979), Ramsden (1979) and Biggs (1985).
Preference-based Models of Learning Style
Learning Style (Dunn et al., 1989)
Dunn and Dunn and Price (1989) defined learning style as the manner in
which different elements from five basic stimuli affect an individual's
ability to perceive, interact with and respond to the learning environment
(Dunn et al., 1989). The `learning style' Dunn et al., present is a good
example of a construct which more properly describes a learning repertoire
rather than a style, and it is a repertoire chiefly made up of learning
preferences. The learning style elements identified in this construct are:
environmental stimulus (light, sound, temperature, design); emotional
stimulus (structure, persistence, motivation, responsibility); sociological
stimulus (pairs, peers, adults, self, group, varied); physical stimulus
(perceptual strengths, including auditory, visual, tactile, kinaesthetic,
mobility, intake, time of day--morning versus afternoon); and psychological
stimulus (global/analytic, impulsive/reflective and cerebral dominance).
A considerable number of studies have been carried out in the development
of the Learning Styles Inventory, investigating and exploring the
application of learning style to the school context (Griggs, 1991; Jonassen
& Grabowski, 1993). The research has mostly taken the form of doctoral
theses. The following are included as a representative sample:
investigating the effectiveness of matching versus mismatching learning
preferences on learning outcomes (De Bello, 1985; Gianitti, 1988); the
identification of developmental patterns (Price et al., 1976; 1977);
establishing relationships between variables (Brennan, 1984; Clark-Thayer,
1987; Bruno, 1988) and discriminating preferences between specific subpopulations (Bauer, 1987; Brunner & Majewski, 1990).
The Learning Styles Inventory comprises a 104-item self-reporting
questionnaire employing a three-choice Likert scale--true, false and
unsure. There are several versions of this instrument aimed at the primary
and secondary age-range. A third version, developed for use with adults, is
called the Productivity Environmental Preference Survey (PEPS). Each
version uses self-report methods to measure factors which reflect the key
variables identified by the authors as forming an individual's response to
the learning task. Each preference factor represents an independent
continuum and is not necessarily related to other factors. Examples of
factors for the environmental variable include: response to noise level, to
light and temperature; for the sociological variable, preference for group
learning, response to authority and typical response to adults; for the
emotional factor, motivation, responsibility and persistence; for the
physical factor, modality preferences, which include auditory, visual,
tactile and kinaesthetic, as well as food/fluids intake and time of day.
Individual and group profiles are produced from the assessment data and the
authors provide guidance for planning style-led instructional method.
Style of Learning Interaction (Riechmann & Grasha, 1974)
The `style' of learning described by Riechmann and Grasha is very similar
to the approach adopted by Dunn et al. (1989) in that it focuses upon an
individual's learning preference. Riechmann and Grasha presented a social
and affective perspective on patterns of preferred behaviour and attitude
which underpin learning in an academic context. They identified three
bipolar dimensions in a construct which described an individual's typical
approach to the learning situation. These dimensions are: avoidantparticipant, competitive-collaborative and dependent/independent, which, as
Jonassen and Grabowski (1993) explained, are related to three classroom
dimensions: student attitudes towards learning; view of teachers and peers;
and reaction to classroom procedure.
Jonassen and Grabowski (1993, p. 281) describe this style construct as a
"social interaction scale because it deals with patterns of preferred
styles for interacting with teachers and fellow students...". The construct
is measured by completing the Student Learning Styles Scale (SLSS), which
is a 90-item self-report inventory consisting of six subscales reflecting
dimensions of the learning `style'. A composite score is totalled and the
respondent's position on the six aspects of this `style' is also recorded.
It is worth noting that there are two forms of this measure: one to assess
a general class, the second to assess individual style in a specific
course. Riechmann and Grasha (1974) expect style to change in different
classes and for a different subject.
Cognitive Skills-based Models of Learning Style
The Child Raring Form (Ramirez & Castenada, 1974)
Ramirez and Castenada (1974) described learning style in terms of fielddependency or field-independency, and its interaction with cultural
differences. The typical responses of individual students who demonstrated
field-independence were identified as learners who often succeeded in the
school context but who responded less favourably to social and holistic
learning activity. Clearly, this model relates to Witkin's construct and
the Wholist-Analytic style dimension, but significantly reflects the
attempt to apply the cognition-centred model to the learning environment.
The Child Rating Form was a direct observation form yielding frequency of
behaviour scales completed by a teacher, or alternatively could be
completed by a student as a self-report questionnaire. The results were
used to identify style dimensions which relate to field sensitivity and
sociological elements involving response to authority and peer orientation.
The Edmonds Learning Style Identification Exercise (ELSIE) (Reinert, 1976)
Reinert's model was aimed at the identification of an individual's natural
`perceptual modality' as they respond to the learning environment.
Reinert's work influenced both the development of the Dunn et al. (1989)
model, as well as the work of Keefe (1987), in developing the NASSP
Learning Style Profile (De Bello, 1990).
The ELSE is composed of 50 one-word items which are used to characterise
the respondent's reaction on four possible levels: visualisation or
creation of a mental picture; alphabetical letters in writing from; sound;
activity, that is an emotional or physical feeling about the word. The
purpose of this assessment is to provide the teacher with information which
will be used to work to the student's strengths or preferred mode of
responding to learning stimuli.
Cognitive Style Interest Inventory and Style Mapping (Hill, 1976)
The work published by Hill while he was Principal at Oaklands Community
College makes for fascinating reading (Hill, 1976). Hill's exploration of
learning style was part of an ambitious attempt to organise an holistic,
college-based approach to learning, which reflected principles of
individualised education. The system was called Cognitive Style Mapping.
Hill devised the construct Educational Cognitive Style to integrate
learning style and curriculum design, as well as the teaching and learning
process. Educational Cognitive Style was understood to be the product of an
interaction between four variables: symbols and their meanings; cultural
determinants; modalities of inference; and educational memory. The
construct was used to develop a diagnostic instrument employed to create a
personalised education for optimal learning.
The Cognitive Style Interest Inventory consists of a three-point scale,
self-report questionnaire, made up of 28 variables reflecting Hill's theory
of cognitive style. The inventory was organised into three main sections
covering symbols and their meaning, cultural determinants and modalities of
difference. There are 216 items and the measure is scored by assigning the
sum of ratings in each specific `theme'. Jonassen and Grabowski (1993)
provide a full discussion of the Hill's theory, but point to the limited
research evidence available to support the instrument. This is in spite of
a great deal of work, mostly in the form of dissertation studies, conducted
in the 1960s and 1970s, examining Hill's work. Perhaps somewhat
surprisingly, Jonassen and Grabowski remain very positive about this
instrument.
Cognitive Style Delineators (Letter, 1980)
Letteri described learning as an exercise in information-processing
involving the storage and retrieval of information. The process of learning
was categorised into six stages ranging from perception reception to longterm memory. Failure to process information in any one of these stages
represents a deficit in cognitive skills acquisition. The teaching of
cognitive skills, or `augmentation' as Reinert (1976) described the process
of cognitive skills training, formed the basis for assessing and developing
learning style and intellectual development. Letteri's style construct is
significant for the presumption that assessment and style awareness should
be used to change a student's cognitive profile and learning style.
Letteri integrated the work of several models of cognitive style to create
a combined assessment of individual skills on a bi-polar continuum. The
assessment identified three types of learner: Type 1 were characterised by
reflective, analytical dimensions of learning style; Type 3 were
characterised by impulsive, global dimensions of style who were typically
non-focused in their learning; Type 2 learners were identified as
reflecting a central position in the continuum.
The Learning Style Profile (Keefe & Monk, 1986)
Keefe's learning style construct describes 24 key elements in learning
style, which are grouped together into three areas: the first is `cognitive
skills', which embraces information-processing activity, such as analytic,
spatial, discrimination, categorisation, sequential processing,
simultaneous processing and memory; the second is "perceptual responses",
which encompasses perceptual responses to data, including visual, auditory
and emotive processing; and the third is `study and instructional
preference', which refers to motivational and environmental elements of
style, including persistence orientation, verbal risk orientation,
manipulative preference, time (early morning-late morning, afternoon,
evening), verbal-spatial grouping, posture, mobility, sound, lighting and
temperature preferences.
The construct, and the rationale for its operationalisation, is based upon
the premise that cognitive skills development is a prerequisite for
effective learning. In this respect, the approach is very much concerned
with `cognitive skills' and reflects an attempt to establish a learning to
learn dimension in mainstream secondary schooling in the USA (Keefe & Monk,
1986). Keefe (1987) argued that if an individual cannot process information
effectively, ineffective learning will take place, minimising the effect of
a positive learning environment. Keefe has produced several monographs
providing guidelines for teachers interested in developing programmes of
work based on this model (Keefe, 1989; 1990).
Summary Evaluation. The learning-centred tradition is by definition
concerned with the learning process. This has led to models of style being
developed which are `fluid', environmentally orientated and very
susceptible to change. Criticism of the approach reflects concern for
construct validity, poor verifiability and an uncertainty about the
relationship between learning style, learning strategy and cognition. The
research continues to be dominated by assessment and with a general
approach heavily influenced by experimental psychology. This explains, in
part, a prevailing psychometric paradigm in style theory, as well as a
continuing focus upon measurement and experimental research design, and a
lack of consensual theory. An attempt to integrate aspects or labels in the
field of learning style is further discussed in the next section.
Learning Style: theory into practice
Models, Measures and Meaning
A proliferation of models, terms and meaning in the field of learning style
seems to increase with each period of new interest and research activity.
Many writers have repeated earlier calls for a clarification in `style'
terminology (Lewis, 1976; Messick et al., 1976; Curry, 1983; Miller, 1987;
Riding & Cheema, 1991; Murray-Harvey, 1994).
Curry, rather pointedly, identifies three areas of continuing concern for
the operationalisation of learning style: "(1) confusion in definitions;
(2) weaknesses in reliability and validity of measurement; (3)
identification of the most style relevant characteristics in learners and
instructional settings." (Curry, 1991, p. 248).
Curry's interest in the `value' of style theory--that is, its application
to the work of learning--is crucial to developing an educational
perspective on `style'. A tangle of terminology and understanding
contributes to this difficulty, which Curry points out is reinforced by the
failure of style researchers, who, she explains, have:
. . .not yet unequivocally established the reality, utility, reliability or
validity of their concepts. Learning/cognitive styles may not exist other
than as an insubstantial artefact of the person-environment interaction.
Alternatively, learning styles may be real, stable, and potent enough to be
useful to educational planners, particularly those with concern for truly
individualised educational programming. (Curry, 1987, p. 16).
As Curry (1987) rightly argued, these fundamental issues both express the
continuing appeal of learning style for educationists, but also serve to
challenge any systematic attempt to apply style-based theory in the school
classroom.
Learning Style: its relevance for education
The idea that `style awareness' may help reach the `hard to teach', and
perhaps contribute to reducing failure generally by enhancing the learning
process, is an elusive but tantalising prospect which clearly merits
further attention. The current interest in teaching and learning style is
evident not only in schools, but also in higher education, work-place
training and professional development. What remains apparently beyond reach
is the systematic operationalisation of style in learning, teaching,
training or management.
Presland (1994) reaches a similar conclusion in his attempt to review and
`operationalise' learning style for the continuing professional development
of educational psychologists. He stated that there is little guidance for
the practitioner interested in implementing style and suggested there is
scope for a large research programme to explore the relevance of style in
continuing professional development (Presland, 1994). The need, well
documented in the literature, for clearer and well grounded development,
suggests that putting theory into practice is overdue, should inform the
continuing research into style and will need to involve a rationalisation
of style as a construct in the psychology of learning.
Is it Cognitive Style or Learning Style?
Part of the attempt to clarify style theory and make better use of it in
professional practice must involve resolving a definition of learning
style. The organisation of contemporary `style' theory into a `three-nested
model' forming an analogous `onion', devised by Curry (1983), represents a
particularly useful effort at relating models in both the cognition- and
learning-centred tradition.
Curry suggested that an inner core of a `style onion' is made up of
personality-centred models, leading to a second strata of informationprocessing models, and then to an outer layer of instructional-preference
models of learning style. This model is used by Curry to review style work
and consider further clarification of the terminology (Curry, 1987).
The style onion usefully offers a model which emphasises the notion of an
individual person's psychology and seeks to explain the formation of
individual learning behaviour. The value of this effort should be
acknowledged, but there is a need to take this further by refining the
model. The basic dimensions of learning style, together with associated
learning strategies, need to be more clearly identified to enable an
elaboration of a personal learning style for the individual learner. As
part of this task, contemporary cognitive and learning style models should
be examined with an eye to distinguishing and integrating basic dimensions
or features of learning style. It is not sufficient, for example, to
suggest casually that learning strategies fit neatly into a style
dimension, or that a style construct may be regarded as an `umbrella'
concept of the kind initially described by Riding and Cheema (1991). The
interrelationship between style, strategy and learning behaviour merits
more attention, and the question of the exact nature of learning style, an
answer.
Fundamental Dimensions of Learning Style
A way forward is perhaps most usefully summed up by Lewis. He stated,
In my opinion, the right thing to do is to focus ... on the search for
individual differences which are basic, in the sense that they underlie
(and to that extent, explain), a whole range of more readily observable
differences. (Lewis, 1976, p. 305).
However, Grigerenko and Sternberg (1995) pointed out that several efforts
have already been made, albeit unsuccessfully, to integrate the various
aspects of style theory. Allinson and Hayes (1994, p. 60), more
optimistically, described the work of several writers, including Kogan
(1980), Messick (1976; 1984) and Miller (1987), who attempted to produce an
integrated model consisting of `super-ordinate' dimensions of cognitive
style.
Messick's work laid an early foundation for such an approach (Messick,
1976; 1984), in which a typology of cognitive abilities, controls and
styles were identified. Significant, in this work, was a distinction drawn
between `style' and `strategy'. Messick originally argued for a distinction
between Thurstone's work on perceptual domains and style (Thurstone, 1924)
and cognitive style as a construct more deliberately used, stating that the
former refers to mental abilities linked with intelligence, whereas
cognitive `styles', in contract, cut across these domains.
Messick et al. (1976) explained that cognitive styles
. . . appear to serve as high level heuristics that organise lower-level
strategies, operations and propensities--often including abilities--in such
complex sequential processes as problem-solving and learning. (Messick et
al. 1976; p. 9)
Riding and Cheema (1991) take a similar approach in their grouping of
dimensions identified in the cognition-centred approach of `style
families', which has informed Riding's model of cognitive style (Riding,
1991; 1994; 1996). As previously argued, further work is required if the
idea of learning style and learning strategy is to be clarified, so that a
definition of learning style and the identification of the "most style
relevant characteristics in learners and instructional settings" (Curry,
1987, p. 248) might be realised.
There is a need to examine the more recently developed models of learning
style with the aim of identifying and integrating additional dimensions of
cognitive style as well as establishing linkage with a range of learning
strategies which are related to these dimensions of learning style. It
seems likely that many of the learning style models developed within the
learning-centred approach might offer insights for the development of
learning strategies. It is arguably useful to think in terms of cognitive
style representing the core of an individual's learning style. The latter
will, in turn, consist of a set of `super-ordinate' dimensions of a
personal learning style. It is possible that two further aspects of
learning might reveal additional dimensions of learning style. The first is
the affective aspect of learning, the second is the motivational aspect of
learning, forming a third and fourth super-ordinate dimension of learning
style.
The motivational aspect of learning is perhaps the least `fixed' and might
well represent a `bridge' between a person's cognitive style and formation
of learning strategy. Both the motivational and the affective dimensions
are arguably reflected in several models of learning style associated with
the learning-centred tradition and perhaps refer more closely to learning
strategy development. There is an urgent need to investigate further the
relationship between these models of `learning style' and the fundamental
dimensions of an individual's personal learning style.
Finally, several multidimensional style models described in this review
carry components of all three levels of the learning style `onion', but a
distinction is required between the `hard-wiring' of an individual's style
and the `soft-wiring' of learning strategies which make up an individual's
learning repertoire; and the conceptual validity, reliability and utility
of these models must be examined before inclusion into a more fully
developed style construct.
The National Association of Secondary School Principals (NASSP) model
(Keefe, 1989) is one example of an attempt, using profiling, at the
educational operationalisation of learning `style'. There is, however, a
need for further refinement and synthesis of the style construct as well as
a `stream-lining' of the assessment content and methodology which makes the
NASSP model rather unwieldy. Secondly, the theory underpinning such a model
should be built around the super-ordinate dimensions of an integrated model
of learning style.
We suggest that Lewis (1976) is right and further development of a `style'
model comprised of super-ordinate dimensions is the way forward. Further,
if we are to make sense of style, find meaning in theory and realise the
`operationalisation' of style in the educational system, the notion that
`learning style' is an individual, stable and person-centred construct,
needs re-emphasising, with a view to developing a profile for an individual
reamer's learning style. This profile should be `basic', containing
`primary' features of the individual's learning repertoire which will
reflect cognitive style and learning preferences; it should be
`manageable', `accessible' and `geared' to the `real' world of education
and training; and it should be linked to an assessment procedure which is
`user-friendly' for both the teacher and student.
The suggestion is then that such a construct will ideally reflect a set of
primary individual differences that include cognitive, behavioural and
affective features combining to form the learner's learning style. The
latter will represent a key consideration in curriculum design, assessmentbased teaching and differentiated learning. The student's role in learning
will surely involve the formation and refinement of learning strategies
which reflect their own particular learning style and the learning task.
The teacher's role in learning must then surely be to incorporate an
awareness of style in their approach to the task of teaching and learning.
The final purpose of an assessment of learning style will be the
enhancement of individuality in the process of teaching and learning.
Correspondence: Stephen Rayner, Assessment Research Unit, School of
Education, University of Birmingham, Birmingham, B15 2TT, UK.
TABLE I. Descriptions and fundamental dimensions of cognitive style
Label
Description
Key Dimension: Wholist-Analytic
Constricted-flexible
control
tendency for distraction or
resistance to interference.
Broad-narrow
preference for broad
categories containing many items
rather than narrow categories
containing few items
Analytical-non analytic
a conceptual response which
differentiates attributes or
qualities conceptualising
rather than a theme or total
effect.
Levelling-sharpening
tendency to assimilate detail
rapidly and lose detail or
emphasise detail and changes
in new information.
Field-dependency/field
independency
individual dependency on a
perceptual field when analysing
a structure form which is
part of the field.
Impulsivity-reflectiveness
tendency for quick as against
a deliberate response.
Cognitive-complexity
A tendency for the
multidimensional or simplicity
or unidimensional processing of
information.
Automisation-restructuring
Preference for simple
repetitive tasks rather than
re-structuring tasks.
Converging-diverging
Narrow, focused, logical,
deductive thinking rather than
broad, open-ended, associational
thinking to solve problems.
Serialist-holist
The tendency to work through
learning tasks or problem
solving incrementally or
globally and assimilate detail.
Splitters-lumpers
A response to information and
interpretation which is either
analytical and methodical or
global.
Adaptors-innovators
Adaptors prefer conventional,
established procedures and
innovators restructuring or
new perspectives in problem
solving.
Concrete
concrete
abstract
abstract
The learner learns through
concrete experience and
abstraction either randomly
or sequentially.
sequential
random/
sequential/
random
Reasoning-intuitive
active-contemplative
Preference for developing
understanding through reasoning
and/or by spontaneity or
insight and learning activity
which allows active
participation or passive
reflection.
Key Dimension: Verbal-Imagery
Abstract versus concrete
Tolerance for unrealistic
experiences
preferred level and capacity
of abstraction. Individual
readiness to accept perceptual
variance with conventional
reality or `truth'.
Verbaliser-visualiser
The extent to which verbal
or visual strategies are used
when processing information
Key Dimensions: Wholist-Analytic
and Verbal-Imagery
Analytic-Wholist
and Verbal-Imager
Tendency for the individual
to process information in
parts as a whole and think in
words or pictures.
Label
References
Key Dimension: Wholist-Analytic
Constricted-flexible
control
Klein (1954)
Broad-narrow
Pettigrew (1958);
Kogan and Wallach (1964)
Analytical-non analytic
Kaga et al. (1964);
Messick and Kogan (1963)
Levelling-sharpening
Klein (1954);
Gardner et al. (1959)
Field-dependency/field
independency
Witkin and Asch (1948a, 1948b);
Witkin (1961); Witkin (1971);
Witkin et al. (1977);
Impulsivity-reflectiveness
Kagan et al. (1964);
Kagan (1966)
Cognitive-complexity
Harvey et al. (1961);
Gardner and Schoen (1962)
Automisation-restructuring
Tiedemann (1989)
Converging-diverging
Hudson (1966; 1968);
Guilford (1967)
Serialist-holist
Pask and Scott (1972);
Pask (1976)
Splitters-lumpers
Cohen (1967)
Adaptors-innovators
Kirton (1976; 1994)
Concrete sequential
Gregorc (1982)
concrete random/
abstract sequential/
abstract random
Reasoning-intuitive
active-contemplative
Allinson and Hayes (1996)
Key Dimension: Verbal-Imagery
Abstract versus concrete
Tolerance for unrealistic
experiences
Harvey et al. (1961)
Klein et al. (1962)
Verbaliser-visualiser
Paivio (1971);
Riding and Taylor (1976);
Richardson (1977)
Key Dimensions: Wholist-Analytic
and Verbal-Imagery
Analytic-Wholist
and Verbal-Imager
Riding (1991; 1994);
Riding and Cheema (1991);
Riding and Rayner (1995)
TABLE II. Models and fundamental features of learning style
Model
Description
Key Feature: Process-based
Meaning orientation/reproducing
orientation/achieving
orientation/holistic
orientation
An integration of
instructional preference
to information processing
in the learner's approach
to study.
Surface-deep--achieving
orientation/
intrinsic-extrinsic-achievement
orientation
An integration of
approaches to study with
motivational orientation.
Synthesis-analysis/elaborative
processing/fact retention/
study methods
The quality of thinking
which occurs during learning
relate to the distinctiveness,
transferability, and
durability of memory and fact
retention.
Concrete experience/
reflective observation/
abstract conceptualisation/
active experimentation
A two-dimensional model
comprising perception
(concrete/abstract thinking)
and processing (active/
reflective information
processing).
Activist/theorist pragmatist/
reflects learners
Preferred modes of learning
which shapes an individual
approach to learning.
Key Feature: Preference-based
Environmental/preference-based
sociological/emotional/
The learner's response to key
stimuli: environmental
physical/psychological
(light, heat); emotional
(structure persistence,
motivation); sociological
(peers, pairs adults, self);
physical (auditory, visual,
tactile); psychological
(global-analytic,
impulsive-reflective).
Participant-avoidant/preference
based collaborative-competitive
independent-dependent
A social interaction measure
which has been used to
develop three bi-polar
dimensions in a construct
which describes a learner's
typical approach to the
learning situation.
Key Feature:
Cognitive-skills-based:
Field-dependency/
cultural differences
Learning style is defined in
terms of field-dependency
and cultural differences which
produces `bicognitive' and
`bicultural' behaviours.
Visualisation/verbal symbols/
sounds/emotional feelings
Learning style defined in
terms of perceptual modality.
Linguistic symbols/
cultural determinants/
modalities of interference
education memory
A model called cognitive
style mapping was developed
which integrated learning
style and pedagogy. The
construct was used to create
a personalised education for
optimal learning.
Field dependency/
scanning-focusing/breadth
of categorisation/cognitive
complexity/reflective-impulsivity
/reflective-impulsivity/
levelling-sharpening.
total-intolerant.
A cognitive profile of three
types of learner reflecting
their position in a
bi-polar analytic-global
continuum which reflects
an individual's cognitive
skills development.
Cognitive skills/perceptual
responses/study and
instructional preferences
Model
Identifies 24 elements in a
learning style construct
grouped together into
three dimensions. The model
presupposes that cognitive
skills development is a
pre-requisite for effective
learning.
References
Key Feature: Process-based
Meaning orientation/reproducing
orientation/achieving
orientation/holistic
Entwistle (1979)
orientation
Surface-deep--achieving
orientation/
intrinsic-extrinsic-achievement
orientation
Biggs (1978; 1985)
Synthesis-analysis/elaborative
processing/fact retention/
study methods
Schmeck et al. (1977)
Concrete experience/
reflective observation/
abstract conceptualisation/
active experimentation
Kolb (1976)
Activist/theorist pragmatist/
reflects learners
Honey and
Mumford (1986)
Key Feature: Preference-based
Environmental/preference-based
sociological/emotional/
physical/psychological
Price et al. (1976; 1977);
Dunn et al. (1989)
Participant-avoidant/preference
based collaborative-competitive
independent-dependent
Riechmann-Grasha (1974)
Key Feature:
Cognitive-skills-based:
Field-dependency/
cultural differences
Ramirez and Castenada (1974)
Visualisation/verbal symbols/
sounds/emotional feelings
Reinert (1976)
Linguistic symbols/
cultural determinants/
modalities of interference
education memory
Hill (1976)
Field dependency/
scanning-focusing/breadth
of categorisation/cognitive
complexity/reflective-impulsivity
/reflective-impulsivity/
levelling-sharpening.
total-intolerant.
Letteri (1980)
Cognitive skills/perceptual
responses/study and
instructional preference
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~~~~~~~~
By STEPHEN RAYNER & RICHARD RIDING, Assessment Research Unit, University of
Birmingham, UK
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Title: `Learning style': Frameworks and instruments.
Subject(s): LEARNING strategies; EDUCATION -- Evaluation
Source: Educational Psychology, Mar-Jun97, Vol. 17 Issue 1/2, p51, 13p, 6
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Author(s): Sadler-Smith, Eugene
Abstract: Investigates several aspects of learning styles and explore
their interrelationship. Four broad categories of learning style;
Definition of learning preferences, learning style and cognitive style;
Relationships between preferences, styles and approaches; Methods used in
the study; How the study indicates some overlap between the dimension
measured by the Learning Styles Questionnaire and the Revised Approaches to
Studying Inventory.
AN: 9706014958
ISSN: 0144-3410
Database: Academic Search Premier
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`LEARNING STYLE': FRAMEWORKS AND INSTRUMENTS
ABSTRACT
In both education and training an important aspect of the design,
development and delivery of learning is the role of individual differences
between learners in terms of their `learning styles'. One may identify four
broad categories of what have been termed `learning style': (i) `cognitive
personality elements' (e.g Witkin et al. 1977; Riding, 1991); (ii)
`information-processing style' (e.g Kolb, 1984; Honey & Mumford, 1992);
(iii) `approaches to studying' (e.g Entwistle & Tait, 1994); (iv)
`instructional preferences' (e.g Riechmann & Grasha, 1974). A study of 245
university undergraduates in business studies aimed to: (i) describe the
range of individual differences present within the sample; (ii) investigate
the relationship between learners' cognitive styles, learning styles,
approaches to studying and learning preferences; (iii) consider the
implications of `learning style' for teaching and learning in higher
education. The present study suggested some overlap between the dimensions
measured by the Learning Styles Questionnaire (Honey & Mumford, 1986; 1992)
and the Revised Approaches to Studying Inventory (Entwistle &Tait, 1994).
No statistically significant correlations were found between cognitive
style, as measured by the Cognitive Styles Analysis (Riding, 1991) and any
of the other `style' constructs used. Further research is required to
investigate these relationships, as is a large-scale factor analytical
study of the Honey and Mumford and Kolb instruments. The notions of whole
brain functioning, integrative approaches to studying and degree of
learning activity are discussed.
Gorham (1986) and Curry (1983) identified three broad categories of
`learning style': (i) `cognitive personality elements' such as field
dependence and independence (e.g. Witkin et al., 1977); (ii) `informationprocessing style', such as Kolb's model of the experiential learning cycle
(Kolb, 1984) and the associated learning styles (converger, diverger,
accommodator, assimilator) or the related learning styles suggested by
Honey and Mumford (activist style, reflector style, theorist style and
pragmatist style; Honey & Mumford, 1992); (iii) `instructional
preferences', such as those measured by inventories like the Grasha-
Riechmann Student Learning Styles Scales (Riechmann & Grasha, 1974). One
may add to this the notion of `approaches to studying' (Marton & Saljo,
1976), which, in terms of function and process, may lie somewhere in
between `cognitive personality elements' and `instructional (i.e. learning)
preferences'. This array of individual difference constructs suggests a
multidimensional, as opposed to bipolar, model of `learning style' (see
Murray-Harvey, 1994, p. 374). In order to acknowledge and accommodate this
range of aspects of individual difference in an holistic way, cognisance
should be taken of learning preferences, learning styles, approaches to
studying and cognitive styles (Sadler-Smith, 1996a). The present study
aimed to investigate each of these aspects of `learning style' and explore
their interrelationships.
Learning Preferences
Learning preferences may be defined as an individual's propensity to choose
or express a liking for a particular instructional technique or combination
of techniques. Riechmann and Grasha (1974) identified three learning
preference styles or types: (i) dependent learners: prefer teacherdirected, highly structured programmes with explicit assignments set and
assessed by the teacher; (ii) collaborative learners: discussion-orientated
and favour group projects, collaborative assignments and social
interaction; (iii) independent learners: prefer to exercise an influence on
the content and structure of learning programmes within which the teacher
or instructor is a resource. For the purposes of the present study, a
simple paper and pencil inventory of learning methods was developed. The
inventory is contained at Appendix 1. Factor analysis of the inventory
items (principal components with Varimax rotation) suggested three learning
method preference factors: (i) autonomous methods (open/distance/flexible
learning, computer-assisted learning); (ii) collaborative methods (roleplay, discussion groups, business games); (iii) dependent methods (lecture,
tutorial/surgery). These conceptual grouping corresponded closely to the
notions of independence, collaboration and dependence used by Riechmann and
Grasha. The individual inventory items were derived from discussions with
students and staff at the institution concerned.
Learning Style
The work of Kolb (1984) in the USA and Honey and Mumford (1986; 1992) in
the UK represent two widely used approaches to the identification of
`learning style', both of which take as their basis Kolb's model of
experiential learning.
Kolb (1984)
Kolb's model of experiential learning describes learning in terms of
processes rather than outcomes and is conceived of as four distinct stages
thus (Kolb, 1984, p. 21):
[learning is] seen best to be facilitated by an integrated process that
begins with here-and-now experiences followed by collections of data and
observations about that experience. The data are then analysed and the
conclusions of the analysis are fed back to the actors in their experience
for their use in the modification of their behaviour and choice of new
experiences.
On the basis of this model, Kolb argued that learning is a four-stage
process consisting of concrete experience, observation and reflection,
formation of abstract concepts and generalisations, and the testing of the
implications of these concepts in new situations, thus leading to further
concrete experiences. An `ideal' learner has the ability to operate with
equal facility at all four stages. Such ideal learners are considered rare
and most individuals have a preference for one or more stages in the cycle.
Kolb suggested that an individual's learning style may be identified by
assessing her or his position on each of two bipolar dimensions using a
self-report type inventory (the Learning Styles Inventory, Kolb, 1976
[revised 1985]; Kolb et al., 1995). It comprises lists of words (e.g.
`analytical', `logical', `receptive', `feeling', `intuitive', etc.) which
respondents are required to rank according to how they feel the words best
describe their learning style (see Tennant, 1988, p. 101). Learning style
scores have been collected for a number of professional groups and it is
argued "offer reasonable indications of the learning style orientations
that characterise the different professions" (Kolb, 1984, p. 88). The
Learning Styles Inventory (LSI) has been criticised for an apparent lack of
validity and reliability (Sims et al., 1986). Allinson and Hayes (1988, p.
271), in reviewing the LSI, quoted Freedman and Stumpf's study (1978) which
found that the LSI items loaded on two bipolar dimensions, but the factor
loadings were low (accounting for only 20.6% of the total variance).
Cornwell et al., (1991) analysed the responses of 317 subjects who
completed the LSI. Their results "afforded support for only two of the
individual ability dimensions and little support for Kolb's two bipolar
dimensions" (1991, p. 455) and that the LSI should be used "with some
caution as a means to inform adults about how they learn best" (p. 460).
Tennant (1988, p. 104) questioned the validity of the experiential learning
model, but acknowledged its value as a framework for planning teaching and
learning activities (p. 105). He cautioned against its wholesale acceptance
lest it leads to misconceptions about learners.
Honey and Mumford (1992)
The most widely used approach to `learning style' in the UK, and often used
as an alternative to Kolb's work, is that of Honey and Mumford (1986;
1992). They used the Kolb model as a basis from which they developed their
own Learning Styles Questionnaire (LSQ) (Honey & Mumford, 1986; 1992), an
80-item self-report type questionnaire which has been designed to identify
an individual's relative strengths in each of four learning styles
(activist style, reflector style, theorist style and pragmatist style).
A factor analytical study by Allinson and Hayes (1988) of learning style
scores for UK managers questioned the validity of the LSQ. They suggested
that confirmation of the LSQ's structure through a factor analysis of the
individual questionnaire items rather than the learning styles scores is
necessary, as is evidence of its predictive validity, before it can be used
with confidence by management educators (Allinson & Hayes, 1988, p. 280).
They did go on to suggest a two-factor structure comprising an `analysis'
factor and an `action' factor (Allinson & Hayes, 1990). In a factor
analytical study of the questionnaire scales with an undergraduate sample,
Sadler-Smith and Riding failed to confirm the LSQ's hypothesised structure
but did observe the analysis and action factors identified by Allinson and
Hayes.
Approaches to Studying
Marton and Saljo (1976), in a study of how Scandinavian students tackled
the task of reading academic articles and texts, identified two contrasting
approaches. Students adopting a `deep' approach "started with the intention
of understanding the meaning of the article, questioned the author's
arguments, and related them to both previous knowledge and personal
experience" (Entwistle, 1988a, p. 77). This approach contrasted with that
of other students who started with the intention of memorising the
important facts and, hence, were described as adopting a `surface'
approach.
The Revised Approaches to Studying Inventory (RASI) (Entwistle & Tait,
1994) is one of a series of instruments designed to identify these
differences in approaches to studying. The RASI is a 38-item self-report
type inventory designed to measure an individual's approaches to studying
in a higher education context in terms of five `orientations': (i) deep
approach; (ii) surface approach; (iii) strategic approach; (iv) lack of
direction; (v) academic self-confidence.
The first three orientations are each made up of four subscales with
between two and four items per subscale. The `lack of direction' and
`academic self-confidence' orientations do not themselves have individual
subscales but are made up of four items each. Its factor structure appears
robust (Sadler-Smith, 1996b).
Deep Approach. This is made up of four subscales: (i) looking for meaning;
(ii) active interest/critical stance; (ii) relating and organising ideas;
(iv) using evidence and logic. Subjects with a deep approach report that
they try to work out the meaning of information for themselves, do not
accept ideas without critical examination, relate ideas from their studies
to a wider context and look for reasoning, justification and logic behind
ideas.
Surface Approach. The four subscales are: (i) relying on memorising; (ii)
difficulty in making sense; (iii) unrelatedness; (iv) concern about coping.
Subjects reporting a surface approach would see themselves as relying on
rote-learning of material, accepting ideas without necessarily
understanding them, emphasising the acquisition of factual information in
isolation to a wider picture and express anxiety about their studies in
terms of organisation and volume of material.
Strategic Approach. The four subscales are: (i) determination to excel;
(ii) effort in studying; (iii) organised studying; (iv) time management.
Subjects reporting a `strategic approach' perceive themselves as having
clear goals related to their studies and being `hard workers', ensuring
that they have the appropriate resources and conditions for successful
study and feel that they are generally well organised.
Lack of Direction. This orientation does not have any separate subscales,
but consists of four items which are intended to reflect subjects' lack of
clear academic and career direction and goals (e.g. "I rather drifted into
higher education without deciding for myself what I really wanted to do").
Academic Self-confidence. Again, this orientation does not have any
separate subscales. Subjects scoring highly on the four-item academic selfconfidence orientation typically perceive themselves as able, intelligent
and able to cope with the intellectual and academic demands of their
studies (e.g. "I seem to be able to grasp things for myself pretty well on
the whole") (Entwistle & Tait, 1994).
Cognitive Style
Cognitive style may be defined as `a distinctive and habitual manner of
organising and processing information'. Riding and Cheema (1991), in a
survey of a number of different types of cognitive style, suggested that
each may be assigned to one of two principal cognitive styles' `families'.
They suggested that learners differ in terms of two fundamental styles:
Wholist-Analytical Dimension of Cognitive Style. This describes the
habitual way in which an individual processes information and is derived
from the work of Witkin (see Witkin et al., 1977). Analytics tend to
process information into its component parts) wholists tend to retain a
global view of a topic. Schmeck (1988, p. 328) concluded that "people with
an extreme analytical style ... have focused attention, noticing and
remembering details. They have an interest in operations and procedures and
proper ways of doing things and prefer step-by-step, sequential
organisational schemes ... They are gifted at critical and logical
thinking." Similarly, people with a "global [i.e. wholist] style ... [have]
an attention toward scanning, leading to the formation of global
impressions rather than more precisely articulated codes ... Their thinking
is more intuitive than that of an analytic person ... [they] are likely to
be more impulsive ... and are more gifted at seeing similarities than
differences." (p. 328).
Verbaliser-Imager Dimension of Cognitive Style. Verbalisers tend to
represent information in memory in `words'; imagers tend to represent
information in memory in `pictorial' form (Riding, 1994).
These two bipolar dimensions may be considered to be orthogonal. An
individual's cognitive style may be assessed using the Cognitive Styles
Analysis (Riding, 1991): this is presented and scored by means of a PC. It
indicates a subject's position on the Wholist-Analytical and VerbaliserImager dimensions by means of ratios which indicate a his or her: (i)
performance in the verbal mode relative to the imagery mode; (ii) balance
between seeing the whole and seeing the parts. Norms for data gathered by
Riding and his co-workers from over 1400 subjects suggest nine cognitive
style types: (i) wholist verbaliser; (ii) wholist bimodal; (iii) wholist
imager; (iv) intermediate verbaliser; (v) intermediate bimodal; (vi)
intermediate analytic; (vii) analytic verbaliser; (viii) analytic bimodal;
(ix) analytic imager.
Schmeck (1988) suggested an integration of `global' and `analytical'
functioning gives a synthesis of styles producing a single flexible style
(referred to by various authors as a `versatile' style or `whole-brain
functioning') which takes advantage of both holistic and analytical
functioning (1988, p. 8).
Hayes and Allinson (1996) have developed a paper and pencil inventory (the
Cognitive Styles Index) which assesses a bipolar intuition-analysis
dimension of cognitive style. They use the Cognitive Styles Index to assess
how far individuals are intuitive or analytic in their cognitive style and
they go on to speculate on to whether or not it is possible to integrate
these two styles to develop a `whole-brain' approach through training or
education of the individual (1996, p. 132).
Kirton (1989) described a number of assumptions regarding cognitive style
that help to distinguish it from other constructs: (i) it is related to
numerous traits of personality that appear early in life and are temporally
stable; (ii) it is bipolar, non-pejorative and non-evaluative; (iii) it is
conceptually independent of "cognitive capacity, success, cognitive
techniques [strategies] and coping behaviour (functioning temporarily
outside ones habitual style)" (1989, p. 3).
Relationships Between Preferences, Styles and Approaches
The present study aimed to: (i) use multiple measures of `learning style'
and examine their interrelationships; (ii) examine the relationship between
these multiple measures of `learning style' and academic performance.
Newstead (1992) examined the relationship between learning styles and
approaches to studying and the predictive validity of the short form
Approaches to Studying Inventory (ASI). The former is "at first sight an
inappropriate question since the two scales [ASI and LSI] are measuring
different things: the ASI is looking at learning orientations which are to
some extent variable and context-dependent, while the LSI is looking at
rather more stable and permanent aspects of learning. Nevertheless, both
involve measures of how active a person is as a learner and it is not
unreasonable to expect that there will be some connection between these
measures" (1992, p. 304). In terms of the relationship between the two
instruments, he found statistically significant, but low, correlations
between Entwistle's `meaning' (i.e. deep) approach and Kolb's abstract
conceptualisation (r = 0.20; p < 0.01) and concrete experience (r = 0.23; p
< 0.01) scales, but does not discount the possibility that the obtained
correlations are spurious. Positive correlations were found between the
achieving orientation and academic performance (r= 0.32; p < 0.01) and
between the meaning orientation and academic performance (r = 0.22; p <
0.05). The correlation between the reproducing orientation and academic
performance was low (r = - 0.15; p < 0.05).
Clarke (1986) examined the predictive validity of the ASI in a medical
school. He found that the cognitive aspects of the approaches failed to
predict academic success: "a self-reported leaning towards the Meaning
Orientation [deep], which subsumes [espoused academic values] ... does not
appear to confer any advantage in performance ... but neither does a
leaning towards the reproducing orientation [the latter was a negative
predictor for final year students]." (1986, p. 318).
Based on the rationales outlined in the previous sections it was expected
that there would be significant relationships between: (i) learning
preferences and learning styles; (ii) learning styles and approaches to
studying; (iii) the theorist learning style (which embodies traditional
academic values) and academic performance; (iv) deep approaches to studying
and academic performance. It was not expected that cognitive style, because
of its fundamental nature, would relate in any simple way to the other
constructs measured.
Method
The sample comprised 245 business studies students (130 males and 115
females) aged between 18 and 58 years (mean age 23.81; standard deviation
8.07), who were following undergraduate programmes in business studies,
marketing, personnel management, computing and informatics for business,
finance and accounting at a university business school in the UK. Subjects
were studying a compulsory semester-long module on personnel (employee
resourcing, relations and development).
For the purposes of the present study, the materials used were: (i) the 38item RASI; (ii) a 23-item Learning Preferences Inventory LPI (see Appendix
1), which consisted of three separate scales: learning method preference
(seven items); learning media preference (nine items); and assessment
method preference (seven items). The latter two scales were included for
exploratory purposes only and will not be considered further; (iii) the
Learning Styles Questionnaire (LSQ) (Honey & Mumford, 1992-see above). The
LSQ was chosen in preference to the LSI as a result of the criticisms of
the latter expressed by Sims et al. (1986), Cornwell et al. (1991),
Newstead (1992); (iv) the Cognitive Styles Analysis (CSA) (Riding, 1991).
The RASI and LPI were combined into a single questionnaire and prefaced by
appropriate prior instructions. The LSQ was administered immediately after
the RASI and LPI.
Questionnaires were scored by a researcher and results and interpretations
of the RASI and LPI scores were not fed back to subjects. The LSQ results
were fed back to subjects with an interpretation of their individual scores
only after the entire sample had been tested and some weeks had elapsed.
The combined RASI and LPI questionnaire was scored as follows: (i) RASI:
agree, 5; agree somewhat, 4; unsure, 3; disagree somewhat, 2; disagree 1;
(ii) LPI: strong preference, 5; preference, 4; no preference, 3; dislike,
2; strong dislike, 1. In accordance with the instructions accompanying the
RASI, subjects were requested not to use `3' unless they really had to or
the item could not apply to them.
The LSQ was scored as follows: one mark was allocated where a subject
agreed that an item applied to them, no mark was allocated if the item did
not apply to them. Individuals could score a maximum of 20 on each of the
four `learning styles' scales (activist, reflector, theorist and
pragmatist).
Data were analysed using the Statistical Package for the Social Sciences
(Release 6.1; 1 994).
Results
The results will be considered as follows: (i) descriptive statistics for
LPI, LSQ RASI and CSA; (ii) relationship between LPI, LSQ RASI and CSA;
(iii) relationship between LPI, LSQ, RASI, CSA and academic performance.
Descriptive Statistics Table I shows the mean scores obtained on the LPI,
LSQ and RASI.
Relationship Between the LPI, LSQ, RASI and CSA. The relationships between
the measures of individual difference were considered by means of the
intercorrelations between the various orientations and scales of the
respective instruments.
Learning Preferences and Learning Styles. The following small, but
statistically significant, correlations were observed between the LPI and
LSQ scales: (i) between the collaborative scale and the activist scale (r =
0.24; p < 0.01); (ii) between the collaborative scale and the reflector
scale (r = -0.15; p < 0.05); (iii) between the autonomous scale and the
reflector scale (r = 0.17; p < 0.05)--see Table II. Hence, there did not
appear to be any strong relationship between learning style and learning
preferences as measured by the LSQ and the LPI.
Approaches to Studying and Learning Preferences. The only statistically
significant correlations between the RASI orientations and the LPI scales
were between the deep orientation and the autonomous (r = 0.21; p < 0.01)
and collaborative scales (r = 0. 16; p < 0.05)--see Table III. There did
not appear to be any strong relationship between approaches to studying and
learning method preferences as measured by the RASI and LPI.
Approaches to Studying and Learning Styles. A number of statistically
significant correlations were observed between subjects' approaches to
studying and their scores on the learning styles scales: (i) the deep
orientation correlated positively with the reflector (r = 0.25; p < 0.01),
theorist (r = 0.39; p < 0.01) and pragmatist scales (r = 0.33; p < 0.01);
(ii) there was a small positive correlation between the surface orientation
and the reflector scale (r = 0.22; p < 0.01); (iii) the strategic
orientation correlated negatively with the activist scale (r = -0.18; p <
0.01) and positively with the reflector (r = 0.35; p < 0.01), theorist (r =
0.42; p < 0.01) and pragmatist scales (r = 0.20; p < 0.01); (iv) there was
a small negative correlation between the lack of direction orientation and
the theorist scale (r = - 0. 16; p < 0.05); (v) there was a small positive
correlation between the academic self-confidence orientation and the
pragmatist scale (r = 0.22; p < 0.01)--see Table IV.
A similar result was obtained by Newstead (1992). He observed a correlation
of 0.20 between the deep orientation and abstract conceptualisation (the
Kolb equivalent of the theorist style). He also speculated on the notion of
a learner's degree of `activity'. In this sense, the sum of an individual's
scores on each of the learning style scales is a measure of how `active' a
learner is: a score of 20 on each of the scales (giving a maximum of 80)
would represent a person who reports themselves as being highly active at
each stage of the experiential learning cycle. The correlations between
this `activity' score and the RASI orientations were computed. The
following significant correlations between the various RASI orientations
and the `activity' score were observed: (i) deep approach (r = 0.43; p <
0.01); (ii) strategic (r = 0.35; p < 0.01); (iii) lack of direction (r = 0.14; p < 0.05). Examination of the scatter-plots did not reveal any
outlying values or non-linear relationships. Hence there does appear to be
some relationship between subjects' approaches to studying as measured by
the RASI and their learning styles as measured by the LSQ. However, the
possibility that a high `activity' score indicates a subject's propensity
to agree with questionnaire items should not be overlooked.
LPI, LSQ, RASI and CSA. In terms of linear correlations, no significant
relationships were detected between cognitive styles and learning
preferences, learning styles or approaches to studying. Sadler-Smith and
Riding (in press b) reported a number of significant interactions between
cognitive style, academic ability and sex in their effects on learning
preference.
LPI, LSQ, RASI, CSA and Academic Performance. In order to investigate the
predictive validity of the LPI, LSQ, RASI and the CSA, the linear
correlations between each of these and a single measure of academic
performance were calculated. The academic performance measurement consisted
of the aggregate percentage score for each individual across 12
undergraduate modules (mean score, 58.89%; SD, 5.76). The correlations are
shown in Table V.
Statistically significant correlations were observed between academic
performance and the: (i) deep approach (r = 0.25; p < 0.01), academic selfconfidence (r = 0.17; p < 0.05) and strategic approach (r = 0.14; p <
0.05); (ii) theorist style (r = 0.17; p < 0.05); (iii) autonomous
preference (r = 0.13; p < 0.05). There was a very low correlation between
the `activity' measure (see above) and academic performance (r = 0.14; p <
0.05).
Discussion
As a number of the LSQ items invite respondents to express preferences for
particular types of learning situations characterised by, amongst other
things, varying degrees of autonomy, collaboration and dependence, it was
anticipated that the correlations between the LPI and LSQ scales would have
been stronger. For instance, whilst it was expected that the activist scale
would have correlated positively with the collaborative preference,
stronger negative correlations with the autonomous preference were
anticipated. One may also have expected a strong positive relationship
between the reflector scale and the dependent preference. This suggests
that either the underlying dimensions that each instrument is measuring are
not the same or that the psychometric properties of the LPI and LSQ are
themselves questionable. The LPI was derived by the author for the purposes
of the present study, hence the factor structure revealed therein has yet
to be confirmed by reference to larger samples. The factor structure of the
LSQ, especially at the item level, remains unclear.
Entwistle (1988b) describes the deep, surface and strategic approaches to
studying in terms of predominant motivations (e.g. interest in the subject
matter, fear of failure, competition, etc.) and intentions (e.g. to fulfil
assessment requirements by reproduction). Individuals satisfy their
motivations and intentions by means of specific learning processes, for
example, rote-learning (Entwistle, 1988b, pp. 46-47). Individual learning
preferences, one could speculate, relate to several factors including: (i)
personality; (ii) needs; (iii) context; (iv) experience. One could
anticipate relationships between `deepness' of approach and autonomy of
process (pursuit of subject through intrinsic motivation) and between a
`surface' approach and a dependence of process (pursuit of assessment goals
as a result of, perhaps, extrinsic motivation). The former was observed to
a limited extent. The correlation between the deep approach and the
autonomous preference could lead one to speculate regarding the extent to
which a deep approach (as a result of interest in the subject, vocational
relevance, etc.) leads a learner to adopt an autonomous approach, as a
process, in the pursuit of particular learning outcomes (deep level of
understanding, integration, etc.). Even if one acknowledges the inevitable
weaknesses in the LPI, the poor relationships between the RASI and LPI
would, on the whole, suggest that they do not measure similar dimensions.
The moderate correlations between the deep approach of the RASI and the
theorist and pragmatist scales of the LSQ suggests some overlap in the
dimension which their respective instruments purport to assess. Similarly,
the correlations between the strategic approach and the reflector and
theorist scales suggests some commonality. Further analysis of the
intercorrelations between the items of the respective scales of the LSQ and
RASI would help to clarify the nature of the underlying dimension which
they may independently be measuring. It should be noted that both Kolb's
and Entwistle's models put considerable emphasis on process. Entwistle's
`to reach a personal understanding' intention and the associated processes
of operation, versatile and comprehension learning (Entwistle, 1988b) may
have some equivalence with Honey and Mumford's `theorising' and,
ultimately, with Kolb's `abstract conceptualisation'. Investigation of the
relationships between the RASI and LSI may prove to be instructive in this
regard. Newstead (1992) speculated that the extent to which a person is
`active' as a learner may link the underlying constructs of the LSI and
ASI. The present study demonstrated moderate correlations between the deep
and strategic approaches of the RASI and the total score on the LSQ
(labelled `activity' by the present author). This could suggest that those
individuals adopting deep and strategic approaches are, in terms of Kolb's
model, `rounded' (or versatile) as learners, that is, have some proficiency
at most of the stages of the experiential learning cycle. One should not
lose sight of the possibility that: (i) in correlational studies of this
nature, some of the observed correlations may, in fact, be spurious; (ii)
subjects may be exhibiting acquiescence in their responses to the LSQ.
The observation that the CSA did not correlate with any of the other
instruments used in the present study is evidence that the underlying
dimensions that it measures are quite different to the motivation, process
and activity constructs which may underlie the LPI, LSQ and RASI. Riding
(this issue) suggests that the Verbaliser-Imager and Wholist-Analytical
dimensions of style are quite fundamental and that they may reflect
activities in different hemispheres of the brain. These activities may be
mediated through experience, context and motivation to affect, for example,
learning preferences and performance under specific instructional
treatments.
The issues discussed here raise a number of questions. Does `whole-brain
functioning' occur, as some authors suggest, in those individuals who have
strengths in both analytical and global thinking (i.e. wholist verbalisers
and analytic imagers) and does this equate to Entwistle's and Pask's
concept of `versatile' learning (Pask, 1976), that is, relating evidence to
ideas and integrating principles with facts? Can this whole-brain,
integrative approach, once it has been identified, be taught and/or
facilitated through the use of particular teaching and learning strategies?
Can a knowledge of cognitive style facilitate a deep, all-round approach to
learning and teaching and, hence, improve the efficiency and effectiveness
of learning?
Conclusion
The present study may indicate some overlap between the dimensions measured
by the Learning Styles Questionnaire (Honey & Mumford, 1986; 1992) and the
Revised Approaches to Studying Inventory (Entwistle & Tait, 1994) which one
could speculate include constructs such as motivation, learning process and
degree of learning activity. Further research is required to investigate
these relationships. As a precursor to this, a large-scale factor
analytical study of the Honey and Mumford and Kolb instruments is required
to explore their factor structure at the item level and their
interrelationships. The value of the former as a method of raising
awareness is widely accepted (see Tennant, 1988; Presland, 1994); the
question of its diagnostic and predictive capabilities remains unresolved.
The present study adds further support to Riding's contention that the
Wholist-Analytical and Verbal-Imagery dimensions of cognitive style are
quite fundamental and independent of learning `styles' and strategies per
se. The notions of whole-brain functioning, integrative approaches to
studying and degree of learning activity are of considerable potential
significance and warrant further investigations of a multidimensional
nature.
Correspondence: Eugene Sadler-Smith, Plymouth Business School, University
of Plymouth, Drake Circus, Plymouth, Devon, PL4 8AA, UK.
TABLE I. Mean scores for the LPI, LSQ and RASI (standard deviation in
brackets)
Construct
Scale
Mean score
Preferences
Autonomous
Collaborative
Dependent
Learning style
Activist
Reflector
Theorist
Pragmatist
Approaches
Deep
Surface
Strategic
Self-confidence
Lack of direction
3.07 (0.95)
3.22 (0.90)
3.92 (0.85)
10.09
14.07
11.80
12.26
(3.79)
(3.46)
(3.37)
(2.97)
3.77
3.17
3.79
3.69
1.65
(0.61)
(0.79)
(0.68)
(0.62)
(0.81)
TABLE II. Correlations between the LPI and LSQ
Autonomous
Collaborative
Dependent
Activist
Reflector
Theorist
Pragmatist
-0.06
0.17[*]
0.07
0.00
0.24[**]
-0.15[*]
-0.04
0.07
-0.08
0.05
0.04
0.08
[*] p < 0.05; [**] p < 0.01.
TABLE III. Correlations between RASI orientations and LPI scales
Legend for Table:
A
B
C
D
E
-
Deep
Surface
Strategic
Lack of direction
Academic self-confidence
A
Autonomous
Collaborative
Dependent
0.21[**]
0.16[*]
0.07
B
C
D
E
-0.10
-0.05
0.05
0.08
-0.10
0.03
-0.05
-0.07
-0.05
-0.01
0.01
-0.02
[*] p < 0.05; [**] p < 0.01.
TABLE IV. Correlations between RASI and LSQ
Legend for Table:
A
B
C
D
-
Activist
Reflector
Theorist
Pragmatist
A
Deep
Surface
Strategic
Lack of direction
Academic
self-confidence
-0.03
-0.08
-0.18[***]
0.04
0.05
B
0.25[**]
0.22[**]
0.35[**]
-0.08
-0.04
C
0.39[**]
0.07
0.42[**]
-0.16[*]
D
0.33[**]
-0.09
0.20[**]
-0.10
0.01
0.22[**]
[*] p < 0.05; [**] p < 0.01.
TABLE V. Correlation of LPI, LSQ, RASI, CSA with academic performance
Correlation with academic
Construct
Scale
performance
Collaborative
Dependent
-0.13
0.03
Learning style
Activist
Reflector
Theorist
Pragmatist
-0.05
0.08
0.17[*]
0.11
Approaches
Deep
Surface
Strategic
Lack of direction
Self-confidence
0.26[**]
-0.11
0.14[*]
-0.11
0.17[*]
Cognitive style
VI ratio
WA ratio
-0.01
0.04
[*] p < 0.05; [**] p < 0.01.
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CURRY, L. (1983) Learning Styles in Continuing Medical Education (Ottawa,
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KIRTON, M.J. (Ed.) (1989) Adaptors and Innovators: styles of creativity and
problem solving (London, Routledge).
KOLB, D.A. (1976) Learning Style Inventory: technical manual (Boston, MA,
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KOLB, D.A. (1985) Learning Style Inventory (Revised, 1981, 1985) (Boston,
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APPENDIX 1
Learning preferences inventory
Please indicate your preference for each of the items listed below. Respond
according to the following scheme: (5) Strong preference; (4) Preference;
(3) No preference; (2) Dislike; (1) Strong dislike.
Legend for Table:
A
B
C
D
E
-
Strong preference
Preference
No preference
Dislike
Strong dislike
Teaching Methods
Lecture
Tutorial/surgery
Role-play
Open/Distance/Flexible learning
Discussion groups
Computer-assisted learning
Business games
~~~~~~~~
A
B
C
D
E
5
5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
By EUGENE SADLER-SMITH, Plymouth Business School, University of Plymouth,
UK
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Author(s): Schatteman, A.; Carette, E.
Abstract: Investigates the effect of Interactive Working Group in learning
styles. background and goal of the study; Theoretical framework of learning
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UNDERSTANDING THE EFFECTS OF A PROCESS-ORIENTATED INSTRUCTION IN THE FIRST
YEAR OF UNIVERSITY BY INVESTIGATING LEARNING STYLE CHARACTERISTICS
ABSTRACT
In an attempt to remedy transition problems from secondary education to
university, the Learning and Guidance Centre of the Faculty of Sciences at
the Vrije Universiteit Brussel has developed a process-orientated
instruction, called Interactive Working Groups (IWG). The major goal is to
promote in-depth learning by the training of general and specific learning
skills in a content-specific context. Previous quantitative analyses have
shown that participation in IWG leads to better performance in examinations
and induces positive effects on the learning approach. Therefore, we
considered separately variables such as prior knowledge test results,
examination scores and learning style characteristics. The goal of the
present research is to understand these effects by analysing quantitatively
the interdependency of previous variables. The results show, for instance,
that the IWG enhance precisely those changes in learning approach and
regulation which induce an increase in performance in examinations. The
results were confirmed by qualitative analyses through interviews.
Introduction
Background and Goal
Due to the fact that students seem to be unprepared for the study skills
that are necessary to succeed in university education (especially in the
first year) (Entwistle & Tait, 1993), the Learning and Guidance Centre of
the Faculty of Sciences at the Vrije Universiteit Brussel (Eisendrath &
D'Haese, 1989) has developed for first year students a process-orientated
instruction called Interactive Working Groups (IWG). The major goal of this
supervision is to promote in-depth learning by the training of general and
specific learning skills (Entwisde, 1993; 1994a) in a content-specific
context (in science) and to encourage a scientific attitude.
Evaluations in the past (Crabbe et al., 1993; Schatteman et al., 1993) have
shown that IWG participation may lead to better scores in examinations and
that it also induces positive effects on the learning approach.
The main questions we address in this study are:
--whether changes in learning style are fostered by IWG;
--whether, due to participation in IWG, changes in learning style can
improve the students' performance in examinations.
The variables considered here are: prior knowledge, examination scores,
learning approach, regulation activities, motivation and beliefs towards
learning and instruction.
This paper has to be seen as a first step in this research goal: based on
quantitative analyses of pre-test, post-test and learning style inquiry
results, rather coherent changes in learning approach and regulation
activities are detected; moreover, they are found to affect positively
performance in examinations. In a qualitative way, these findings were
confirmed through interviews.
Interactive Working Groups
The Learning and Guidance Centre wanted to approach the deficiencies in the
way first year students learn. Their learning is very often limited to a
surface approach, characterised by memorising and reproduction.
IWG are created with the following two aims in mind:
(1) to train and to stimulate students in:
--activities which are fundamental in the process of knowledge
assimilation: relating, structuring, analysing;
--activities to control the processing of learning: planning, diagnosing,
evaluating, reflecting;
(2) to change, if necessary, the cognitive structure of the student in
order to build new and correct concept images.
IWG are student- instead of teacher-orientated and the method induces the
active participation of the student in the regulation of his or her own
learning process.
The premise of IWG is that the efficiency of a study-method course
increases when study skills are embedded in a content-dependent context.
Therefore, IWG are organised in parallel with, but closely related, to the
regular courses of physics, mathematics, chemistry and biology. In this
way, students experience very directly the benefit of an appropriate study
method and supplementary study load is minimised.
The instructor interacts on a metacognitive level, although manipulating
contentdependent concepts and tasks (Vermunt, 1992). The ultimate goal is a
shift from external regulating activities (by the instructor) to individual
internal regulating activities (by the students).
Theoretical Framework
The key words and ideas related to this study are constructivism, processorientated instruction, learning process, learning style, interaction
between instruction and learning, in the sense used by Vermunt (1992).
Constructivism
The main idea is that learning is an active and constructive process (Paris
& Byrne, 1989). Understanding is more complex than the mere intake of
knowledge: an active integration process occurs; all previous knowledge,
competencies and experiences are important for the learner to construct new
internal representations of information. Learners gather, (re)organise and
generalise information; their mental representations are in this way
elaborated and modified over time. Contrary to the static intake model,
understanding is seen to be subject to progressive refinement and it never
actually finishes.
One of the paradigms of the constructivist theory is `situated cognition',
meaning that knowledge and competencies can only acquire their full
significance for the learner in a domain-specific context. It entails that
`transfer' is not assumed to occur spontaneously (Duffy & Jonassen, 1991).
Learning Styles
Students differ in the way they learn. We follow Vermunt when he states
that the individual learning style is determined by the different learning
activities (assimilation, regulation) they use during the learning process,
and by their motivations towards learning and opinions about learning and
instruction (Simons, 1982; Vermunt, 1992). The learning styles are rather
stable, but they are not unchangeable.
The learning styles of the students can be diagnosed by reliable and valid
questionnaires such as the Inventory of Learning Styles (ILS) of Vermunt
(1992) and others.
The ILS contains more than 120 expressions about learning and instruction,
classified in 16 main scales (possibly with subscales), in their turn
sorted into four principal categories: learning approach, regulation of
learning, learning motivations, and beliefs on learning and instruction.
According to this inquiry, four prototype learning styles can be
distinguished at university level: meaning-directed, reproductiondirected,
concrete-directed and problematic (lack of regulation) styles (Vermunt,
1996). We can reasonably assume that the meaning- and concrete-directed
learning style prototypes (or, more realistically, a mixture of them) offer
the best perspectives for success in higher education (Vermunt, 1995).
Table I displays the meaning of the different learning style scales. Table
II shows the correlation of these scales with each of the four prototype
learning styles.
Process-orientated Instruction
In contrast to the classical instructional concepts, instruction can be set
up according to the learning process of the learner. Here, the instructor
insures that the learners perform the proper thinking activities to build,
change and make use of the mental models within a specific domain.
The issue here is that instruction can initiate, guide and influence the
learning process in order to increase the efficiency of learning.
Conceptions and learning skills are therefore taught in total coherence. It
is important that instruction is adapted to the learning styles of the
learners, in order to avoid destructive frictions.
Links with IWG
The IWG meet the properties of a process-orientated instruction in the way
that this instruction is set up according to the learning process:
--thinking skills and conceptions are diagnosed before the instruction
takes place;
--conceptions and thinking skills are treated interdependently;
--students are active participants; they are trained, on the one hand, in
activities which are fundamental in the process of assimilation of
knowledge and, on the other, in activities that control the processing of
learning.
In this research, we investigate the interaction of the IWG instruction
with the students' learning activities and performance.
Method
Subjects
The student group we investigated included medicine, dentistry and
biomedical students. This choice was made only for practical reasons, but
some results (for instance motivational aspects) have to be interpreted in
this context.
We used the following experimental design:
--the experimental group (n = 15): students who participated in IWG
mechanics frequently (at least four of the eight sessions),
--the control group (n = 46): students who did not participate in IWG
mechanics at all.
Data and Tests
For both groups, we considered following data:
Quantitative Data. Pre-test results: scores on a prior knowledge test of
mathematics administered at the start of the first semester; post-test
results: scores on a preliminary exam (at the end of the first semester) in
mechanics; learning style data: based on the ILS questionnaire developed by
Vermunt, administered at the start of the academic year (ILS1) as well as
at the end of the first semester (ILS2), just after the preliminary exams.
The total scores obtained by the students on each scale of ILS were
transformed into ordinal scale scores (from 1 to 5) by normalisation, after
considering the frequency distributions.
Qualitative Data. We realise that possible quantitative effects have to be
interpreted carefully (Entwistle, 1994b) as they could be attributed to
factors independent of the IWG. Additional qualitative analyses are needed
to clarify these effects and to make the conclusions of the quantitative
analysis more reliable.
In order to reveal their learning experiences, 16 students were interviewed
(eight students from the experimental group and eight students from the
control group) at the end of the first semester, after the learning style
inquiry was administered for the second time. They were all interviewed
individually for about 1 hour in an open way: the interview schedule
specified themes rather than detailed questions. The goal was to gather
information on their learning activities their motivation towards and
beliefs on learning and instruction, before entering university as well as
throughout this first year.
Results
Research Question 1
In what way does the experimental group show different learning style
characteristics from the control group, at the start and at the end of the
first semester?
Frequency distributions for the ILS scores were considered. Table III shows
the learning style characteristics and changes during the first semester
for both student groups.
Globally, it is striking that the learning style of the control group was
rather stable throughout the first semester; more pronounced changes can be
observed for the experimental group.
The figures in bold in Table III suggest positive effects possibly caused
by the intervention through IWG.
Learning Approach and Regulation
--an increase in higher scores for the experimental group and a status quo
for the control group for deep approach learning activities and selfregulating activities;
--a higher increase for elaborate approach activities for the experimental
group;
--a decrease in higher scores for external regulation for the experimental
group.
Motivation/Conceptions
--a decrease of certificate and self-test orientation for the experimental
group;
--a greater increase of personal interest for the experimental group;
--an increase of vocational orientation for the experimental group, a
decrease for the control group;
--an increase in higher scores for use of knowledge for the experimental
group and a status quo for the control group;
--almost no effects were observed on intake of knowledge and a slight
decrease was observed in construction of knowledge.
Globally, based on these figures, one can say that the general learning
style profile of the experimental group at the beginning of the academic
year was not very promising compared to that of the control group.
Initially, the experimental group showed a larger tendency towards surface
approach, external regulation and self-test orientation and less tendency
towards deep approach. No strong effects were expected on self-regulation,
as the scores were already higher at the start in the experimental than in
the control group.
At this stage of the research, effects on deep approach and self-regulation
are important considering the goals of IWG. The status quo for intake and
construction of knowledge give rise to concern.
Nevertheless, one has to be careful with these conclusions, because, for
instance, a status quo in percentages can conceal an internal shift of
scores within a group.
Research Question 2
In what way do the learning style characteristics of each student group
change during one semester?
The cross-tabulation in Table IV confirmed the positive effects on deep
approach.
Comparing the areas III and III' of both cross-tabulations, the progress in
deep approach activities was clearly larger for the experimental group.
Similar results were obtained for self-regulation (more progress for the
experimental group), external regulation (greater decrease for the
experimental group) and personal interest (greater increase for the
experimental group).
It is positive to note that the scores on intake of knowledge were rather
low (for both groups); unfortunately, no better results were obtained for
the experimental group. Nevertheless, the relative low scores on
construction of knowledge confirm our concern: more than 60% of the
experimental group preserved or reinforced the belief that construction of
knowledge is not one of their tasks, although they improved in deep
approach learning activities.
Overall, we can say that the effects of IWG on the learning style are
rather satisfactory.
Research Question 3
How is progress in performance related to changes in learning style?
Considering Learning Activities. For the entire population of students
compared to their evolution in deep learning approach, we investigated
those students performing well on the pre-test and those performing well on
the preliminary exam. This is shown in Table V.
The group of students who improved considerably (Area III) for the deep
learning approach seemed to have the greatest chance of success in the
preliminary exam, although they did not perform so well on the pre-test.
Relating this result with the positive effect of IWG on deep approach (see
Table IV), we conclude that IWG succeeded in enhancing the students'
chances of success by improving their deep learning approach.
Similar relations were found between an increase in self-regulation and
improvement of performance. Combined with the previously mentioned positive
impact of IWG on self-regulation, we again can conclude that selfregulation is one of the factors explaining the positive effects of IWG on
performance.
For external regulation, we detected an interesting subtle distinction: (a)
in the experimental group, a decrease in external regulation went together
with an increase in study success; (b) in the control group, on the
contrary, an increase in external regulation enhanced the chances of
success.
This last point was explained through the interviews with the students of
the control group: lack of time was such a crucial problem that making use
of external regulation (any advice of the teacher) could help and save
time.
Considering Motivations. Here, we notice that an increase in vocational
orientation motivated the students of the experimental group to progress in
performance; the contrary was true for the control group. Moreover, an
increase for self-test orientation was a greater stimulant of good
performance in the control group than in the experimental group.
Research Question 4
How are changes of learning style characteristics interrelated in each
student group?
It is striking that for the experimental group the correlations between
differences in scale scores (ILS2-ILS1) were much more pronounced than for
the control group. Shifts in learning scales for the experimental group
seem to be less coincidental, but instead more related to the entire
learning style, as shown in Table VI.
For instance, a shift in scores for deep approach was more positively
correlated with a shift in personal interest and more negatively correlated
with a shift in self-test orientation for the experimental group than for
the control group. In the latter group, this deep approach shift was
positively correlated with a surface approach shift, which was not the case
for the experimental group. Striking are also the shifts in external
regulation which were more for the experimental group than for the control
group correlated with shifts in surface approach, self-test orientation,
ambivalence, intake of knowledge and lack of regulation.
Research Question 5
Which learning style characteristics are indicators of progress in
performance or of an evolution to a more appropriate learning style, or
which students would benefit the most of IWG intervention?
Progress in Performance. We simultaneously analysed the results on ILS1 and
shifts (positive or negative) in performance from pre-test to preliminary
exam, for the experimental group as well as for the control group. The
results are displayed in Table VII.
A necessary condition at the start to participate in IWG appears to be that
students are personally interested (low personal interest is fatal both for
the experimental as for the control group), that they are not too
ambivalent and that they have a rather strong belief about construction of
knowledge (IWG affect neither ambivalence, nor this belief about
construction of knowledge).
A low deep approach tendency (according to the positive effect of IWG on
this learning activity) and a high need for external regulation did not
turn out to be negative characteristics at the start.
Changes in Deep Approach. As we noticed before that an increase of deep
approach activities enhances the chances of success in examinations, we
additionally investigated in Table VIII if predictions were possible for
those shifts in deep approach.
Students of the control group who increased their deep approach learning
activities or preserved a good deep approach tendency are characterised by
low lack of regulation and low ambivalence at the start. High lack of
regulation and high ambivalence appear not to hinder students of the
experimental group to evolve positively in deep approach learning
activities.
The previously mentioned favourable starting characteristics according to
performance (high personal interest, low ambivalence, high construction of
knowledge) seem also important at the start in order to improve deep
approach activities.
It is now impossible to define which starting characteristics of the
learning style can be indicators of improvement in learning style or in
performance. Previous observations suggest that motivational aspects and
beliefs at the start are more important to enhance success by IWG than
favourable learning and regulation activities, except for external
regulation. However, previous research on effects of IWG (Crabbe et al.,
1993) showed that effects on performance were still significant after
considering motivation as a co-factor.
We want to remark that through the interviews we understood that most of
the students participated in IWG at the start due to a need for external
regulation.
Interview Findings
In general, the results on the ILS were confirmed by the interviews.
However, the fact that the ILS investigates the global learning style while
our research is focused on the IWG and performance in physics should not be
overlooked. For instance, a high memorising activity (according to ILS2)
may be explained by the high amount of memorising needed to study biology
or chemistry.
Students with a good result in their preliminary exam often voice that the
IWG were a good help in preparing for the exam and that IWG motivate them
to study. The good result in their exam was also considered a reward for
their efforts and as a motivation to carry on.
Students from the experimental group said they understand the material if
they can rephrase it in their own words, if they are able to help other
students solving their problems, or if they can apply their knowledge. It
is striking from the interviews that students of the control group, on the
contrary, use external factors to confirm their knowledge. They evaluate
themselves by computer tests or by the scores on the preliminary exams.
Some students participate in IWG without knowing what IWG is all about.
They do not participate in IWG with the intention to change their learning
methods. Some participate to understand the material better. Once students
have participated a few times, the IWG become an important factor of their
external regulation. They said that they learned to work with the material
in a broader and deeper way, and that they learned to find connections.
As IWG are very interactive, students voice that they learn a lot from each
others' mistakes.
In general, IWG are considered to be a fruitful support and a motivation to
study. However, some students get frightened when they find out that a
whole story lies behind the taught facts. Before attending an IWG, students
are asked to prepare a particular part of the material. As a consequence,
students of the experimental group keep up with the study material.
Some students of the control group revealed that they had consulted the
general counselling office of our institution. We realise that this may
influence some positive evolutions of this group on the level of their
learning style. Moreover, the fact that students of the control group have
been subjected to the learning style inquiry may have influenced their way
of learning by becoming conscious of some defects.
Conclusion
The analyses revealed that IWG enhance deep approach learning activities
and selfregulation, which, in their turn, increase the chances of success
in examinations. This was precisely one of the main goals of IWG. Moreover,
we noticed that IWG affected the learning styles more globally, in a
coherent way.
The interviews revealed that the scores on the ILS closely corresponded to
the real activities and beliefs of the students, and therefore it is a
reliable instrument for our research goals.
The result of this investigation will have effects on the IWG methodology;
though we noticed that IWG enhance deep learning approach and do not really
affect the belief on construction of knowledge. Special tasks to approach
this deficiency have to be developed. This looks comparable with the
tackling of misconceptions: one cannot change the concept image by only
correcting the process; students have to be confronted with problem
situations in such a way that the erroneous concept is not sufficient to
solve the problem and that only a correct concept can offer this
opportunity.
On the level of selection criteria for participation in IWG, more learning
style characteristics will be taken into account.
Much further investigation has to be done; firstly, to determine areas in
some subspaces of the multidimensional state space which will correspond to
`good' or `bad' learning style profiles (related with performance on
examinations) and, later, to adjust IWG so that `bad' profiles can evolve
to become `better' ones.
Acknowledgements
We want to thank EIs Robbrecht for her advice about the interviews and
Telidja Klai for her advice, for her concrete participation in the
interviews, as well as for her critical analyses of the transcriptions.
Both psychologists are working at the study counselling office at our
university. We are also very grateful to Nicole Fux for her detailed
analysis of the interviews.
Correspondence: Anne Schatteman, Vrije Universiteit Brussel,
Zelfstudiecentrum B, Pleinlaan 2, B-1050-Brussels, Belgium.
TABLE I. Learning style: scales and categories
Learning approach
learner performs cognitive processing
activities such as:
Deep approach
--relating structuring, critically
analysing, selecting
Surface approach
--analysing step-by-step, memorising,
reiterating
Elaborate approach
--concretising, applying knowledge
Regulation of
Learning
Self-regulation
the learner performs metacognitive
regulation activities such as:
--orientating oneself before tackling
a learning task,
--planning, process evaluation,
self-testing, diagnosing, evaluating,
reflecting
External regulation
Lack of regulation
the instructor is being called upon for
the activation and execution of
regulation activities
any attempt by the learner to regulate
the learning process fails
Study Orientations
Certificate-orientated motivation by the future obtaining of a
degree or certificate
Self-test orientated
the learner is motivated by testing
her/his learning abilities, proving
what she/he is capable of
Personally interested motivation stems from geniune interest
in a specific domain or the
opportunities to enrich oneself
Vocation-orientated
the prime goal is skilling oneself for
a specific vocation
Ambivalent
insecure, doubtful attitude towards
learning
Conceptions
Intake of knowledge
Construction of
knowledge
Use of knowledge
Stimulating education
Co-operation
performing learning activities is
considered to be the task of the
instruction; the learner views
her-/himself as a passive absorber of
knowledge
the learner assumes her-/himself the
responsibility of performing learning
activities; learning is viewed as
gaining insight in the learning
material and grasping the relations
between the components of it
emphasis is put on the expected
practical use of the learning
material; concretising activities
are seen as an important task of
learning and instruction
the learner accepts her/his task as
processor of learning material, but
expects the instruction to provide
the necessary stimuli
with fellow students and sharing
learning tasks is highly valued
TABLE II. Correlation of the learning style scales with each of the four
prototype learning styles[*]
Legend for Table:
A
B
C
D
-
meaning
reproduction
application
problematic
Learning Style
Learning Approach
Deep approach
Surface approach
Elaborate approach
A
B
C
X
X
X
X
D
Regulation of Learning
Self-regulation
External regulation
Lack of regulation
Study Orientations
Certificate-orientated
Self-test-orientated
Personally interested
Vocation-orientated
Ambivalent
Conceptions
Intake of knowledge
Construction of knowledge
Use of knowledge
Stimulating education
Co-operation
X
X
X
X
X
X
(X)
(X)
(X)
X
X
X
X
(X)
(X)
X
X
X
[*] X = high positive correlation;
(X) = still correlating but less important.
TABLE III. Learning style characteristics and changes during the first
semester for experimental and control groups
Experimental group
ILS1
I (%)
ILS2
II (%)
I (%)
II (%)
20
13.3
53.3
13.3
46.7
20
20
33.3
33.3
53.5
46.7
40
Regulation of Learning
Self-regulation
External regulation
Lack of regulation
26.7
20
26.7
40
53.3
46.6
26.7
26.7
33.3
53.3
40
40
Study Orientations
Certificate-orientated
Self-test-orientated
Personally interested
Vocation orientated
Ambivalent
40
13.3
33.3
46.7
33.3
33.3
53.4
26.7
40
33.3
40
33.3
26.7
20
20
20
33.3
46.7
60
40
Conceptions
Intake of knowledge
Construction of knowledge
Use of knowledge
Stimulating education
Co-operation
6.7
53.3
26.7
26.7
33.3
33.4
33.4
20
26.7
26.7
20
66.7
33.3
40
40
33.3
20
40
40
26.7
Learning Approach
Deep approach
Surface approach
Elaborate approach
Control group
ILS1
I (%)
Learning Approach
Deep approach
37
ILS2
II (%)
I (%)
II (%)
34.8
34.8
39
Surface approach
Elaborate approach
37
34.8
32.6
28.2
39.1
34.8
37
41.3
Regulation of Learning
Self-regulation
External regulation
Lack of regulation
43.5
37
43.5
21.7
39.1
13
32.6
37
45.7
28.2
41.3
19.5
Study Orientations
Certificate-orientated
Self-test-orientated
Personally interested
Vocation orientated
Ambivalent
21.7
21.7
50
26.1
41.3
47.8
41.3
32.6
47.8
19.6
32.6
28.3
37
26.1
45.7
43.5
40
41.3
39.2
26.1
Conceptions
Intake of knowledge
Construction of knowledge
Use of knowledge
Stimulating education
Co-operation
34.8
52.2
26.1
39.1
30.4
23.9
24.9
21.7
21.7
39.1
34.8
50
23.9
37
34.8
17.4
23.9
23.9
26
37
Frequency distributions of the scores for the ILS were
considered. Displayed are, for both student groups and for
each scale of the ILS, relative frequencies of lower scores
(column I: scores = 1 or 2) and of higher scores (column II:
scores 4 or 5), for the two administrations of the ILS
(ILS1: start of first semester; ILS2: end of first semester).
TABLE IV. Change of deep approach during the first semester
TABLE V. Evolution in deep approach versus performance for the total
population of students.
TABLE VI. Intercorrelations between the changes of the learning scales
after the first semester
vdeep
vsurf
vselfr
vext
vlackr
vcer
vdeep
vsurf
vselfr
vext
vlackr
vcer
vvoc
vselft
vpers
vamb
vint
vconstrk
X
0.1076
0.7431
-0.0653
0.4071
X
0.6973
X
0.5377
-0.2893
0.1467
-0.5036
0.4305
-0.3876
-0.4729
0.1977
0.2183
-0.4729
0.4572
0.2869
0.3570
-0.0157
X
0.4150
0.3869
0.3876
0.4489
0.5457
0.6172
0.1313
-0.0698
-0.1164
X
0.1511
X
0.6847
0.1436
-0.2276
vdeep
vsurf
vselfr
vext
vlackr
vcer
vvoc
vselft
vpers
vamb
vint
vconstrk
vvoc
vselft
vpers
0.3346
0.2505
0.1946
0.3281
0.4668
0.0672
0.2104
-0.0768
X
-0.3258
-0.0359
X
vamb
vint
0.4412
0.4331
-0.2353
0.1477
0.1037
0.3568
0.0629
0.3069
0.3912
0.1361
0.0255
0.0963
0.3894
X
0.3347
0.1130
X
X
0.4038
0.3142
-0.4060
0.1131
vconstrk
X
vdeep = `deep ILS2' minus `deep ILS1' for the deep approach
learning scale. The figures below (above) the diagonal represent
the correlations between the learning scales for the
experimental (control) group. Figures in bold show differences
between the two student groups.
TABLE VII. Initial learning style indicators for progress in performance
TABLE VIII. Initial learning style indicators for changes on the deep
approach learning scale for experimental (Exp.) and control (Contr.) groups
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~~~~~~~~
By A. SCHATTEMAN, E. CARETTE, J. COUDER & H. EISENDRATH, Vrije Universiteit
Brussel, Brussels, Belgium
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Source: Educational Psychology, Mar-Jun97, Vol. 17 Issue 1/2, p111, 15p, 8
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Item Number: 9706014971
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Title: Secondary school teachers and learning style preferences...
Subject(s): LEARNING strategies; EDUCATION -- Evaluation
Source: Educational Psychology, Mar-Jun97, Vol. 17 Issue 1/2, p157, 14p, 1
chart, 7 diagrams
Author(s): Lawrence, M. Veronica M.
Abstract: Investigates the preferred learning styles of secondary school
teachers and managers using the Honey and Mumford model of learning styles.
Overview of the Honey and Mumford learning style model; Learning style in
an educational context; Rationale for research; Data collection and
analysis; Method used in the research; Discussion of the research findings;
Further research.
AN: 9706014982
ISSN: 0144-3410
Database: Academic Search Premier
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SECONDARY SCHOOL TEACHERS AND LEARNING STYLE PREFERENCES: ACTION OR
WATCHING IN THE CLASSROOM?
ABSTRACT
The problematic issue in education is in applying learning styles research
to classroom settings in schools where a range of teacher and student
learning style preferences operate simultaneously. The preferred learning
styles of secondary school teachers and managers were investigated using
the Honey and Mumford model of learning styles. The Learning Styles
Questionnaire (LSQ) developed by Honey and Mumford (1986) was used in this
research. The LSQ identifies four learning style preferences: Activist,
Reflector, Theorist, Pragmatist. Data was collected (1989 1992) from a
random sample of 353 Main Professional Grade (MPG) (now known as the Common
Pay Spine [CPS]) teachers and 47 senior managers working in Local Education
Authority (LEA) maintained secondary schools. Findings indicated that in
the sample: (i) Teachers tended to have similar learning style preferences,
namely, Reflector with a back-up preference for Theorist. Their least
preferred style was Pragmatist. (ii) Where learning style preferences
differ between teachers, these could be accounted for by differences in
subject specialism. Using a two-way analysis of variance, a highly
significant interaction was found between subject taught (12 subject
specialism) and teachers' learning style preference; (iii) Significant
differences in learning style preferences were found between MPG teachers
and senior managers in schools. The paper finishes with a brief description
of the focus of the research into the role of learning style preferences in
secondary school teachers' classroom management. Variables being
investigated are teaching style, including teachers' beliefs and values
surrounding learning and teaching (evidenced by their language patterns);
observable teacher behaviour; and students' altitude to learning.
It has been suggested that learning styles are a permanent part of human
behaviour (Kolb, 1976, 1984; McCarthy, 1982; Honey & Mumford, 1986, 1992;
Curry, 1983). They are, however, considered to be a flexible structure,
rather than set, unchangeable personality traits (Fielding, 1994). There is
a question about the extent to which a person's learning styles can be
changed.
Curry's description of the `onion model' of learning styles goes some way
towards clarifying the level of malleability of learning styles. Curry
proposed that measures for identifying learning styles could be grouped
into three main types which varied in the extent to which they were likely
to be influenced by extraneous factors, such as environment. Curry
described an `onion model' of learning styles with cognitive personality
style as the innermost layer and virtually unchangeable. The second layer
of the onion model was the `information-processing style'. This was the way
in which a person processed information from the environment, adapting it
in accordance with their underlying personality. It was at this level of
interaction, between personal predisposition and how a person processes the
information bombarding them from their environment, that this research was
concerned. The Honey and Mumford Learning Styles Questionnaire (LSQ)
(1986), selected for this research, falls into the second layer of the
Curry `onion model'. The LSQ identifies style preferences which are
compatible with an individual's personality predisposition and which are
still open to change and development. The outermost layer, `instructional
preference', referred to an individual's choice of learning environment. It
was the layer most likely to change and to be influenced by external
factors. The importance of the model has been in helping to group,
according to function, the range of instruments associated with learning
styles. Much of the learning styles research developed within the context
of experiential learning. Many of the instruments developed to elicit
learning styles use experiential learning as a framework.
The notion of experiential learning was initially developed by Kolb.
Sequential stages through which a person has to go in order to learn
effectively was the key to the `experiential learning model' proposed by
Kolb. It is suggested that effective learners are competent in each of the
four stages of the learning cycle (Kolb et al., 1974; Honey & Mumford,
1986, 1990b, 1992). Four stages in the experiential learning cycle were
described by Kolb through which an individual will pass sequentially,
beginning with having an experience, through to reflecting upon that
experience, prior to increasing their understanding of that learning by
placing it within a theoretical framework. The final stage in the cycle is
one whereby the individual will test out the experience in a practical
setting. Learning has occurred only when the sequence is followed through,
according to Kolb. People may vary in relation to where they begin the
sequence, some, for example, preferring to start by thoroughly reviewing
relevant data rather than direct associated experiences. The two continua
functioning in learning styles identification, as described by Kolb-namely, `doing and watching' and `thinking and feeling'--hold a relative
emphasis with the four stages of learning (see Fig. 1). The four learning
style preferences identified by Honey and Mumford are matched to the four
stages of the Kolb experiential learning cycle in Fig. 1.
Honey and Mumford Learning Style Model
Honey and Mumford developed the notion of Kolb's learning cycle and
designed an 80-item, forced choice (agree/disagree) questionnaire. The
Learning Styles Questionnaire (LSQ) can elicit a person's learning style
preference by focusing on their behaviour. The four preferred styles of
learning were identified as: Activist, Reflector, Theorist and Pragmatist,
and match the four stages of the Kolb experiential learning cycle. People
with a style preference of Activist gain from learning which is action
based and immediately experienced. People with a Reflector learning style
preference opt for work which involves data gathering and analysis. The
Theorist preferred learning style focuses on analysing and synthesising
information, while people with a Pragmatist learning style preference need
to see the direct application of their learning in helping plan practical
solutions to their problems. The Honey and Mumford model has been applied
to the educational sector with school managers in eliciting their learning
style preference, with a follow-up study using a diagnostic questionnaire
to clarify subjects' learning orientation (Seymour & West-Burnham, 1989,
1990; Kelly, 1995).
Honey and Mumford's work on preferred learning styles (1986,1992), which is
developed from the Kolb model, was selected for use with teachers in this
study. It has easily understood language by focusing on behaviours, is
applicable to school environments and holds a developmental focus. The
Learning Styles Questionnaire (LSQ) and associated materials proved a
powerful motivator in encouraging teachers to review their own learning.
For pupils and students in schools, the outcome was one of adjusted
classroom practice towards a more learner-centred approach.
Learning Styles in an Education Context
In some education circles, it is suggested that an awareness and
understanding of learning styles may provide another dimension to our
understanding of, for example, individual differences in learners in
schools, colleges and universities (Entwhistle & Ramsden, 1983; Gibbs,
1992; Fielding, 1994; FEDA, 1995). Indeed, Fielding forcefully argues that
an understanding of learning styles should be `a student entitlement and an
institutional necessity' (1994, p. 393).
Research into learning styles has primarily attended to students in higher
education (Kolb; Gibbs; Entwhistle & Ramsden; Biggs, 1993) and adults in
the business and commercial environment (Kolb, 1979; Honey & Mumford,
1990a; Mumford, 1982, 1987). Much of the published research has been on the
psychometric properties of the various instruments designed to elicit
people's learning style (Freedman & Strumpf, 1978; Allinson & Hayes, 1988,
1990; Fung et al., 1993). The work of McCarthy (1987, 1990) is possibly the
most well known in illustrating how the curriculum can be designed to
reflect the four stages of experiential learning as described by Kolb.
Evidence of its application in British schools and universities, however,
is scarce.
The number of studies focusing on the effect of learning style on classroom
practice in schools in Britain, or in management training, is comparatively
small (Kelly, 1995). Indeed, the possibility of a link between a teacher's
own learning style preference and the way they manage their classroom has
yet to be investigated. Observation and anecdotal evidence suggest a link
(Lawrence, 1992).
In the secondary school arena, teaching pupils and students has focused on
the specifics of teaching itself, such as: lesson planning; depth of
subject knowledge; types of teaching techniques; resource issues; and,
ironically perhaps, disaffected learners. Application of knowledge about
how people learn, the dynamics involved and how this might affect classroom
learning has taken a back seat. More so, perhaps, since the advent of the
National Curriculum, GNVQs and NVQs. Teachers are expected to make radical
adjustments to the way they manage their classrooms to meet the
requirements of the National Curriculum, changing expectations of pupils
and prospective employers. Discovering ways whereby teachers' attention
could be directly focused on aspects of learning, therefore, is important.
A learning styles approach enabled teachers to focus on how they teach,
without implying deficiency in their present activity. Instead, it simply
provided a welcome alternative for further understanding of what was
happening in the process of learning between learner and learning, teacher
and taught, and how teachers managed their classrooms. Experiential
learning offered a way of establishing learning as a process. Learning
styles offered teachers an insight into individual differences in learning.
The attraction of a learning styles approach lay in it being both directly
applicable to teachers' own experience of learning as learners and to their
experience as teachers in schools teaching others. By drawing teachers'
attention to their own learning patterns, two things can be established:
(1) that learning is a process; and (2) people learn in different ways.
The suggestion is that within a class a teacher could expect to have a
range of pupil and student learning styles represented. If the goal is to
meet the range of learning style preferences, using predominantly one
teaching method, it is therefore not likely to do this. Furthermore, it may
be that teachers are influenced by their own learning style preferences. It
would appear reasonable to suggest that a person is more likely to work in
a way which matches their own learning style preference. In teachers, this
would suggest development of a particular style of classroom management
over and above others. If teachers teach and manage their classrooms
according to their learning style preference, pupils' learning will be
affected. For example, a science teacher with a learning style preference
of Activist and back-up Reflector style, rearranged the laboratory so that
pupils sat in a semi-circle, in contrast to colleagues who retained the
separate rows. The interaction and dynamics were different in the
laboratories.
Though there is no conclusive evidence to suggest that matching learning
style preferences in learners significantly enhances their learning
(Davidson, 1990), anecdotal evidence suggests that continued mismatching
will detrimentally affect motivation and attitudes to learning. This
becomes an even greater issue if, as a group, teachers have similar
learning style preferences which are different to their pupils' learning
style preferences. Add to this a possibility of any differences in learning
style preferences between teachers being due to different subject
specialism and the need to identify whether a link between classroom
management and learning style becomes apparent. The secondary school
curriculum in England and Wales is structured according to separate subject
specialisms. Pupils in any one day will regularly be taught by different
teachers, probably with different learning styles.
Learning styles research suggests a range of things for classroom teachers.
Firstly, there is likely to be a diversity in learning style preferences in
any one classroom (Lawrence, 1992). The question arises as to whether one
particular teaching strategy or set of strategies will suit all learners.
The answer is that it will not. Unless the teaching methodology bridges
each of the four stages in the learning cycle (Kolb, 1984) and can embrace
a wide variety of techniques, any pupil/student whose style does not match
is likely to be disadvantaged. There is an issue for teachers as to whether
to teach to existing style preference or to enable learners to develop
lesser preferred styles. The answer would seem to lie in a balance. An
implicit suggestion in learning styles research is that being faced with
learning opportunities which consistently mismatch a person's preference is
likely to lead to disinterest, lessening in motivation and a decrease in
the likelihood of learning. The reverse is also suggested. However, the
debate, on matching style preference to teaching method, is inconclusive.
The evidence supporting the notion that matching reaming styles with
teaching styles will improve performance is mainly anecdotal and widely
generalised (Davidson, 1990).
Rationale for Research
From the training events run over a period of 3 years (1989-1992), patterns
of learning style preferences seemed to emerge among teachers and their
managers. Moreover, the groupings which emerged appeared to reflect
differences in how teachers and senior managers solved tasks set in the
training programmes, classroom management and differences between teachers
and their managers in their attitude towards change. This prompted the
systematic collection of the information relating to learning style
preferences of the teachers and senior managers attending professional
development sessions. Were there similarities between teachers' learning
style preferences? What effect did this have on the way they tackled tasks
in professional development sessions? What impact did this have on the way
they approached their jobs as teachers? Was there a link between learning
style preferences and classroom management and their selection of teaching
approaches? Where there were differences in learning style preferences
between teachers, were these due to differences in subject specialism?
Data Collection and Analysis
Details collected from teachers in the sample included: school and LEA
name, gender, initial qualification, subject taught, raw scores from the
LSQ, learning style preferences using the Honey and Mumford norms, and
status (i.e. MPG teacher or senior manager). Teachers and managers were
told that all information would be regarded as confidential.
The percentiles were categorised by Honey and Mumford's norms for the raw
scores of the 400 respondents: very strong preference, strong preference,
moderate, low preference and very low preference. The dominant learning
style preference is signified by a score at strong or very strong
preference level.
For the purpose of the analysis, each of the learning styles was rated on a
separate scale so that any pattern of preferences was possible. Learning
style preferences were therefore dealt with as separate variables in the
two-way analysis of variance, which are used throughout. Table I provides
the information relating to teachers' and managers' learning style
preferences by subject specialism and status.
Method
Subjects
Learning style preferences of 353 secondary school teachers and lecturers
in further education colleges were collected over a period of 3 years
(1989-1992). A sample of 47 school senior managers was taken. All were from
the maintained sector. The opportunity for taking the sample arose from
training programmes in `learning and teaching styles and environments'.
Some were organised as discrete 4-day courses, others were whole-school
training days. All participants in the programmes agreed that the data
could be used for research purposes.
Instrument
A number of self-administered inventories is available by which teachers
could discover their own learning style preference. The Learning Styles
Questionnaire (LSQ) developed for managers by Honey and Mumford (1986) was
selected rather than either Kolb's Learning Style Inventory (1976) or the
Myers-Briggs schedule Myers (1962). The LSQ focuses on what a person does,
rather than asking direct questions about their approach to learning. The
LSQ shows a reliability correlation of 0.89, with individual styles
correlating between 0.80 and 0.95 (Honey & Mumford, 1992). Its face
validity has a degree of accuracy, in that the results match consistently
respondents' self-perceptions. However, its technical validity and
predictability are more difficult to ascertain (Allinson & Hayes, 1988,
1990; Fung et al., 1993; Sadler-Smith, 1996). The LSQ is contained in the
`Manual of Learning Styles' (Honey & Munford, 1986; 1992). The
questionnaire was used to elicit teachers' own learning style preferences.
Gender
No significant differences were found between men and women in the sample
and learning style preferences (F = 0.772; df = 3,1212; p < 0.51). This
reflected the findings by Honey and Mumford (1992) which indicated that
gender differences were minimal.
Senior Managers and MPG Teachers
The two-way analysis of variance between MPG (now, CPS) teachers and senior
managers (heads and deputies, not heads of department) and learning style
preference found a significant interaction (F = 2.208; df = 12,1146; p <
0.01). The findings are illustrated in Fig. 2. The interaction suggests a
difference in learning style preference probably linked to differences in
role and function in school for senior managers and teachers.
The dominant style preference for teachers was that of Reflector with a
back-up style preference of Theorist. The combination suggests a preference
for carefully gathering data, conceptual analysis and synthesis of the data
in making decisions and solving problems (their least preferred style was
Pragmatist). Teachers with this combination of learning style preference,
when convinced by the evidence, will work hard at making the identified
adjustments. Their focus would be on observation with a people focus rather
than a task focus. One of the difficulties is that they often tend towards
perfectionism with a strong value of `right and wrong' and low risk-taking
behaviour. Their preparation and rigour can lead to, for example,
procrastination, inflexibility and delays in implementing change.
The dominant learning style preference for senior managers was for Theorist
typified by behaviours associated with analysing, synthesising and
conceptualising from which to plan strategies for action and decisions.
Having a clear focus, underpinned by supporting evidence, would be
influential in their decision-making process. Their lesser preferred styles
were that of Activist and Pragmatist, both on the `doing' side of the
continuum. This suggests a disinclination for getting immediately involved
(distancing themselves from the experience) and for planning practical
solutions. Recent research by Kelly (1995) suggests there is a shift in
learning style preference of managers in schools to a more Activist
preference suggesting `action' and `doing' in preference to previously
described behaviours of `watching' and `thinking' (Honey & Mumford, 1992;
Lawrence, 1992).
The relevance of the earlier findings for schools and how they may function
as institutions is the fact that Reflector and Theorist learning style
preferences sit within the `watching' and `thinking' sections of the
continua: `watching' and `doing'; `feeling' and `thinking' (Fig. 1, cf.
Kolb). Anecdotally, schools in the mid-1990s appear to be much more actionbased, moving from one new initiative to the next, frequently before
consolidating the learning.
Subject Specialism and Teachers' Learning Style Preference
A question regarding the learning style preferences of the teachers in the
sample arose. Were there any differences in learning style preference? To
what extent did the differences group according to the different subjects
being taught in secondary schools?
The subjects represented in the sample were: mathematics (n = 33), physics
(n = 25), chemistry (n = 25), biology (n = 28), English and drama (grouped
together, n = 59), social sciences (n = 22), geography (n = 16), history (n
= 38), art and music (grouped together, n = 14), modern foreign languages
(n = 25), physical education (n = 19), technology (n = 29), business
studies and IT (grouped together, n = 20).The sample included heads of
department as well as MPG teachers. A two-way analysis of variance was run
between preferred learning styles and subject specialism. English and drama
teachers were not included in the analysis because the number of teachers
in the sample was much larger than the other subjects (n = 59). A highly
significant interaction was found between the subject taught and the
teacher's learning style preference (F = 2.018; df = 33,846; p < 0.001).
For the majority of subject specialism (69% or (9/13), the dominant style
preference of Reflector, or Reflector/Theorist combination remained. These
were: business studies and IT, social sciences, geography, modern foreign
languages, technology, mathematics, physics, chemistry and biology. The
least preferred learning style for the majority of subject specialism was
pragmatist (54% or (7/13) (see Figs 3, 4 and 5). The learning style
preferences profile for chemistry, physics, technology and geography
teachers is identical (Fig. 3).
Art and music teachers and physical education teachers had a dominant style
preference of Activist with a back-up style of Pragmatist (Fig. 6).
This suggests that these teachers function at the practical planning
section of the `doing-watching' continuum and the direct experience section
of the `feeling-thinking' continuum (Fig. 1). This is in contrast to the
majority of their colleagues who operate within the `watching' and
`thinking' sections of the continua. History teachers and English and drama
teachers reflected the stages of the learning cycle with a dominant
preference for Activist, a back-up style of reflector, following the stages
in sequence with a least preferred style of Pragmatist (Fig. 7). The
learning style preferences profile for history and English and drama
teachers is identical.
Most of the teachers were teaching the history project, a structured
approach which partially reflected the learning cycle. This could, in part,
explain their learning style preference profile.
Discussion
The importance of the research findings is in the degree to which learning
style preferences affect the way in which teachers manage their classrooms,
including their choice of teaching methodologies.
The majority of the teachers in the sample had a learning style preference
for Reflector with Theorist back-up. This learning style preference falls
within the watching and thinking sections of the continua (Fig. 1). In
managing their classrooms, many felt most comfortable teaching in a way
which meant that they controlled both the information and the way in which
pupils would be expected to learn. Watching and listening were the
behaviours expected by teachers of the pupils. This was interesting in that
it reflected their own approach to learning evidenced by their learning
style preference and how they tackled curriculum change. They expressed a
need to know that the pupils had all the information and the only way for
this to happen was if they were to give it to them. Thorough preparation
and tight schedules often meant that there was little room for manoeuvre
within the lesson plan. A certainty that they themselves had covered all
the options appeared as a consistent characteristic among the sample
teachers (Lawrence, 1992).
Their need for thorough data collection was advantageous in encouraging
them to look at the learning styles research and experiential learning
cycle as another way of managing their classrooms. As suggested earlier,
when people with this learning style combination are convinced by the
evidence, they will work hard at making the adjustments. Their challenge
lay in increasing the risk factor by enabling pupils to learn in different
ways. An example of this was an A-level chemistry teacher, who taught two
parallel groups the same topic using different methods. Her role as teacher
was also different. Both groups had the same end test. Her experimental
group, where she had designed the topic using a greater range of teaching
methods, reflecting more closely the four stages of the learning cycle,
performed better on the end test than the group she had taught using her
usual information-giving and lecture modes of delivery. This example
illustrates how this teacher was prepared to review her classroom
management in light of her own learning about learning styles. She
explained that she would never have considered changing her classroom
approach had it not been for the learning styles research. By linking the
classroom practice to the experiential learning cycle, teachers could
adjust the emphasis of their lessons.
In contrast to the majority of subject specialists, the following group of
teachers preferred to learn from settings which offered action and
involvement; doing and feeling sections of the continua (Fig. 1). Art
teachers in the sample expected pupils to experiment, to immerse themselves
in doing the activity and to experience using the materials. Trying out an
idea, seeing what it looks like and then reflecting on the experience and
the product would be a commonly described pattern of activity. The teachers
explained how they would structure their lessons to reflect this. In
discussion, they agreed that this way of approaching felt familiar and
comfortable. This reflected their own approach to learning and their
learning style preference. They could see that pupils who wanted to watch
before being involved, or find out more about the detail before starting
could become frustrated. The Activist preferred learning style lends itself
to becoming directly involved with what is happening, being prepared to
take risks and embracing new ideas. It is interesting to note the match
between learning style preference and how art teachers described their
approach to managing the classroom.
PE teachers in the sample had a similar learning style preference of
Activist with a back-up Pragmatist style preference. Getting on with the
task, practicing specific activities and involvement are expectations
described by the PE teachers. On the whole, PE teachers became frustrated
with what they saw as unnecessary discussion and examination of the
curriculum, especially if it occurred without concrete examples which they
could use in their lessons. Being able to demonstrate in practice what was
discussed held a high premium with them. This, too, was reflected in the
way in which many managed their classes.
The profile of the sample history teachers was interesting in that it
reflected the distinct way in which the history project was taught.
Teachers would use artefacts in their lessons, encourage pupils to
empathise with the people they were studying and use an extensive range of
teaching approaches which reflected, in part, the experiential learning
cycle. This demanded of many teachers a role change as well as
familiarisation with different methodologies. A question arises as to
whether it was the demands of a new curriculum which has influenced the
teachers' learning style preference, or whether teachers with this learning
style preference were more likely to teach history.
The teachers of English, in contrast to the history teachers teaching the
history project course, did not necessarily organise their classes to
reflect the four stages of the experiential learning cycle. Rather, the
profile suggests behaviours which encourage pupils to be involved through
structured discussion, using first-hand experience, trying out unfamiliar
territory and using a variety of immediate experiences. The gap often lay
in providing sufficient space for review so that learners with a more
Reflector style preference felt hurried.
When teachers in the sample became aware of their own learning style
preferences, many commented on the way this helped them understand better
the variety of behaviours within their classes; for example, why the same
task might motivate some and not others. Many teachers decided to organise
their curriculum according to the four stages in the cycle of learning
(Kolb). This would mean that, at some time, each of the four learning style
preferences would be catered for. Other teachers described the learning
cycle and learning style preferences with pupils. They anticipated that
alerting pupils to the differences in learning style preferences might
positively affect motivation. This proved to be the case (Lawrence, 1992).
A finding which caused some concern was the negligible number of teachers
with a Pragmatist learning style preference in schools. In classrooms, for
example, this often meant little opportunity for drawing links between
learning and practical examples, or how to plan practical solutions to
problems. On a whole-school level it could lead to inertia: discussion
about, rather than doing, something to change things!
Differences between teachers attributable to learning style preferences may
account for differences in versatility of teachers in relation to being
prepared to employ, for example, a range of teaching methodologies. One may
begin to see a connection between learning style preference and how
teachers teach.
Whether it is the teacher's own learning style preference which influences
the way they teach or the nature of the subject itself which influences
decisions over the curriculum remains an important and as yet unresolved
question. This is a matter for further investigation as it fell outside the
parameters of this study.
Conclusion
The connection between learning style preference and what happens in
schools is beginning to become clearer. What is apparent is that there is a
place for learning styles research in clarifying our understanding of
classroom and whole-school dynamics.
The differences in learning style preferences found between senior managers
in schools and teachers in this study may be linked to different roles and
functions. The extent to which a person's learning style preference is
influenced by their job is not yet clear. Kelly (1995) suggests there may
be a causal relationship between role and learning style preference in
school managers as he identified a change in the profile of senior managers
in schools towards a much more Activist learning style. The Honey and
Mumford model of learning style (1986, 1992) operates at the informationprocessing style layer cf. Curry (1983). It may be that a person's learning
style preference may shift to accommodate the role they undertake.
The research findings identify that there is an interaction between a
teacher's own learning style preference and subject specialism. Anecdotal
evidence suggests a connection between a teacher's own learning style
preference and their approach to classroom management. One could speculate
as to the likelihood of being able to predict a teacher's learning style
preference from their subject specialism and then their approach to
classroom management. However, the research evidence, as yet, is
inconclusive about the exact nature of the relationship between learning
style preference in teachers and classroom management, or subject
specialism.
Further Research
The focus of the present research is to analyse the role of learning style
preferences on teachers' classroom management. There is little research
into the relationship between learning style preferences and the way in
which teachers organise their classrooms. Most of the research has
concentrated on education managers' and students' learning style
preference.
The focus of the research is towards investigating the processes involved
in how teachers manage their classrooms using learning style preferences as
a framework to analyse what is happening. What happens between teachers and
pupils/students appears further influenced by the teacher's understanding
of, and beliefs about, learning, teaching and their interrelation, if any.
Variables to be investigated are teaching style, including: teachers'
beliefs and values about teaching and learning using their language
patterns; observable teacher behaviour; and students' attitude to learning.
The case study will combine qualitative and quantitative research methods.
Correspondence: M. Veronica M. Lawrence, Institute of Education, University
of Warwick Coventry, CV4 7AL, UK. email <m.v.m.lawrence@warwick.ac.uk>
TABLE 1. Learning style preferences by subject specialism and status
Activist Reflector Theorist Pragmatist
PE
Music and Art
English
History
Chemistry
Physics
Technology
Geography
Business
Biology
Maths
Languages
Social sciences
4
4
4
4
1
1
1
1
2
2
2
2
3
1
2
3
3
4
4
4
4
4
4
3
3
4
2
1
2
2
3
3
3
3
4
3
4
4
2
3
3
1
1
2
2
2
2
1
1
1
1
1
Senior Managers
Head of Department
Teachers
1
1
2
3
4
4
4
2
3
2
3
1
4 = Dominant learning style; 1 = least preferred style.
FIG. 1. The learning cycle (Kolb, 1984) and the learning style preferences
(Honey & Mumford, 1986).
FIG. 2. Learning styles. Teachers and managers.
FIG. 3. Learning styles. Chemistry; physics; technology; geography.
DIAGRAM: FIG. 4. Learning styles. Business; biology; maths.
DIAGRAM: FIG. 5. Learning styles. Languages; social sciences.
DIAGRAM: FIG. 6. Learning styles. PE; music and art.
DIAGRAM: FIG. 7. Learning styles. English; history.
REFERENCES
ALLINSON, C.W. & HAYES, J. (1988) The Learning Style Questionnaire: an
alternative to Kolb's Inventory?, Journal of Management Studies, 25, pp.
269-281.
ALLINSON, C.W. & HAYES,J (1990) Validity of the Learning Styles
Questionnaire, Psychological Reports, 67, pp. 859-866.
BIGGS, J. (1993) What do inventories of students' learning processes really
measure? A theoretical review and clarification, British Journal of
Educational Psychology, 63, pp. 3-19.
CURRY, L. (1983) An organisation of learning styles theory and constructs,
Microfiche ED, 235 185.
DAVIDSON, G.V. (1990) Matching learning styles with teaching styles: is it
a useful concept in instruction? Performance & Instruction, 29, pp. 36-38.
ENTWHISTLE, N.J. & RAMSDEN, P. (1983) Understanding Student Learning
(London, Croom Helm).
FEDA (1995) Learning Styles (Peterborough, Potters, Meridan House).
FIELDING, M. (1994) Valuing difference in teachers and learners: building
on Kolb's learning styles to develop a language of teaching and learning,
The Curriculum Journal, 5, pp. 393-417.
FREEDMAN, R.D. & STRUMPF, S.A. (1978) What can one learn from the Learning
Style Inventory?, Academy of Management Journal, 21, pp. 275-282.
FUNG, H., Ho, A.S.P. & KWAN, K.P. (1993) Reliability and validity of the
Learning Styles Questionnaire, Journal of Educational Technology, 24, pp.
12-21.
GIBBS, G. (1992) Improving the Quality of Student Learning (Bristol
Technical and Education Services).
HONEY, P. & MUMFORD, A. (1986) The Manual of Learning Styles, 2nd Edn
(Maidenhead, Honey).
HONEY, P. & MUMFORD, A. (1990a) The Manual of Learning Opportunities
(Maidenhead, Honey).
HONEY, P. & MUMFORD, A. (1990b) The Opportunist Learner (Maidenhead,
Honey).
HONEY, P. & MUMFORD, A. (1992) The Manual of Learning Styles, 3rd Edn
(Maidenhead, Honey).
KELLY, M. (1995) Turning heads: changes in the preferred learning styles of
school leaders and managers in the 1990s, School Organisation, 15, pp. 189201.
KOLB, D.A. (1976) Learning Style Inventory (Boston, McBer).
KOLB, D.A. (1984) Experiential Learning Experience as the Source of
Learning and Development (Englewood Cliffs, NJ, Prentice-Hall).
KOLB, D.A., RUBIN, I.M. & MCINTYRE, F.M. (1974) Organizational Psychology:
an experiential approach (Englewood Cliffs, NJ, Prentice-Hall).
KOLB, D.A., RUBIN, M.I. & MCINTYRE, J.M. (1979) Organizational Psychology.
a book of readings (Englewood Cliffs, NJ, Prentice-Hall).
LAWRENCE, M.V.M. (1992) Do the preferred learning styles of secondary
school teachers show similarity across different schools, or do they
homogenise within particular organisations?, Unpublished Masters Thesis,
Nottingham University.
MCCARTHY, B. (1987) The 4MAT System (Barrington, Excel).
MCCARTHY, B. (1990) Using the 4MAT system to bring learning styles to
schools, Educational Leadership, 48, pp. 31-37.
MUMFORD, A. (1982) Learning styles and learning skills, Journal of
Management Development, 1(2), pp. 55-65.
MUMFORD, A. (1987) Learning styles and learning, Personnel Review, 16, pp.
20-23.
MYERS, I.B. (1962) Introduction to Type, Pala Alto, CA: Consulting &
Psychologists Press.
SADLER-SMITH, E. (1996) `Learning Style': frameworks and Instruments, Paper
presented at the Learning Style Conference, University of Birmingham, 19-21
April.
SEYMOUR, R. & WEST-BURNHAM, J. (1989) Learning styles and education
management: part 1, International Journal of Educational Management, 3, pp.
19-25.
SEYMOUR, R. & WEST-BURNHAM, J. (1990) Learning styles and education
management: part 2, International Journal of Educational Management, 4, pp.
22-26.
~~~~~~~~
By M. VERONICA M. LAWRENCE, Institute of Education, University of Warwick,
Coventry, UK
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Source: Educational Psychology, Mar-Jun97, Vol. 17 Issue 1/2, p157, 14p, 1
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Result 79 of 127
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Title: The learning styles of medical students: an annotated bibliography
of twenty years of research.
Subject(s): LEARNING strategies; MEDICAL students
Source: Perceptual & Motor Skills, Dec96 Part2, Vol. 83 Issue 3, p1411,
12p
Author(s): Hylton, Jaime; Hartman, Steve E.
Abstract: Presents an annotated bibliography of 25 papers on the learning
styles of medical students from 1975 to 1995. Specific inventories used to
measure individual styles; Learning Style Inventory.
AN: 9711040270
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Title: The learning styles of community college art students.
Subject(s): ART students -- Psychology; COMMUNITY college students -Psychology; LEARNING strategies
Source: Community College Review, Winter96, Vol. 24 Issue 3, p17, 10p, 2
charts, 1 diagram
Author(s): Gusentine, Stephen D.; Keim, Marybelle C.
Abstract: Determines the demographic profile and the learning styles of
community college art students. Assimilation as the most dominant learning
style; Differences in learning styles between art majors and nonmajors;
Impact of age and gender on learning approach.
AN: 9703240670
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THE LEARNING STYLES OF COMMUNITY COLLEGE ART STUDENTS
Community colleges have witnessed a changing student body during the past
several decades (Cohen & Brawer, 1996). While traditional students, 18 to
22 years of age, still account for a large percentage of two-year college
students, nontraditional students have flocked to community colleges in
record numbers. The nontraditional students, also known as new students
(Cross, 1971), include women, minorities, older adults, the academically
underprepared, and those in lower socioeconomic strata. To accommodate the
distinctiveness of new students, community colleges will need to assess the
instructional techniques of their faculty to determine the congruence
between teaching strategies and students' learning styles. One of the most
promising answers to more effective teaching is research on student
learning styles (McCarthy, 1980). Matching learning styles with teaching
styles is particularly appropriate in working with poorly prepared students
(Claxton & Murrell, 1987).
Learning styles can be defined in many ways; aspects of learning style
include personality, information processing, social interaction, and
instructional preference. Keefe (1982) defined learning style as "the
cognitive, affective, and physiological traits that serve as relatively
stable indicators of how learners perceive, interact with, and respond to
the learning environment" (p. 44). Schmeck (1983) thought of learning
styles as a predisposition to adopt a particular learning strategy
regardless of the specific demands of the learning task. Several
instruments have been designed to identify learning styles, including the
Canfield Learning Styles Inventory, the Productivity Environmental
Preference Survey, the Adult Learning Needs Survey, the Gregorc Style
Delineator, Your Style of Learning and Thinking, the Schmeck Inventory of
Learning Processes, and the Kolb Learning Style Inventor. All have been
thoroughly validated and used in numerous studies. The Kolb Learning Style
Inventory (1985) was the instrument selected for this study because of its
readability and ease of scoring; thus, only the Kolb theoretical framework
is detailed in the following paragraphs.
Kolb's (1984) theory of experiential learning was based on the work of
Dewey, Lewin, and Piaget. Kolb posited that learners must involve
themselves in learning for the experience to be educative. He maintained
that knowledge is acquired either by concrete experience or abstract
conceptualization and that knowledge is processed through reflective
observation or active experimentation. Kolb theorized that a person first
has a concrete experience (CE), and then makes reflective observations (RO)
about it. Then these reflective observations will form the basis of
abstract conceptualizations (AC) as the individual fits the observations
into generalized theories. A person will then test these theories through
active experimentation (AE). This theory forms the basis of Kolb's model,
which is a circle with two bipolar constructs (Figure 1). The constructs
describe two ways of grasping knowledge through either concrete experience
(CE) or abstract conceptualization (AC) and two ways of transforming
knowledge, namely active experimentation (AE) or reflective observation
(RO). The model is divided into quadrants between the vertical axis of the
grasping dimension and the horizontal axis of the transforming dimension.
The grasping dimension represents two dialectically opposed modes of
acquiring knowledge. The first, concrete experience, involves the learner
acquiring knowledge through direct contact with the experience. The second,
abstract conceptualization, involves using conceptual interpretation and
symbolic representation to acquire knowledge.
The transforming dimension (the horizontal axis) represents two
dialectically opposed modes of processing knowledge. The first mode,
reflective observation, involves the learner processing information
internally by reflecting on it. The emphasis of this mode of transformation
is on dealing with the information rather than trying to manipulate it. The
second mode of transforming knowledge, active experimentation, involves
manipulating information and testing it in new situations.
According to the model, a person's learning style is determined by his or
her preference for a particular phase of the cycle indicated by one of the
quadrants. Each quadrant represents one of four learning style modes, which
are labeled Divergers, Assimilators, Convergers, and Accommodators.
Divergers grasp knowledge through concrete experience and transform it
through reflective observation. Those falling into this quadrant are
imaginative, good at generating alternative ideas, and good in situations
that call for brainstorming. Divergers tend to be emotional, peopleoriented, and aware of meanings and values. They tend to specialize in the
humanities and liberal arts.
Assimilators grasp knowledge through abstract conceptualization and
transform it through reflective observation. Emphasis in this quadrant is
on inductive reasoning and creating theoretical models in order to
assimilate their observations into an integrated explanation. Assimilators
favor abstract concepts rather than people and tend to specialize in areas
such as mathematics and science.
Convergers grasp information through abstract conceptualization and process
it through active experimentation. Those with this learning style are
interested in the practical application of theories. Their greatest
strengths lie in problem-solving and decision-making. People with this
learning style tend to be unemotional and concern themselves with things
rather than people. Areas of study tend to be technical, such as
engineering.
Accommodators grasp information through concrete experience and process it
through active experimentation. Accommodators' greatest strength lies in
doing things, carrying out plans and experiments, and involving themselves
in new experiences. They are best suited for situations where one must
adapt to immediate circumstances. In organizations these people often have
action-oriented jobs and are sometimes seen as aggressive. Areas of study
tend to favor marketing and sales.
Method
The purpose of the study described in this report was to determine a
demographic profile and the learning styles of community college art
students. Two hundred students from five community colleges in southern
Illinois constituted the sample for the research. A demographic survey and
the Kolb Learning Style Inventory (LSI) were administered by the first
researcher during regularly scheduled two-dimensional studio and art
history/appreciation classes. One hundred students were enrolled in
transfer courses, and 100 were in noncredit community education classes.
All students who attended art classes on the days that the researcher
visited the community colleges were surveyed. After students completed the
instruments, the scoring procedure for the LSI was demonstrated to them so
that they could score their own inventories and ascertain their learning
styles. Students were permitted to keep the grid on which they had plotted
their scores, as well as a description of the learning styles. The
researcher then collected the completed LSIs and the supplementary
materials.
Kolb's model of learning formed the basis of his Learning Style Inventory.
The LSI is a 12-item, self-report questionnaire that asks respondents to
rank order four sentence endings with each item. The sentence endings are
ranked, 4 to 1, with 4 being the most characteristic of the person's
learning style and 1 the least characteristic. The LSI generates four raw
scores emphasizing the person's preference for CE, RO, AC, and AE, plus two
combination scores that indicate the extent to which the person emphasizes
abstractness over concreteness (AC-CE) and the extent to which a person
emphasizes action over reflection (AE-RO). A positive score on the AC-CE
scale indicates a more abstract score, and a negative score is more
concrete. On the AE-RO scale, scores are either more active or more
reflective. By plotting the two combination scores on a grid, an individual
learns which of the four learning styles he or she prefers.
Results and Conclusions
Demographic Profile
Age. The ages of the students varied a great deal (Table 1). The age
categories with the most students were 60 and older (n=59, 29.5%) and 22
and younger (n=53, 26.5%). Fewer numbers and percentages were found in the
other age ranges.
In the transfer courses, the predominant age was 22 or younger (n=53, 53%),
followed by 18 (18%) in the 23-29 group, 12 (12%) between 30 and 39, 11
(11%) between 40 and 59, and 6 (6%) who were 60 or older.
In the community education courses (noncredit), the predominant age was 60
and older (n=53, 53%). There were no students 22 and younger enrolled in
the noncredit courses, seven (7%) were between 23-29, 14 (14%) between 3039, 13 (13%) between 40 and 49, and 13 (13%) between 50-59.
Gender. Women were overwhelmingly represented in the sample. Of the 200
students, nearly 72% (n=143) were women and 28% (n=56) were men (Table 2).
One student did not furnish this information.
Men accounted for 44% (n=44) in the transfer category and 12% (n=12) in the
community education group; there were 56 women (56%) in the transfer
category and 87 (87%) in the community education group.
Age and Gender. The male community college art students were considerably
younger than the women (Table 2). Of the 56 men in the study, 28 (50%) were
22 or younger. Of the 143 women, 50 (35%) were 60 or older.
Gender and Category. Women were more likely to be enrolled in community
education courses than in transfer courses. Sixty-one percent (n=87) of the
women were in the noncredit classes and 39% (n=56) were in the transfer
classes.
Men were more likely to be enrolled in transfer courses than in community
education courses. Seventy-nine percent (n=44) of the men were in transfer
classes, and 21% (n= 12) were in the community education classes.
Art Majors. More men than women were planning to be art majors. Thirty-four
percent (n=19) of the men and 9% (n=13) of the women intended to major in
art.
Concentration. Art studio was more popular than art history. Twenty-nine
(91%) of the prospective art majors planned to concentrate in art studio
and 3 (9%) were more interested in art history.
Age of Art Majors. Most of the students who were preparing to major in art
were youthful in age. Nineteen (59%) were 22 or younger. Six (19%) were 2329, 5 (16%) were 30 to 39, and 2 (6%) were 40-49.
Reason for Taking Course. The predominant reason for art majors to take the
course in which they were enrolled was because it was a requirement. Nearly
two-thirds (n=21, 66%) indicated that the course was required, 5 (16%) were
taking it as an elective, and the remaining 6 gave other reasons. Of the
community education students, nearly all were taking the course for
personal interest, listing reasons such as enjoyment, pleasure, or fun.
Learning Styles
Dominant Learning Style of Students Planning to Major in Art. It was
hypothesized that the dominant learning style of students who planned to
major in art would be in the Diverger quadrant. According to Kolb's
Learning Style theory, students in the arts use the learning modes of
reflective observation and concrete experience. However, the largest number
of art majors in this study preferred the Assimilator learning style (n=14,
44%), followed by Accommodator (n=7, 22%), Converger (n=6, 19%), and
Diverger (n=5, 16%). Because of the small percentage of students preferring
the Diverger style, the hypothesis was rejected.
Differences in Learning Styles between Art Majors and Nonmajors. Both art
majors and nonmajors preferred the Assimilator learning style. The
preferences of the transfer students who were nonmajors were Assimilator (n
= 28, 44%), Accommodator (n = 17, 27 %), Diverger (n=13, 20%), and
Converger (n=6, 9%). A chi-square analysis (chi[sup 2]= 1.937, df=3,
p=.585) revealed no statistically significant differences between art
majors and nonmajors in their learning style preferences.
Differences Between Traditional Age and Nontraditional Age Students in
Approaching Learning. A t test was used to compare the combination scores
(AC-CE and AE-RO) between traditional and nontraditional age students (23
years of age or older). The AC-CE showed no statistically significant
differences (t=963, df= 197, p=.337), but the AE-RO was significant at the
.034 level (t=-2.138, df=197). This indicated that traditionally aged
college students processed information through reflective observation,
while the nontraditionally aged students processed information through
active experimentation.
Relationship Between Demographic Age and Learning Styles. A frequency
analysis was first prepared for the youngest students (22 years and
younger; n=53) and for the oldest students (60 and older; n=59). The
dominant learning styles for the younger students were Assimilator (47.2%),
followed by Diverger (22.6%), Accommodator (18.9%), and Converger (11.3%).
The dominant learning styles for the older students were Accommodator
(33.9%), followed by Diverger (32.2%), Converger (18.6%), and Assimilator
(15.3%). To determine whether there was a statistically significant
difference between age and learning style, a chi-square analysis was
performed. The results indicated that there was a highly significant
difference (chi[sup 2]=13.63, df=3, p=.003). The older students preferred
to learn by concrete experience and to transform it into knowledge by
active experimentation. The younger students preferred to learn using
abstract conceptualization and processing it through reflective
observation.
Relationship Between Gender and Learning Styles. A chi-square analysis was
performed to determine if there were significant differences between gender
and learning styles. The results indicated that there were no significant
differences (chi[sup 2]=5.77, df=3, p=. 123). A t test was conducted to
determine if there were gender differences in students' approaches to the
learning dimensions. There was no significance on the AC-CE dimension (t=
1.89, df= 196, p=.059), but there was a significant difference on the AE-RO
dimension (t=-1.98, df=196, p=.049). The results indicated that the women
preferred to transform information into knowledge through active
experimentation and that the men preferred to transform it using reflective
observation.
Discussion
Analysis of the demographic data revealed two distinct groups of community
college art students. Those planning to major in art were predominantly
young and male. Those taking the noncredit community education classes were
overwhelmingly older women. The findings about art majors in this study are
similar to those of Cohen (1988), who found that students who anticipated
earning a significant portion of their income from art careers tended "to
be younger, more likely male, full-time students, enrolled because of the
faculty's reputation and availability, and planning on further study in
more specialized programs" (p. 255). Differences in learning styles
according to gender were consistent with those found by Brainard and Ommen
(1977). They found that men preferred instruction that allows for greater
independence and that men like to work with inanimate objects. Women
preferred a more structured learning environment with well-organized and
adequately detailed material.
Conclusions about the learning styles of older students were not
substantiated in this study because the 60 and older group preferred the
Accommodator grouping, characterized by concrete experience and active
experimentation. McCarthy (1980), Keefe (1982), and Kolb (1984) contended
that as a person ages he or she will move toward greater levels of
abstraction.
The older women in this research resembled those studied by Cohen (1988),
who found that students over the age of 35 took art classes to satisfy
personal interest. The older women in this study apparently took art
courses not only for the instruction, but also for the social interaction.
Canfield's (in Claxton & Murrell, 1987) suggestions that older students
prefer traditional instructional formats--listening, reading, well-
organized and detailed materials and less independence--are probably still
appropriate.
Based on the characteristics of the students planning to transfer to a
four-year institution and to major in art, the following recommendations
are made for instructing them: (1) create an independent learning
environment using individualized learning programs; (2) allow students to
work on subject matter of their own choosing; (3) give assignments that are
conceptual in nature; (4) teach the theory behind the different media; (5)
teach art history by emphasizing the driving conceptual forces behind
different schools of art and during different periods of time; (6) employ
the Socratic method by allowing these students to arrive at their own
solutions; (7) give individual critiques in addition to group critiques;
and (8) provide students with individual reading lists and examples in
other disciplines such as philosophy, poetry, and dance that both support
and challenge their personal systems of belief and aesthetics.
For older students who prefer the Accommodator learning style, these
suggestions are offered: (1) create a structured learning environment using
well defined instructional goals; (2) encourage students to work on group
still lifes at least for the first painting; (3) encourage learning groups
with group critiques after each painting technique has been introduced and
dealt with by the students; (4) give assignments that are specific in
nature, detailed, and well-organized; (5) create and distribute detailed
handouts and provide reading lists; and (6) give clearly outlined
demonstrations carefully explaining each step. Art instructors who want to
accommodate the learning styles and personal characteristics of their
students are encouraged to experiment with the instructional
recommendations offered by the researchers, modifying them where
appropriate.
Other community colleges may wish to replicate this study to determine
whether or not their art students also fall into two distinct categories.
It may be that students studying art in other locations are altogether
different from those in rural Illinois community colleges. However, it may
be that art students at many two-year colleges are the young male and the
older woman.
Although the numbers of students studying art in the two-year college are
relatively small, the finding that older students in this study preferred
the Accommodator learning style has implications for further research.
Among community college students who are majoring in other disciplines, is
it possible that older students in other majors have similar learning
styles to the art students in this study? Or do older students in other
fields prefer more abstract learning styles? Only further studies using the
Kolb Learning So, le Inventory can answer these questions.
Table 1
Subjects by Age Groupings
Age
22
23
30
40
50
60
Frequency
and younger
- 29
- 39
- 49
- 59
and older
53
25
26
18
19
59
----200
Percent
26.5
12.5
13.0
9.0
9.5
29.5
----100.0
Table 2
Subjects by Age and Gender
Women
-------------------Frequency
Percent
Age
22
23
30
40
50
60
Men
-------------------Frequency
Percent
and younger
- 29
- 39
- 49
- 59
and older
25
17.5
28
15
10.5
10
22
15.4
4
18
12.6
0
13
9.1
5
50
34.9
9
------------143
100.0
56
DIAGRAM: Figure 1. - Kolb's Cycle of Learning
50.0
17.9
7.1
0
8.9
16.1
----100.0
References
Brainard, S.R., & Ommen, J.L. (1977). Men, women, and learning styles.
Community College Frontiers, 5(3), 32-36.
Claxton, D., & Murrell, P. (1987). Learning styles: Implications for
improving educational practices. Washington, DC: ASHE/ERIC Higher Education
Report.
Cohen, A.M. (1988). Art education in community colleges. Studies in Art
Education, 29(4), 250-256.
Cohen, A.M., & Brawer, F.B. (1996). The American community college. San
Francisco: Jossey-Bass.
Cross, K.P. (1971). Beyond the open door. San Francisco: Jossey-Bass.
Keefe, J.W. (1982). Assessing student learning styles: An overview. In
Student learning styles and brain behavior (pp. 43-53). Reston, VA:
National Association of Secondary School Principals.d
Kolb, D.A. (1984). Experiential learning: Experience as the source of
Development. Englewood Cliffs: Prentice Hall.
Kolb, D.A. (1985). Learning Style Inventory technical manual (Rev. ed.).
Boston: McBer & Co.
McCarthy, B. (1980). The 4 MAT system: Teaching learning styles with
right/left mode techniques. Barrington, IL: Excel Inc.
Schmeck, R.R. (1983). Learning styles of college students. In N. R. F.
Dillon and R. R. Schmeck (Eds.), Individual differences in cognition, vol.
1 (pp. 233-279). New York: Academic Press.
~~~~~~~~
By Stephen D. Gusentine and Marybelle C. Keim
Stephen D. Gusentine teaches art at Southeastern Illinois College in
Harrisburg, Illinois and works as a self-employed artist
Marybelle C. Keim is a professor in the Department of Educational
Administration and Higher Education at Southern Illinois University in
Carbondale, Illinois
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Title: Factors affecting college students' learning styles: Family
characteristics which contribute to...
Subject(s): COLLEGE students -- Family relationships; LEARNING strategies
Source: College Student Journal, Dec96, Vol. 30 Issue 4, p542, 5p, 1 chart
Author(s): Schmeck, Ronald Ray; Nguyen, Thuhien
Abstract: Examines factors affecting college students' learning styles
focusing on the effect of family characteristics on career choice and on
attitudes toward education and learning strategies. Effect of directive
family influence on college students; Effect of families emphasizing
mercenary motives for going to college; Effect of authoritarian families on
students.
AN: 9707062840
ISSN: 0146-3934
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FACTORS AFFECTING COLLEGE STUDENTS' LEARNING STYLES: FAMILY CHARACTERISTICS
WHICH CONTRIBUTE TO COLLEGE STUDENTS ATTITUDES TOWARD EDUCATION AND
PREFERENCES FOR LEARNING STRATEGIES
Although the literature contains information regarding the influence of
family characteristics on career choice, it contains little information
regarding the effect of this variable on attitudes toward education and on
learning styles of college students. The present study focused upon these
dimensions using the Family Characteristics Questionnaire and the Inventory
of Learning Processes. Results suggested that directive family influence
lowered the efficacy and assertion of college students. The reverse was
true of nondirective families. Also, families which emphasize mercenary
motives for going to college lowered academic interest and raised the
agentic (task focused) behavior of college students. Authoritarian families
lowered the students' concern with form and appearance and raised their
tendency to process elaboratively (self actualizing) while studying,
perhaps indicating an attempt to achieve independence through rebellion.
The influence of parents and family on career development of college
students has received considerable attention (e.g. Cochran, 1985; PaLmer
and Cochran, 1988; Rodriguez and Blocker, 1988: Young, Friesen, and
Pearson, 1988). However, little attention has been given to the specific
mechanisms by which family characteristics influence individual differences
in attitudes, motivation, and learning styles of college students. A
notable exception is the work of Zirkel and Cantor (1988).
Zirkel and Cantor (1988) interviewed college students as they began there
education and then repeatedly during their four years in college. They
noted that they could reliably classify them according to their concerns
about being away from their families. All students agreed it was a
challenging task. However, the group that had difficulty separating from
family described the separation in "serious" terms such as deprivation of
guidance and touching (e.g. hugs) and in terms of developing adult
identity, while the other group simply described it in terms of practical
tasks such as having to do their own laundry, their own cooking, and so on.
The interesting point is that, throughout their four years in college,
reliable differences appeared between these two groups with regard to their
attitudes toward the role of being a college student in general and toward
academic performance in particular.
The first group (the one concerned most about separation) tended to perform
as well as the second, but they tended to underestimate their level of
performance. They also showed more stress, more concern about competition,
and more general dissatisfaction with their performance. Also, independence
was a definite issue with them. For example, they were most in need of
family support but least likely to return home to live after college. When
the two groups were asked about the meaning of grades, the first group was
concerned about disappointing their parents and about the stress of
competition. The second group merely mentioned practical aspects such as
completing class assignments on schedule. It appeared that their previous
family environment was affecting the attitudes and learning styles of both
groups in college.
The present study was concerned with relationships between a recently
constructed measure of college students' family background and an
elaborate, widely used measure of their current college learning styles.
The measure of family background was developed by Nugyen (1993). The
instrument assesses six dimensions of family background. The six
characteristics are distinguished by an emphasis on one of the following:
(Family A) effort and work; (Family B) family cohesion; (Family C)
nondirective, practical support (e.g. "I'm here if you need me; I know you
will do free"); (Family D) directive support (e.g. "I want you to major in
X; your career will be Y"); (Family E) mercenary motives for going to
college; (Family F) general obedience to family demands (authoritarian
child rearing).
This retrospective assessment of characteristics of family background was
treated as a potential predictor of the dimensions of the students' current
learning styles (the Inventory of Learning Processes: Revised). An attempt
was made to describe prior family influences that contribute to current
learning style in the college setting.
Method
Subjects.
Eighty three students at a large midwestern university completed the
assessments. Their participation was voluntary, and they were currently
enrolled in various small classes representing varied majors and grade
levels.
Instruments.
The Inventory of Learning Processes-Revised (ILP-R; Geisler-Brenstein &
Schmeck, 1995) is a revised and expanded 150-item version of the original
62-item Inventory of Learning Processes (Schmeck, Ribich, & Ramanaiah,
1977). The major scales measure motivational and attitudinal aspects of
school learning: Academic Self-Efficacy (SE), Academic Motivation(M),
Academic Self-Esteem (ES), and Academic Self-Assertion (SA). They also
assess general preferences for learning strategies: Methodical Study (MS;
concerned with appearance and form), Deep Processing (DP; concern with
ideas and theory), Elaborative Processing (EP; concern with selfactualization and personal experience), and Agentic Processing (AP; concern
with task analysis, task completion, and sequencing of tasks). The ILP-R
takes 20-30 minutes to complete. Responses are recorded on custom computer
answer sheets (using a 6-point Likert scale format). Internal consistency
reliabilities (Cronbach alpha) range from .70 to .93 for the major scales
used in the present study.
The Family Characteristics Questionnaire was originally developed by Nguyen
(1993) while exploring the nature of the family setting in VietnameseAmerican students. This unpublished Masters Thesis involved factor
analyzing 64 items relating to family background. Thirty two items were
retained and subsequently keyed on 6 different subscales: (Family A) effort
and work; (Family B) family cohesion; (Family C) nondirective, practical
support (e.g. "I'm here if you need me; I know you will do fine"); (Family
D) directive support (e.g. "I want you to major in X; your career will be
Y"); (Family E) mercenary motives for going to college; (Family F) general
obedience to family demands (authoritarian child rearing styles). Cronbach
Alpha internal consistency reliabilities range from .69 to .82.
Results
The Pearson product-moment correlational analysis summarized in Table 1 was
the first step in the description of the relationship between students'
family backgrounds and their approaches to learning in college. Table 1
also presents the intercorrelations among the six Family scales themselves.
The intercorrelations among the ILP-R scales were ritually identical to
those reported by Geisler-Brenstein & Schmeck (1995) and can be obtained
from that source. In addition, we examined the data using step-wise
regression analyses. In each case, one of the ILP-R scales served as a
dependent variable and the six Family scales were entered as independent
variables. This was done to provide, for each learning style, information
regarding the independence of the contribution of each of the 6 family
scales in predicting college learning styles. In general, the stepwise
regression analyses duplicated the results in Table 1, but there were two
equations which yielded more than one significant predictor and one
equation that entered only one predictor although the correlation table
suggested that there were two. These regression analyses are presented in
Footnotes 3, 4, and 5 at the bottom of Table 1.
It can be seen in Table 1 that the Family C scale (nondirective support)
contributed positively and the Family D scale (directive support)
contributed negatively to the prediction of self-efficacy as measured by
the ILP-R Academic Self-Efficacy scale. Although the correlation
coefficient for Family D fell short of significance, the stepwise
regression analysis indicated both C and D made significant independent
contributions to the prediction of self-efficacy (cf. Footnote 3). With
regard to ILP-R Academic Motivation (assessing academic interest, effort,
and responsibility), the analyses suggested that such motivation is lower
in students from families which emphasize mercenary motives for going to
college (Family E). With regard to ILP-R Academic Self-Assertion, results
suggest that Family D (directive child rearing) may lower the assertiveness
of the son or daughter in college.
With regard to ILP-R Methodical Study, Family A (effort and work) and
Family F (obedience) were negatively related to this concern with form and
appearance, but the stepwise regression analysis entered only Family A into
the equation predicting Methodical Study (cf. Footnote 4). With regard to
ILP-R Elaborative Processing, the findings indicate that Family
F(obedience) related positively to this learning style. In spite of the
fact that the correlation coefficient in Table 1 fell short of statistical
significance, the stepwise regression analysis also entered Family D
(negatively) into the prediction equation suggesting a significant
independent contribution from this scale (cf. Footnote 5). And finally
regarding the ILP-R Agentic Processing scale, scores on Family E (mercenary
motives) predicted high scores on Agentic Processing.
Discussion
The Family C scale contributed positively and the Family D scale
contributed negatively to the prediction of self-efficacy in college as
measured by the ILP-R Academic Self-Efficacy scale. Family C reflects an
emphasis upon nondirective, practical support for children (e.g. "I'm here
if you need me"). Family D reflects parental directive support for the
student to the point of informing the son or daughter what subject to major
in and what career to plan for. Thus, in general, students reared in
families which emphasize parental direction also have lower self-efficacy
with regard to their potential to succeed in college. There is no way of
knowing whether the family environment "caused" the current state of the
students' efficacy or whether students with low efficacy simply perceive
their families as being directive or even coerce them into being directive,
i.e. the direction of causality is in need of further study.
With regard to ILP-R Academic Motivation (including academic interest,
acceptance of personal responsibility, and effort), the analyses suggest
that academic motivation is lower in students from families that emphasize
mercenary motives for going to college (Family E). This is not surprising
since such students would probably view college as a means to an end rather
than as an end in itself. Such students would tolerate the process, but not
enjoy it.
With regard to ILP-R Academic Self-Assertion, our results suggest that the
family which emphasizes parental direction (Family D; e.g. informing the
son or daughter what to choose as a major) also may lower the assertiveness
of the son or daughter in college. Once again with regard to the direction
of causality, it may be the case that students low on assertiveness look to
their families to provide direction and perceive them as doing so. In other
words, the direction of causality is open to further study.
With regard to ILP-R Methodical Study (measuring impression management or a
concern with "looking like" a good student), the student from a family that
emphasizes effort and work as the road to success (Family A) demonstrates
less concern with appearances (lower Methodical Study). Although Table 1
suggests that the authoritarian family (Family F) is similarly related to
Methodical Study, the stepwise analysis indicated that only Family A (work
and effort) contributed independently to the prediction.
With regard to ILP-R Elaborative Processing (measuring the tendency to
personalize one's studying, and valuing personal experience which relate to
studies), the findings indicate that students who perceive their families
as being authoritarian (Family F) tend to engage in elaborative processing
when studying in college. This may reflect a rebellion or search for
independent identity on the part of these individuals. On the other hand,
the results also suggest that those who report a supportive but directive
approach on the part of their parents (Family D; e.g. telling them what
major to choose) tend to avoid elaborative processing. The latter students
perhaps are not rebelling but rather willingly accepting parental
direction, perhaps even eagerly requesting it. The price the latter
students pay may be a reduction in elaborative processing, one of the more
beneficial learning strategies. It should be noted that this is consistent
with the lower self-efficacy and self-assertion demonstrated by these same
students.
And finally, regarding the ILP-R Agentic Processing scale (measuring an
emphasis on task analysis and task completion in college studies), high
scores on Family E (measuring an emphasis upon mercenary motives for going
to college) predicted high scores on Agentic Processing. This is consistent
with the interpretation of the Family E scale as a measure of the pragmatic
philosophy that college education is a means to an end rather than an end
in itself. It is the way to get a job.
To summarize, the family emphasizing effort and work seems to give rise to
a student who is less concerned with form and appearance. The nondirective
family seems to raise the student's efficacy. The directive family lowers
the student's efficacy. In addition, the latter family contributes to
lowered assertion and elaborative processing (all having to do with selfexpression and self-discovery. The mercenary family contributes to a
lowering of academic motivation but also raises pragmatic work habits (many
teachers would consider these to be "good students" yet it turns out that
they aren't really interested in learning for the sake of learning). The
authoritarian family lowers methodical study and raises elaborative
processing.
The contrast between the effects of directive and authoritarian families
upon elaborative processing may seem perplexing, but it is likely the case
that there are drastic differences between providing friendly direction and
giving orders. It is the difference between "you need me to tell you what
to do; I'm willing to take that responsibility for you," versus "you'll do
it because I say so." When the student willingly relinquishes
responsibility (directive support), self-expression and self-discovery
suffer. When the family simply takes responsibility in authoritarian
fashion, rebellion seems to occur in college as indicated by lowered
methodical study and increased elaborative processing.
Table 1 Intercorrelations among Family Scales and the scales
of the Inventory of Learning Processes (N = 83).
Legend for Chart:
A
B
C
D
E
F
G
H
I
J
K
L
M
-
Inventory of Learning
Inventory of Learning
Inventory of Learning
Inventory of Learning
Inventory of Learning
Inventory of Learning
Inventory of Learning
Inventory of Learning
Family Scales[2]: A
Family Scales[2]: B
Family Scales[2]: C
Family Scales[2]: D
Family Scales[2]: E
Processes:
Processes:
Processes:
Processes:
Processes:
Processes:
Processes:
Processes:
Revised[1]:
Revised[1]:
Revised[1]:
Revised[1]:
Revised[1]:
Revised[1]:
Revised[1]:
Revised[1]:
SE[3]
M
ES
SA
MS[4]
DP
EP[5]
AP
A
F
B
G
C
H
K
D
I
L
E
J
M
Fam. A
.16
.06
.02
.14
.05
.06
--
-.03
---
-.30[*]
---
Fam. B
.19
-.05
.11
.05
-.03
-.11
--
-.07
.22
--
.08
---
Fam. C
.33[*]
.00
.09
.09
.15
-.16
--
.01
.27[*]
--
-.14
.54[*]
--
Fam. D
-.22
-.05
-.19
-.18
-.14
.13
.27[*]
-.28[*]
.08
--
.08
.25[*]
--
Fam. E
-.13
-.02
-.24[*]
-.16
-.13
.25[*]
.13
-.24
.24[*]
.52[*]
-.11
.16
--
Fam. F
.10
.01
-.16
.50[*]
-.11
-.01
-.27[*]
.07
.28[*]
.31[*]
.28[*]
.31[*]
.28[*]
1 Scales: SE = Academic Self Efficacy; M = Academic Motivation: ES =
Academic Self Esteem; SA = Academic Self Assertion; MS = Methodical Study;
DP = Deep Processing; EP = Elaborative Processing; AP = Agentic Processing.
2 Scales: A = effort and work; B = family cohesion; C = nondirective,
practical support; D = directive support; E = mercenary motives for
education; F = general obedience, authoritarian.
3 Stepwise regression (SE):
Variable
Entered
Beta
R[sup 2]
Family C
Step 1
+.33
-Family D
Step 2
-.33
.21
4 Although both Family A and Family F were significantly and negatively
correlated with MS, only Family A was entered in the stepwise analysis (cf.
the intercorrelation among Family scales).
5 Stepwise regression (EP):
Variable
Entered
Beta
R[sup 2]
Family F
Family D
Step 1
Step 2
+.27
-.28
-.14
References
Cochran, L. (1985). Parent career guidance manual. Richmond, British
Columbia, Canada: Buchanan-Kells.
Geisler-Brenstein, E. & Schmeck, R. (1995). The Revised Inventory of
Learning Processes: A multifaceted perspective on individual differences in
learning. In Birenbaum, M. & Dochy, F. (Eds.), Alternatives in assessment
of achievements, learning processes and prior knowledge. Dordrecht: Kluwer
Academic Publishers.
Nguyen, T. (1993). Vietnamese-American students perceptions of family
environments and parental attitudes concerning academic performance and
career choice (Instrument Development). Unpublished Master's thesis,
Southern Illinois University, Carbondale, Ill.
Palmer, S. & Cochran, L. (1988). Parents as agents of career development.
Journal of Counseling Psychology, 35, 71-76.
Rodriguez, M. & Blocker, D. (1988). A comparison of two approaches to
enhancing career maturity in Puerto Rican college women. Journal of
Counseling Psychology, 35, 275-280.
Schmeck, R. R., Ribich, F., & Ramanaiah, N. (1977). Development of a self
report inventory for using individual differences in learning processes.
Applied Psychological Measurement, 1, 413-431.
Young, R. A, Friesen, J. D. & Pearson, H. M. (1988). Activities and
interpersonal relations as dimensions of parental behavior in the career
development of adolescents. Youth and Society, 20, 29-45.
Zirkel, S. & Cantor, N. (1990). Personal construal of life tasks: Those who
struggle for independence. Journal of Personality and Social Psychology,
58, 172-185.
~~~~~~~~
By RONALD RAY SCHMECK AND THUHIEN NGUYEN, Southern Illinois University
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Source: College Student Journal, Dec96, Vol. 30 Issue 4, p542, 5p, 1 chart.
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Title: Learning strategies, styles and approaches: An analysis of their
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Subject(s): LEARNING -- Technique; RESEARCH -- Management
Source: Higher Education, Mar94, Vol. 27 Issue 2, p239, 22p, 7 charts
Author(s): Cano-Garcia, Francisco; Justicia-Justicia, Fernando
Abstract: Examines the interdependence among research tools for measuring
learning from different theoretical bases. Testing of university students;
Existence of three dimensions or paths involved in learning; Motivational
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Title: Differences in learning styles of low socioeconomic status for low
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Source: Education, Fall96, Vol. 117 Issue 1, p141, 7p, 1 chart
Author(s): Caldwell, Ganel P.; Ginther, Dean W.
Abstract: Investigates differences in the learning style of elementary
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DIFFERENCES IN LEARNING STYLES OF LOW SOCIOECONOMIC STATUS FOR LOW AND HIGH
ACHIEVERS
Differences in the learning style of elementary aged low socioeconomic
status, low and high achievers were investigated. Eighty-two subjects,
drawn from a pool of 194 third and fourth grade students in 2 low
socioeconomic elementary schools were administered the Learning Styles
Inventory (LSI) (Dunn, Dunn & Price, 1989). Variables on the LSI
differentiating low from high achievers (p < .05), in math and reading,
were used as predictor variables in a linear discriminate analysis.
Predictor variables correctly classified 78.83% of the students in reading
achievement and 80% in math achievement. All significant variables were
related to motivation. The findings indicated that, for low SES elementary
students, motivational (internal) rather than environmental (external)
factors predicted achievement.
At present, estimates of the percentage of students who are at-risk of
dropping out of school range from 15% in rural communities to 66% in some
urban populations (Cairns, Cairns, & Neckerman, 1989). Since they have
increased economic, legal and psychological problems, at-risk students who
eventually drop out of school create numerous problems for society. This
may be due, in part, to inadequate skills and limited earning potential
(Steinberg, Blind, & Chan, 1984). Students from a low socioeconomic (SES)
background constitute the largest population of individuals considered to
be at-risk of not graduating from high school (Tuma, 1989; Hobbs, 1990).
Through research, educators and cognitive psychologists are investigating
ways to reduce the number of at-risk students.
Students cite numerous reasons for dropping out of school. These reasons
are frequently complex and involve several factors. Research (Dunn &
Griggs, 1988) has grouped these factors into four general categories:
familial factors, personal characteristics, socioeconomic factors, and
educational achievement and school behaviors. Of these factors, educational
achievement and school behaviors are the only factors that can be altered
by educators. Other studies (Texas Education Agency, 1986; Hahn, 1987)
reveal that lack of academic achievement is the single best predictor of
dropping out of school. Thus, if dropout rates are to be significantly
lowered, strategies to improve the academic achievement of at-risk students
must be developed.
Research (Hobbs, 1990) asserts that socioeconomic status (SES) is the
single best predictor of academic achievement; low SES predicts low
achievement. Programs such as Title I, which began in the 1960s, have
attempted to remediate the problems associated with being economically
disadvantaged. Studies (Hubbell, 1983; White, 1985) indicate that
participation in these programs initially increases achievement, but that
these academic gains fade over time. This suggests that the methods
currently being used to remediate the educational deficits associated with
being economically disadvantaged should be revised. The question then
becomes, how should these methods or techniques be changed?
A significant body of research (Dunn & Dunn, 1992; Dunn, Krimsky, Murray, &
Quinn, 1985; Hodges, 1985; Lemmon, 1985; Pizzo, 1981) indicates that the
achievement of all students could be improved by providing initial
instruction in a manner consistent with each student's learning style.
Based on studies (Johnson, 1984; Thraser, 1984; Gadwa & Griggs, 1985) using
the Dunn and Dunn model of learning styles and the Learning Style Inventory
(Dunn, Dunn & Price, 1989), many high school dropouts have learning styles
that are mismatched with the traditional instructional mode.
While low SES is highly correlated with low achievement, some low SES
students are academically successful. These differences in achievement may
be associated with differences in learning styles. Since both low SES and
learning styles incompatible with traditional instruction are highly
associated with school dropouts (Dunn & Griggs, 1988), it would seem that
the low SES, nontraditional learner is in double jeopardy of dropping out.
This raises the question: Does the learning style of the low SES high
achiever differ from the learning style of the low SES low achiever? The
purpose of this study is to determine if there is a difference in the
learning style of low SES high achievers and low SES low achievers, in math
and reading.
Method
Subjects
One hundred nineteen subjects, enrolled in two elementary schools in Texas,
were drawn from a pool of 194 third and fourth grade students in eleven
classes. Subjects were selected based on their participation in the free
lunch program (Federal REgulation 7CFR245, Garner & Cole, 1986). Thus, the
entire sample contained 119 students, of which there were 22 AfricanAmerican males, 24 African-American females, 31 Caucasian males and 42
Caucasian females. Further selection resulted in a final sample of 82
subjects.
Instrument
The Learning Style Inventory (LSI) by Dunn, Dunn and Price (1989) was used
to assess the learning styles of the subjects. The LSI (Dunn, Dunn & Price,
1989) is based on the Dunn and Dunn model of learning style and is the most
reliable instrument for use with elementary grade students. Currently the
Dunn and Dunn model is divided into five broad categories and includes 21
elements that demonstrate how learners are affected by their (a) immediate
environment, (b) own emotionality, (c) sociological preferences; (d)
physiological characteristics; and (e) processing inclinations (1992, p.
3).
Procedures
Using cumulative records, the following data were compiled for each
subject: (a) ethnicity, (b) gender, (c) IQ (Otis-Lennon School Abilities
Test), (d) and the Texas Learning Index (TLI) in reading and math from the
1994 Texas Assessment of Academic Skills (TAAS) Test (eighth edition).
Prior to testing, the classroom teacher read a short story explaining
learning styles to all third and fourth grade students in their respective
classrooms. The story was titled, "Mission From Nostyle: Wonder and Joy
Meet the Space Children" (Braio, 1987). After the story, students were
allowed to ask questions and to participate in a discussion of learning
styles. After the discussion the LSI was administered to all students in
the classroom. Each student received a copy of the LSI. Test items were
read aloud by the teacher. Students were allowed 10 seconds to fill in
their response.
The LSI was machine scored by Price System, Inc. Subjects included in this
study met three criteria: (a) Their Learning Style Profile had a
consistency score of 70 or higher, (b) they had no missing data, and (c)
they participated in the free lunch program. Using the mean Texas Learning
Index (TLI), from the 1994 Texas Assessment of Academic Skills (TAAS) in
reading and math, subjects were classified as either low achievers or high
achievers in reading and low achievers or high achievers in math. Subjects
selected were the 30 with the highest scores and the 30 with the lowest
scores in reading achievement, as well as the 30 with the highest scores
and the 30 with the lowest scores in math achievement. Fifty of the 119
subjects were subsequently selected for both reading and math, and 32
subjects were selected only for either reading or math. A total of 82
subjects were used for the final data analysis. Gender and race were
controlled for in both the low and high groups in math and reading. In math
there were 14 males and 16 females in the low group and 14 males and 16
females in the high group. There were 10 African-Americans and 20
Caucasians in the low math achievement group and 10 African-Americans and
20 Caucasians in the high math achievement group. There were 15 males and
15 females in the low reading achievement group and 15 males and 15 females
in the high reading achievement group. Of these 60 students, there were 11
African-American and 19 Caucasians in the low reading achievement group and
11 African-American and 19 Caucasians in the high reading achievement
group.
Results
Two separate direct discriminat function analysis were performed to predict
membership in one of two groups, high and low achievers in reading and high
and low achievers in math. Predictor variables for the discriminate
analysis were those variables which indicated a significant difference in
independent group means (p < .05). Screening of the data showed no
multivariate outliers. Evaluation of assumptions of linearity, normality,
multicollinearity and homogeneity of variance-covariance matrices revealed
no compromises to multivariate analysis. Selected predictor variables in
reading were motivation, persistence, responsible, kinesthetic, and teacher
motivated. One direct discriminat function was calculated for reading with
a X[sup 2](5) = 22.58, p < .004. The cannonical correlation indicated that
33.6% of the variance in the discriminant function for reading (R = .58)
can be attributed to these five variables. This combination of variables
accurately predicted group membership in 78.83% of the 60 cases in reading
and misclassified 21.17% of the 60 cases (See Table 1).
Selected predictor variables in math were motivation, persistence,
responsible and teacher motivated. One direct discriminat function was
calculated for math with a X[sup 2](4) = 15.81. p </= .003. In math, 24% of
the variance was attributed to the discriminant function (R = .49). Based
on math achievement these variables accurately predicted group membership
in 80.00% of the 60 cases and misclassified 20.00% of the 60 cases (See
Table 2).
The results indicated that a combination of learning styles variables
(motivation, persistence, responsible, kinesthetic and teacher motivated)
discriminated between low achievers and high achievers in reading by
placing 78.83% of the cases in the correct group. Twenty-two of 30 low
achievers were correctly classified (73.3%) and 8 were incorrectly
classified (26.7%). Twenty-five of 30 high achievers were correctly
classified (83.3 %) and 5 were incorrectly classified (16.7%) (See Table
1). This is considerably better than would be expected by chance alone when
both groups are evenly divided.
In math, the predictor variables (motivation, persistence, responsible and
teacher motivated) placed low and high achievers in the correct group in
80.00% of the cases. Twenty-two of 30 low achievers were correctly
classified (73.3%) and 8 were incorrectly classified (26.7%). Twenty-six of
30 high achievers were correctly classified (86.7%) and four were
misclassified (13.3%)(See Table 2). This is considerably better than would
be expected by chance alone with equal groups.
Discussion
These results indicated that high achievers, in both reading and math, are
characterized as being highly motivated, persistent, responsible
(conforming), and teacher motivated. An evaluation of these results
indicated that variables related to motivation are the common construct
among the predictor variables included in the discriminat analysis.
For the purposes of the following discussion, the variables on the LSI are
categorized as either (a) environmental factors or (b) internal factors.
Studies based on other populations (Dunn & Griggs, 1985; Dunn & Dunn, 1992)
found differences in the learning style preferences of low achieving
students and high achieving students on the environmental variables of
lighting, mobility, design, learning with others, and tactile/kinesthetic
preferences vs. auditory/visual preferences. In addition, there were
differences in the learning style preferences of low and high achievers on
internal variables associated with motivation, and persistence. However,
the low SES low and high achievers in this study differed only on variables
associated with internal factors.
This raises two primary questions:
a) What factors might account for differences in levels of motivation,
persistence, and responsibility between low SES low achieving and high
achieving students?
b) How can the school environment be changed to increase the motivation of
low achieving students?
Clearly, motivation is a complex phenomenon. For this reason, multiple
factors may account for the motivational levels of low SES students.
Moreover, the influence of any one factor may differ from individual to
individual and from situation to situation. Brophy (1988) defines
motivation to learn as" . . . a student tendency to find academic
activities meaningful and worthwhile and to try to derive the intended
academic benefits from them" (pp. 205-206).
The motivation to learn is governed by cognitive and affective components
which guide and direct behavior (Ames, 1992). Within this framework, the
motivation to learn can be described in terms of achievement goals.
Achievement goals can be divided into two contrasting constructs: (a)
performance goals or performance-oriented behavior and (b) mastery goals or
mastery-oriented behavior. Dweck and Leggett (1988) characterize students
who exhibit performance oriented behavior in three ways: (a) they view
difficulties as failures and future effort is considered to be pointless,
(b) they exhibit negative self-cognitions and performance when faced with
failures, and (c) they pursue performance goals.
Individuals pursuing performance goals are concerned with receiving
positive judgement of their ability. The thinking processes of children
described as performance-oriented indicate they attribute success to
factors outside of themselves, such as, "the task was easy, the teacher
likes me, or I was lucky". Failure is attributed to lack of ability. In
both cases the child believes he/she has no control over the outcome, thus,
he/she has an extrinsic locus of control. This frequently leads to lowered
motivation (Licht & Dweck, 1984; Weisz, 1981).
In contrast, students high in motivation, called mastery-oriented students,
pursue learning goals directed toward increasing their competence (Dweck,
1975). Mastery-oriented students associate success with effort. They
provide self-praise and encouragement, they accept responsibility for
failure, but do not tend to blame themselves. Failure is attributed to lack
of effort. These individuals are said to have an intrinsic locus of control
(Dweck, 1975).
A key component in attribution theory is the issue of control. Performanceoriented individuals see themselves as having little or no control over the
events in their lives. Whereas, mastery-oriented individuals see themselves
as having a high level of control. Research by Nolen and Haladyna (1990)
indicated that the perception of control appears to be a significant factor
affecting children's task involvement and the quality of their learning.
It could be argued that the low socioeconomic child may have an increased
risk for exhibiting performance-oriented behavior. Research (Garner & Cole,
1986) indicates that students from a low SES background exhibit lowered
expectancy for success and lower intrinsic motivation. In addition, Schultz
(1993) found that "socioeconomic advantage and achievement motivation are
important mediators of academic performance" (p. 229). Based on these data
it can be hypothesized that low socioeconomic background and low motivation
may interact in such a way that each compounds the effects of the other.
Yet, some low SES students are successful. For successful students, high
levels of motivation may counterbalance many of the negative effects of low
SES on achievement. The possible interaction of low socioeconomic status
with an extrinsic locus of control and performance-orientation as opposed
to an intrinsic locus of control and a mastery-orientation, make the
subject of motivation a critical issue for teachers in schools which teach
low SES students. Learning environments must be structured to achieve the
highest level of internal motivation from all students.
Assuming that individual control is a critical component of internal
motivation, classrooms which allow for and encourage personal control will
be effective. Numerous studies (Ames, 1992; Boggiano & Katz, 1991;
Boggiano, Main, & Katz, 1990) suggest that classrooms which are less
competitive and more autonomy-inducing increase the perceived level of
individual control.
In the autonomy-inducing classroom, the teacher is less controlling. Flink,
Boggiano, and Barrett (1990) found that the controlling behaviors of
teachers negatively affected performance. Students who had more controlling
teachers performed lower than students of less controlling teachers. It
appears that competitive classrooms and controlling teachers both
contribute to the students perception of little or no control, since all
control is external. (Boggiano & Katz, 1991).
In the autonomy-inducing classroom, students are active participants in
setting goals for their own learning through the use of contracts, selfmonitoring of progress, cooperative group leaning and task choice (Stipek &
Kowalski, 1989). Recognition of differing learning styles could be
implemented easily into the autonomy inducing classroom, becoming another
tool which could further increase the student's sense of autonomy and
control. This tool would allow students to learn in different ways and to
maintain a high level of control over their immediate learning environment.
In conclusion, low motivation is a critical factor in student achievement,
especially for the low socioeconomic student. Enhancing motivation requires
that students become active participants in their own learning with
teachers assuming a less controlling role. This enhanced motivation would
lead the student to value effort and would increase the individual's
commitment to effort based strategies.
This study showed no significant difference in the environmental learning
style needs of these low SES students. The critical differences between low
and high achievers were internal variables related to motivation.
Instructional methods and strategies which encourage students to become
active participants in their own learning would help to develop autonomy
for the individual student, thereby, increasing motivation and achievement.
Table 1. Classification of Low and High Achievers in Reading Based on the
Predictor Variables of Motivation, Persistent, Responsible, Kinesthetic,
and Teacher Motivated.
Actual Group
n
Low Achievers
Percent Correctly
Classified
30
High Achievers
Percent Correctly
Classified
30
Predicted Group
Low
High
22
73.3%
5
16.7%
8
26.7%
25
83.3%
Total percent of cases correctly classified
78.83%
Table 2. Classification of Low and High Achievers in Math Based on the
Predictor Variables of Motivation, Persistent, Responsible, and Teacher
Motivated.
Actual Group
n
Low Achievers
Percent Correctly
Classified
High Achievers
Percent Correctly
Classified
30
30
Predicted Group
Low
High
22
8
73.3%
4
26.7%
26
13.3%
86.7%
Total percent of cases correctly classified
References
80.00%
Ames, C. (1992). Classrooms: Goals, structures, and student motivation.
Journal of Educational Psychology, 84(3), 261-271.
Boggiano, A.K. & Katz, P.A. (1991). Maladaptive achievement patterns in
students: The role of teachers' controlling strategies. Journal of Social
Issues, 47(4), 35-51.
Boggiano, A.K., Main, D.S., & Katz, P.A. (1990). Children's preference for
challenge. The role of perceived competence and control. Journal of
Personality and Social Psychology, 54, 134-141.
Braio, A.C. (1987). Mission from nostyle: Wonder and joy meet the space
children. Jamaica, MR, St. John's University.
Brophy, J.E. (1988). Synthesis of research on strategies for motivating
students to learn. Educational Leadership, 44, 40-48.
Cairns, R.B., Cairns, B.D. & Neckerman, H.J. (1989). Early school dropout:
Configurations and determinants. Child Development, 60, 1437-1452.
Dunn, R. & Dunn, K. (1992) Teaching elementary students through their
individual learning styles. Needham Heights MA: Allyn and Bacon.
Dunn, R., Dunn, K., & Price, G.E. (1989). Learning style inventory.
Lawrence, KS: Price Systems, Inc.
Dunn, R., & Griggs, S.A. (1988). High school dropouts: Do they learn
differently from those who remain in school. The Principal, 34, 1-8.
Dunn, R., Krimsky, J., Murray, J., & Quinn, P. (1985) Light up their lives:
A review of research on the effects of lighting on children's achievement.
The Reading Teacher, 38(9), 863-869.
Dweck, C.S. (1975). Motivational processes affecting learning. American
Psychologist, 41, 1040-1048.
Dweck, C.S. & Leggert, E.L. (1988). A social-cognitive approach to
motivation and personality. Psychological Review, 95, 256-273.
Fink, C., Boggiano, A.K., & Barrett, M. (1990). Controlling teaching
strategies: Undermining children's self-determination and performance.
Journal of Personality and Social Psychology, 59, 916-924.
Gadwa, K., & Griggs, S. 91985). The school dropout: Implications for
counselors. The School Counselor. 33, 9-17.
Garner, C.W. & Cole, E.G. (1986). The achievement of students in low-ses
settings: An investigation of the relationship between locus and control
and field dependence. Urban Education, 21(2), 189-206.
Hahn, A. (1987). Reaching out to America's dropouts: What to do? Phi Delta
Kappan, 73(4), 290-94.
Hobbs, D. (1990). School based community development: Making connections
for improved learning. In S. Raferty & D. Mulkey (Ed). The Role of Rural
Schools in Community Development (pp. 57-64). Mississippi State, MS.
Southern Rural Development Center.
Hodges, H. (1985). An analysis of the relationships among preferences for a
formal/informal design, one element of learning style, academic
achievement, and attitudes of seventh and eighth grade students in remedial
mathematics classes in a New York City junior high school. (Doctoral
dissertation, St. John's Univ.) Dissertation Abstracts International, 45,
2791A.
Hubbell, R. 91983). A Review of Head Start Since 1970. Washington, DC: U.S.
Department of Health and Human Services.
Johnson, C.D. (1984). Identifying potential school dropouts. (Doctoral
dissertation, United States International University.) Dissertation
Abstracts International, 45, 2397A.
Lemmon, P. (1985). A school where learning styles makes a difference.
Principal, 64, 7.
Licht, B.G. & Dweck, C.S. (1984). Determinants of academic achievement: The
interaction of children's achievement orientations with skill areas.
Developmental Psychology, 20, 628-636.
Nolen, S.B. & Haladyna, T.M. (1990). Motivation and studying in high school
science. Journal of Research on Science Teaching.
Pizzo, J. (1981). An investigation of the relationships between selected
acoustic environments and sound, an element of learning style, as they
affect sixth grade students' reading achievement and attitudes. (Doctoral
dissertation, St. John's University). Dissertation Abstracts International,
42, 2475A.
Schultz, G.F. (1993). Socioeconomic advantage and achievement motivation:
Important mediators of academic performance in minority children in urban
schools. The Urban Review, 25(3), 221-232.
Steinberg, L., Blind, P.L. & Chun, K.S. (1984). Dropping out among language
minority youth. Review of Educational Research, 54, 113-132.
Stipek, D.J. & Kowalski, P.S. 91989). Learned helplessness in taskorienting versus performance-orienting testing conditions. Journal of
Educational Psychology, 81, 384-391.
Texas Education Agency. (1986). Characteristics of at-risk youth.
Practitioner's Guide, Series Number One. 26.
Thrasher, R., (1984). A study of the learning style preferences of at-risk
sixth and ninth graders. Pompano Beach, FL: Florida Association of
Alternative School Education.
Tuma, J.M. (1989). Mental health services for children: The state of the
art. American Psychologist, 44, 188-199.
Weisz, J.R. (1981). Perceived control and learned helplessness among
mentally retarded and nonretarded children: A developmental analysis.
Developmental Psychology, 15, 311-319.
White, K.R. (1985). Efficacy of early intervention. Journal of Special
Education, 19, 401-416.
Authors Note
Ganel P. Caldwell and Dean W. Ginther, Department of Psychology and Special
Education, East Texas State University, Commerce Texas.
The authors wish to thank the administrators, teachers, and students of the
Denison Independent School District who participated in this study. We also
thank Bernadette Gudzella and Harry Fullwood for serving as members of the
thesis committee.
Correspondence concerning this article should be addressed to Dean W.
Ginther, East Texas State University, Department of Psychology and Special
Education, East Texas State University, Commerce, TX 75489.
~~~~~~~~
By GANEL P. CALDWELL AND DEAN W. GINTHER, East Texas State University,
Commerce, Texas 75428
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Title: Learning style characteristics: An introductory workshop.
Subject(s): INSTRUCTIONAL systems -- Design -- Congresses; LEARNING
strategies
Source: Clearing House, Nov/Dec92, Vol. 66 Issue 2, p122, 5p, 2 diagrams
Author(s): Reynolds, Jim; Gerstein, Martin
Abstract: Presents the outline and rationale for a three-hour workshop on
learning styles designed for use with teachers, counselors and
administrators. Learning style characteristics; Learning style workshop;
Conclusions.
AN: 9705041279
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Section: SPEAKING OUT
LEARNING STYLE CHARACTERISTICS: AN INTRODUCTORY WORKSHOP
The topic of learning style characteristics is of widespread interest in
the educational community, especially on the middle and secondary school
level (Dunn and Griggs 1989; Orsak 1990; Sinatra 1990). Teachers and
administrators have found that when they are aware of their own learning
styles and the styles of their students they can improve the quality of
instruction in their schools.
This article presents the outline and rationale for a three-hour workshop
on learning styles designed for use with teachers, counselors, and
administrators. The workshop has been conducted with secondary school and
college students; secondary school counselors, teachers, and
administrators; and college counselors, faculty, and administrators.
Learning Style Characteristics
Learning style characteristics are preferences that people have for the way
they learn. Individual differences in the way a learner approaches the task
of learning is called his or her learning style. A learner's preference for
the visual (seeing) sensory mode over the auditory (hearing) sensory mode
is an example of a preferred learning style characteristic. The preference
for visual stimulation suggests that the learner may need to study and
learn by techniques that provide visual representation of the material
being learned, such as graphs, charts, and drawings. Experts, however, do
not seem to agree on how to define learning style or on the number and type
of characteristics that make up one's learning style. This confusion over
learning style terminology and concepts has been addressed by Bonham
(1988), Curry (1990), and Reynolds (1991).
Although writers define learning styles somewhat differently, the
importance of the concept of individual learning style characteristics is
recognized by many writers and researchers (Claxton and Murrell 1987;
Cornett 1983; DeBello 1990; Hill 1976; Keefe 1987; Kolb 1984; McCarthy
1980; Price 1987; Smith 1982; Witkin and Goodenough 1981). It is helpful to
look at the different ways that writers distinguish characteristics related
to learning styles. Cornett (1983) and Keefe (1987) identified three major
categories of learning style characteristics as cognitive, affective, and
physiological. Price, Dunn, and Dunn (1982) identified four major
categories with four to six elements in each for a total of twenty
characteristics that affect the learning process. DeBello (1990) suggested
that there may be "as many definitions of learning styles as there are
theorists" (203). But most experts would probably agree that the concept of
learning style should be viewed as multidimensional.
Figure 1 presents a conceptual model for categorizing learning style
characteristics as multidimensional. This conceptual model of one's unique
pattern of learning style characteristics includes, but may not be limited
to, the six categories of perceptual preference, physical environment,
social environment, cognitive style, time of day, and motivation/values.
This categorization is a modification of the one used by Price, Dunn, and
Dunn (1982). They placed perceptual preference and time of day into one
physical-needs category; also, the term psychological is used by Dunn
(1984) to categorize cognitive styles. Similar categorization of learning
style characteristics can be found in the work of Hill (1976) and his model
of cognitive mapping.
Learning Style Workshop
Design and Rationale
Our workshop is designed to introduce participants to the concept of
learning style characteristics. The objectives of the workshop are to
encourage the participants to
describe in general terms the idea or concept of learning style
characteristics;
identify some of their own learning style characteristics; and
think about how they might use their own learning style characteristics to
make a difference in their learning.
Kolb's (1984) model of experiential learning was selected as the model
around which to design the workshop. Kolb's four learning styles are
examples of learning style characteristics associated with the category of
cognitive styles as seen in figure 1. The four elements of the workshop
were developed to use instructional techniques associated with Kolb's four
learning styles.
Kolb's Learning Style Model
Kolb's (1984) model of learning styles is based on the idea that learners
use the following four learning modes: (a) concrete experience (feeling);
(b) reflective observation (watching); (c) abstract conceptualization
(thinking); and (d) active experimentation (doing). He sees the four
learning modes as making up two dimensions that are "polar opposites" (Kolb
1984). One dimension is represented at one end by concrete experience
(feeling) and at the other end by abstract conceptualization (thinking).
The other dimension is made up of active experimentation (doing) on one end
and reflective observation (watching) at the other end. The two dimensions
of concrete/ abstract involvement and active/reflective participation can
be combined to produce the four quadrant learning style model that is
presented in figure 2.
The four quadrants represent the four learning styles: (a) diverger, (b)
assimilator, (c) converger, and (d) accommodator. Kolb (1985) developed a
Learning Style Inventory (LSI) that can be used to assess the four learning
styles of individuals.
Workshop Elements
The first element of the workshop is designed to support the
instructional/learning strategies associated with Kolb's diverger learning
style. This learning style is best characterized as one in which the
learner is concerned with divergent ideas and is usually considered an
imaginative learner (Kolb 1985). Brainstorming and discussion groups are
effective instructional/learning strategies for this style (Svinicki and
Dixon 1987). This element consists of a small group exercise in which the
participants are asked to identify some positive learning experiences from
their past. Each group makes a list of positive learning environment
characteristics and the lists are posted on the was. The workshop leader
then leads a discussion about the similarities and differences of the
lists. Each list usually contains such items as group study sessions,
hands-on experience, quiet place to study, discussion in groups or
classrooms, and use of audiovisual aids. This workshop element, as well as
the others, takes between thirty and forty-five minutes so the whole
workshop can be completed in two to three hours. In order to make the small
groups workable, it is suggested that the total number of workshop
participants be no more than twenty-five.
The second element of the workshop deals with modes of reflective
observation and abstract conceptualization, which Kolb (1984) called the
assimilator learning style. This learning style is best described as one in
which the learner responds to abstract ideas and/or concepts. This type of
person is viewed as a rational or logical learner, and lecture is seen as
an appropriate instructional activity for this learning style. This element
of the workshop consists of a brief lecture on learning style
characteristics with the leader using an overhead transparency of the
categories of learning style characteristics (see figure 1) to show how
characteristics can be grouped into six major categories. The workshop
leader reviews each of the six categories and tells how the characteristics
in each of the categories are defined. The leader points out that one might
find other categories and/or other characteristics in these six categories.
For example, some researchers might include kinesthetic as a characteristic
under perceptual preference (James and Galbraith 1985; Price 1987). Others
might define left/right brain dominance as a unique category, but figure I
would categorize brain hemispheric dominance as just another type of
cognitive style.
The third element of the workshop deals with abstract conceptualization and
active experimentation, which Kolb (1984) called the converger learning
style. This learning style is best characterized as the theory-intopractice stage in which the learner starts to relate theory to practical
application. This element of the workshop allows participants to start to
identify their own learning style characteristics through the use of two
inventories. The first is a thirty-item self-assessment perceptual
preference inventory. Participants are asked to identify items that help
them learn. The self-assessment perceptual preference inventory has ten
items that are visual activities such as "reading assignments/books," ten
auditory items such as "hearing recitations by others," and ten
tactile/kinesthetic items such as "drawing pictures." The idea for this
inventory was adapted from the work of James and Galbraith (1985). The
participants score their inventories and end up with three scores--one
score for each of the three areas of visual, auditory, and tactile/
kinesthetic.
The workshop leader should use his or her own scores to explain the selfassessment perceptual preference inventory. The numbers that reflect the
senior author's results would be a high number on the visual items (7 to
8), a low number on the auditory items (3 to 4), and a midrange number for
tactile/kinesthetic (around 5). These results identify a strong preference
for visual learning, weak preference for auditory, and an average response
for tactile/kinesthetic learning. The senior author has learned, from other
learning style inventories and from his own learning experience, that
having a preference for visual learning means, for example, looking at a
class roster to learn student's names and drawing diagrams to learn
concepts. Other workshop participants are asked to share their scores and
to self-validate those scores using their own learning experiences.
The second instrument used is Kolb's (1985) Learning Style Inventory. The
LSI consists of twelve sentences with four different endings for each
sentence. Respondents are asked to rank the endings to each sentence as the
endings match with their own self-description of how they learn. The LSI is
self-scoring and produces four raw scores that can then be plotted to
identify one of Kolb's four learning styles as presented in figure 2.
Kolb's (1985) LSI includes a section that can be used to interpret the
results for the workshop participants. Murrell's (1987) Learning-Model
Instrument has also been used for this part of the workshop. This
instrument takes less time to score but does not have the LSI research data
base. This instrument was published by University Associates so it can be
used in an educational setting without charge. The Murrell instrument is
based in large part on Kolb's model.
The use of the two learning style characteristics inventories allows the
workshop participants to start to identify some of their own learning style
characteristics. The participants are asked to validate the results of each
inventory against their own learning experiences and behavior in order to
change the placements if they do not seem to fit. Most participants seem
able to self-validate the results of the inventories used in the workshop.
The fourth element of the workshop deals with what Kolb (1984) called an
accommodator learning style. This learning style is characterized by an
emphasis on active experimentation (doing) and concrete experience
(feeling). Laboratory and field work are examples of the instructional
activities associated with this learning style (Svinicki and Dixon 1987).
This last part of the workshop has the participants return to their small
groups. This time the group is asked to develop a list of learning
strategies that might help each group member to become a more productive
learner. Each group is asked to share their list of learning strategies
with the rest of the workshop participants.
At this point in the workshop, the leader discusses the idea that Kolb's
styles might be used as a model for instruction and learning. Each of the
four workshop elements are reviewed and their relationship with the Kolb
(1984) model are discussed. The four learning styles of Kolb can be viewed
as a learning cycle. In the first stage, the learner becomes aware of the
need to learn and to seek meaning by asking the question "why." In the
second stage, the learner asks "what do I need to know" and seeks content
information. In the third stage, the learner deals with the theory-intopractice process of using the new knowledge, while the fourth stage allows
the learner to apply this new knowledge to real life situations. McCarthy
(1980) suggested that all students be taught using instructional/learning
techniques that apply to each of the four Kolb (1985) learning styles.
Conclusion
The commitment and practices needed to help produce more effective learners
can start at a very early age. As students move up through the middle and
secondary levels, more emphasis and time can be placed on developing
learning skills/strategies based on individual learning style
characteristics. The learning process is lifelong, and when individuals
receive insight into their unique patterns of learning style
characteristics, they are empowered as learners. A major objective of our
educational system should be to empower learners to direct and take charge
of their own learning.
DIAGRAM: FIGURE 1; Categories of Learning Style Characteristics
DIAGRAM: FIGURE 2; Kolb's Learning Styles
REFERENCES
Bonham, L. A. 1988. Learning style use: In need of perspective. Lifelong
Learning 11(5):14-17.
Claxton, C. S., and P. H. Murrell. 1987. Learning styles: Implications for
improving educational practices. ASHE-ERIC Higher Education Report No. 4.
Washington, D.C.: Association for the Study of Higher Education.
Cornett, C. E. 1983. What you should know about teaching and learning
styles. Bloomington, Ind.: Phi Delta Kappa Educational Foundation.
Curry, L. 1990. A critique of the research on learning styles. Educational
Leadership 48(2):50-56.
DeBello, T. C. 1990. Comparison of eleven major learning styles models:
Variables, appropriate populations, validity of instrumentation, and the
research behind them. Journal of Reading, Writing, and Learning
Disabilities: International 6(3):203-22.
Dunn, R. 1984. Learning style: State of the science. Theory Into Practice
23(1):10-19.
Dunn, R., and S. A. Griggs. 1989. Learning Styles: Quiet revolution in
American secondary schools. Clearing House 65(1):40-42.
Hill, J. E. 1976. The educational science. Bloomfield Hills, Mich.: Oakland
Community College.
James, W. B., and M. W. Galbraith. 1985. Perceptual learning styles:
Implications and techniques for the practitioner. Lifelong Learning
8(4):20-23.
Keefe, J. W. 1987. Learning style theory and practice. Reston, Va.:
National Association of Secondary School Principals.
Kolb, D. A. 1984. Experiential learning: Experience as the source of
learning and development. Englewood Cliffs, N.J.: Prentice-Hall.
Kolb, D. A. 1985. Learning-style inventory: Self-scoring inventory and
interpretation booklet. 2nd ed. Boston: McBer.
McCarthy, B. 1980. The 4MAT system: Teaching to learning styles with
right/left mode techniques. Barrington, Ill.: EXCEL.
Murrell, K. L. 1987. The learning-model instrument: An instrument based on
the learning model for managers. In The 1987 annual: Developing human
resources, edited by J. W. Pfeiffer, 109-119. San Diego, Calif.: University
Associates.
Orsak, L. 1990. Learning styles versus the Rip Van Winkle syndrome.
Educational Leadership 48(2):19-21.
Price, G. E. 1987. Changes in learning style for a random sample of
individuals ages 18 and older who responded to the productivity
environmental preference survey. Paper presented at the 1987 Annual
Convention of the AACD, New Orleans. ERIC Document Reproduction Service No.
ED 283 112.
Price, G. E., R. Dunn, and K. Dunn. 1982. Productivity environmental
preference survey: PEPS manual. Lawrence, Kan.: Price Systems.
Reynolds, J. 1991. Learning and cognitive styles: Confusion over
definitions and terminology. Virginia Counselors Journal 19(1):22-26,
Sinatra, C. 1990. Five diverse secondary schools where learning style
instruction works. Journal of Reading, Writing, and Learning Disabilities:
International 6(3):323-34.
Smith R. M. 1982. Learning how to learn: Applied theory for adults.
Chicago: Follett.
Svinicki, M. D., and N. M. Dixon. 1987. The Kolb model modified for
classroom activities. College Teaching 35:141-46.
Witkin, H. A., and D. R. Goodenough. 1981. Cognitive styles: Essence and
origins. New York: International Universities Press.
~~~~~~~~
By JIM REYNOLDS and MARTIN GERSTEIN
Jim Reynolds is an associate professor and counselor at Northern Virginia
Community College, Alexandria, Virginia. Martin Gerstein is an associate
professor of education at Virginia Polytechnic Institute and State
University, Blacksburg, Virginia.
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Title: Psychometric properties of the revised Grasha Riechmann Student
Learning Style Scales.
Subject(s): LEARNING strategies
Source: Educational & Psychological Measurement, Feb96, Vol. 56 Issue 1,
p166, 7p, 2 charts
Author(s): Ferrari, Joseph R.; Wesley, Joseph C.
Abstract: Examines the psychometric properties of the revised GrashaRiechmann's Student Learning Style Scales. Factor analyses of items and
scales; Analyses of the student subjects learning styles.
AN: 9603202453
ISSN: 0013-1644
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PSYCHOMETRIC PROPERTIES OF THE REVISED GRASHA-RIECHMANN STUDENT LEARNING
STYLE SCALES
The 60-item version of Grasha and Riechmann's Student Learning Style Scales
(six scales, 10 items per scale) was administered to a large sample of
college freshmen on each of three campuses (total N = 870) in the
northeast. The Participative, Avoidant, and Collaborative scales showed
acceptable internal consistency, but the Dependent, Independent, and
Competitive scales did not. Factor analyses of items and scales produced no
solution approximating simple structure in any sample. Neither items nor
scales yielded a factor pattern resembling the theoretical structure
postulated by Grasha and Riechmann in any sample, although scale scores in
two samples yielded a Participative-Avoidant factor that is one of the
theoretical dimensions. Properties of the 60-item version are thus very
similar to those reported for an earlier 90-item version.
In the 1960s and 1970s, aptitude-treatment interaction captured the
imagination of many educational psychologists. With aptitude variously
defined as abilities, cognitive/developmental readinesses, conceptual
levels, personality traits, and learning styles, aptitude-treatment
experimentation flourished (for interesting summaries of this body of work,
see Keefe, 1987, and Schmeck, 1983). Compared to other constructs, learning
styles fared poorly in these studies (Curry, 1990; Goldstein & Bokoros,
1992; Moran, 1991; Westman, 1993). As advocates of learning styles
attempted to bolster their position (e.g., Dunn, 1987; Keefe & Ferrell,
1990), critics such as Moran (1991) called for more adequate theory and
measurement: "There is a need for rigorous conceptual and empirical
analysis (including psychometric validation) of the construct of learning
style to avoid the danger that over-extension of this term will weaken its
theoretical foundations" (p. 239).
This report focuses on the current version of a measure first developed
around 1970, namely Grasha and Riechmann's (Riechmann & Grasha, 1974)
rationally constructed inventory of learning styles. Proceeding on the
assumption that learning styles were describable in terms of three bipolar
dimensions--dependent rs. independent, participative vs. avoidant, and
collaborative rs. competitive--Grasha and Riechmann devised items to assess
each pole of each dimension, six scales in all. At first they used eight
items per scale, but soon developed a 90-item inventory (with 15 items per
scale and a 5-point Likert response format) (Riechmann & Grasha, 1974; see
also Hruska & Grasha, 1982).
Ferrell (1983) administered the 90-item inventory along with other
objective self-report measures of learning style to a sample of high school
seniors (N = 471) and a sample of community college students (N = 260). Her
data showed clearly that the instruments had one shortcoming in common:
None of them was able to produce a factor structure corresponding to the
dimensions it was supposed to measure. Grasha and Riechmann then
abbreviated their inventory to the 60-item form assessed here (10 items per
scale; the form can be obtained from A. F. Grasha, Dept. of Psychology,
University of Cincinnati, Cincinnati, OH 45221). Representative items are,
for Independent, "I like to develop my own ideas about course content"; for
Avoidant, "I often daydream during class"; for Collaborative, "Working with
other students on class projects is something I enjoy"; for Dependent,
"Teachers should state exactly what they expect from students"; for
Competitive, "To stand out in my classes, I try to do assignments better
than other students"; and for Participative, "Classroom activities
generally are worthwhile." The purpose of the present study was to examine
the factor structure and other psychometric features of the 60-item
version.
Method
Three campuses provided subjects; designated A, B, and C, they differed
appreciably in size and selectivity (Kelly & Quinlan, 1993). Sampling and
recruiting of subjects, and the inducements offered for their
participation, were dissimilar on the three campuses. Time and
circumstances of data collection differed as well. Such dissimilarities
render between-campus comparisons equivocal, but are advantageous for
psychometric analysis. For example, if converging patterns are found in
disparate samples from the target population, inferences are strengthened
accordingly.
Subjects
From each campus, one sample of students was obtained whose college career
began in September 1992. The Campus A sample consisted of 375 students (14%
of entering freshmen), the Campus B sample had 171 students (15%), and the
Campus C sample had 324 students (57%). Women outnumbered men by at least
2:1 in the student body at all three campuses (Kelly & Quinlan, 1993) and
in each of these samples outnumbered men by approximately 3:1. In work with
the 90-item version of their instrument, Hruska and Grasha (1982) reported
"little or no sex differences in styles" (p. 83). Responses from the women
and men were therefore combined in the analysis of results.
Procedure
Sampling and recruiting. At Campus A, freshmen enrolled in introductory
psychology were invited to fill out a questionnaire containing the Grasha-
Riechmann items as an alternative way to earn optional extra credit in the
course. At Campus B, 555 freshmen who had expressed an interest in research
participation received a cover letter, a statement of consent, which
included a waiver granting the investigators access to the registrar's
records, and the questionnaire. The letter promised confidentiality and a
written report of results, and asked the prospective subject to complete
the questionnaire and return it via campus mail within 3 weeks. At Campus
C, freshmen participating in an orientation program were asked to fill out
the questionnaire.
Data collection. All subjects signed a consent form approved by the
respective campus human subjects committee, and all completed the same
questionnaire, which also contained Berzonsky's (1989) measure of identity
styles and Solomon and Rothblum's (1984) measures of academic
procrastination and reasons for procrastinating. At Campus C, subjects'
grade point averages were provided by college officials at the end of the
academic year.
Statistical analysis. The learning styles data were analyzed using SPSS
programs: internal consistency data were obtained via the RELIABILITY
package, factor patterns via the FACTOR package (SPSS, 1990, chapters 26
and 21, respectively).
Results
Reliability data are summarized in Table 1. The Avoidant, Collaborative,
and Participative scales showed marginally acceptable estimates of
reliability (seven of the nine values of Cronbach's alpha are above .70,
and negative interitem rs are rare), whereas the other scales yielded
inadequate estimates (all nine values of coefficient alpha are .70 or less,
and together the three scales yield 62 negative interitem rs). When Briggs
and Cheek's (1986) rule is applied so that items measuring the same
construct should intercorrelate >.20, the Avoidant, Collaborative, and
Participative scales taken together meet this criterion about twice as
often (59% of rs) as the other three scales taken together (28% of rs).
Factor analysis of items was attempted via direct oblimin rotation (which
allows for correlated factors) for data from each campus. No approximation
to simple structure emerged in data from any campus; in each case, the
program (SPSS, 1990, pp. 335-336) terminated after 23 or more iterations.
Factor analysis of scale scores with varimax rotation (SPSS, 1990, pp.
331334) was attempted twice, once seeking a six-factor solution and once
seeking simple structure. The six-factor solution was uninterpretable
because of multiple salient loadings exhibited by one or more scales in
each sample. Results of the simple structure analyses were not much better;
they are shown in Table 2. Simple structure was not produced in any sample.
A bipolar Participative vs. Avoidant factor did emerge in the data from
Campuses A and B, but not in data from Campus C. The Grasha-Riechmann
rationale specifies two other bipolar factors as well (i.e., Dependent rs.
Independent and Collaborative vs. Competitive) (Riechmann & Grasha, 1974),
but these factors did not appear.
In data from Campus C, scale scores were correlated with grade point
average earned during the freshman year. Pearson rs were significant only
for Participative (r = .23, p < .01) and Avoidant (r = -.27, p < .01)
scales.
Discussion
Generic caveats regarding learning styles have been issued by Curry (1990),
Moran (1991), and others; the results obtained in the current investigation
bear them out, at least as far as the Grasha-Riechmann scales (Riechmann &
Grasha, 1974) are concerned. Three of the six scales appear to be defective
because of low reliability estimates. Consequently, the 60-item form
examined here exhibits the same serious shortcoming Ferrell (1983) found in
the 90-item form--that is, the scales do not yield a clear, stable factor
structure congruent with the theoretical structure (Hruska & Grasha, 1982;
Riechmann & Grasha, 1974).
Another similarity between Ferrell's (1983) findings and the present
results is the bipolar Participative vs. Avoidant factor in data from both
of her samples, and from Campuses A and B here. This factor shows some
stability (it has appeared in data from at least four samples) and, at the
campus where scholastic records were available, both of its poles correlate
significantly and in the expected direction with grade point average for
the freshman year.
Nevertheless, some reservations concerning the Participative vs. Avoidant
factor are in order. First, it ought to appear in the data from Campus C,
but does not. Instead, as Table 2 shows, the Participative, Collaborative,
and Dependent scales all load at the same pole of Factor 1--an outcome
contrary to Grasha and Riechmann's (1974) theory. Second, although both
Participative and Avoidant styles correlate significantly with grade point
average (Hruska & Grasha, 1982, found the same pattern using the 90-item
version), measures of other nonintellective constructs yield slightly
larger than those observed here. Wolfe and Johnson (in press), for example,
obtained rs of .34 for conscientiousness and .38 for self-control, and
Schuerger and Kuna (1987) reported that conscientiousness measured during
high school correlates .28 with cumulative grade point average subsequently
earned in college.
Furnham (1992) presented evidence that learning styles are entirely
reducible to personality traits and recommended that educational
psychologists abandon learning styles in favor of trait theories. His point
has received support both from data arrays (including the present one),
showing that certain measures of learning style lack psychometric adequacy,
and from studies demonstrating that traits do a better job of predicting
performance.
Although we agree with Furnham, it is possible that the Grasha-Riechmann
scales (Riechmann & Grasha, 1974) can be "salvaged" by improving their
technical adequacy. Revision of the Dependent, Independent, and Competitive
scales to make them internally consistent would have to be the first step.
The constraints that these scales must not overlap with the Participative,
Avoidant, and Collaborative scales, and must not overlap with each other,
make this a difficult step. The next, and larger, challenge is to
demonstrate that the six scales yield the three bipolar factors specified
by Grasha and Riechmann's theory. Finally, if congruence is achieved, it
has to be shown that the factors are stable and capable of withstanding
multitrait-multimethod assessment. Until such thoroughgoing refinement is
carried out, conventional measures of ability and personality will continue
to have more utility than learning style inventories.
This report was presented at the 1994 Eastern Psychological Association
convention in Providence, RI. Correspondence about this article should be
directed to Joseph R. Ferrari, Department of Psychology, DePaul University,
2219 North Kenmore Avenue, Chicago, IL 60614-3504. The data described here
can be made available via Internet. Contact Raymond Wolfe, Department of
Psychology, SUNY College, Geneseo, NY 14454-1401 (e-mail: Wolfe @UNO.CC.
geneseo.edu).
Table 1
Internal Consistency Estimates for Learning Style Scales
in Data From Three Samples: Coefficient Alpha and Summary of
Interitem rs
Learning styles
Independent
Coefficient alpha
Number of negative
interitem rs
Range of interitem rs
Number of interitem
rs > .2
Avoidant
Coefficient alpha
Number of negative
interitem rs
Range of interitem rs
Number of interitem
rs >.2
Collaborative
Coefficient alpha
Number of negative
interitem rs
Range of interitem rs
Number of interitem
rs > .2
Dependent
Coefficient alpha
Number of negative
interitem rs
Range of interitem rs
Number of interitem
rs > .2
Competitive
Coefficient alpha
Number of negative
interitem rs
Range of interitem rs
Number of interitem
rs > .2
Participative
Coefficient alpha
Number of negative
interitem rs
Range of interitem rs
Number of interitem
rs > .2
Table 2
Campus A
(n = 375)
.51
7
-.07 to .29
Campus B
(n = 171)
Campus C
(n = 324)
.57
7
-.23 to .40
.55
8
-.07 to .31
4
10
11
.73
.77
.68
0
.01 to .46
1
-.13 to .58
0
.04 to .38
24
31
20
.75
.73
.77
0
.04 to .44
1
-.01 to .67
0
.09 to .48
33
25
31
.55
.44
.60
10
-.14 to .36
7
-.11 to.35
5
-.14 to.40
12
1
13
.70
.70
.70
4
-.07 to .42
3
-.09 to .50
4
-.04 to .47
21
19
21
.73
.69
.76
0
.06 to .45
1
-.00 to .41
0
.13 to .44
25
19
31
Factor Analysis of Learning Style Scale Scores in Three
Samples: Varimax Rotation
Learning styles
Independent
Avoidant
Collaborative
Dependent
Competitive
Participative
Campus A
(n = 375)
Factors
1
2
3
-.10
-.03
.71
.84
.34
.54
-.18
.91
-.24
.05
.35
-.71
.88
.07
.03
.03
.64
.20
Campus B
(n = 171)
Factors
1
2
.23
-.86
.48
.06
.01
.87
-.10
.03
-.28
.78
.75
.22
3
.85
-.10
-.44
-.26
.18
.16
Campus C
(n = 324)
Factors
1
2
.36
-.42
.80
.73
.16
.88
.57
.63
.10
.23
.75
-.05
References
Berzonsky, M.D. (1989). Identity style: Conceptualization and measurement.
Journal of Adolescent Research, 4, 268-282.
Briggs, S. R., & Cheek, J. M. (1986). The role of factor analysis in the
development and evaluation of personality scales. Journal of Personality,
54, 106-148.
Curry, L. (1990). A critique of the research on learning styles.
Educational Leadership, 48(2), 50-55.
Dunn, R. (1987). Research on instructional environments: Implications for
student achievement and attitudes. Professional School Psychology, 21, 4352.
Ferrell, B. G. (1983). A factor analytic comparison of four learning-styles
instruments. Journal of Educational Psychology, 75, 33-39.
Furnham, A. (1992). Personality and learning style: A study of three
instruments. Personality and Individual Differences, 13, 429-438.
Goldstein, M. B., & Bokoros, M. A. (1992). Tilting at windmills: Comparing
the Learning Style Inventory and the Learning Style Questionnaire.
Educational and Psychological Measurement, 52, 701-708.
Hroska, S. R., & Grasha, A. F. (1982). The Grasha-Riechmann Student
Learning Style Scales. In J. Keefe (Ed.), Student learning styles and brain
behavior (pp. 81-86). Reston, VA: National Association of Secondary School
Principals.
Keefe, J. W. (1987). Learning style: Theory and practice. Reston, VA:
National Association of Secondary School Principals.
Keefe, L W., & Ferrell, B. G. (1990). Developing a defensible learning
style paradigm. Educational Leadership, 48(2), 57-6L
Kelly, M, & Quinlan, L. (1993). College admissions data handbook, 19931994: Northeast region. Concord, MA: Orchard House.
Moran, A. (1991). What can learning styles research learn from cognitive
psychology? Educational Psychology, 11,239-245.
Riechmann, S. W., & Grasha, A. F. (1974). A rational approach to the
construct validity of a student learning style scales instrument. Journal
of Psychology, 87, 213-223.
Schuerger, J. M, & Kuna, D. L. (1987). Adolescent personality and school
and college performance: A follow-up study. Psychology in the Schools, 24,
281-285.
Schmeck, R. R. (1983). Learning styles of college students. In R. F. Dillon
& R. R. Schmeck (Eds.), Individual differences in cognition (Vol. 1, pp.
233-279). New York: Academic Press.
Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination:
Frequency and cognitive-behavioral correlates. Journal of Counseling
Psychology, 31,503-509.
SPSS, Inc. (1990). SPSS base system user's guide. Chicago: Author.
Westman, A. S. (1993). Learning styles are content specific and probably
influenced by content areas studied. Psychological Reports, 73, 512-514.
Wolfe, R. N, & Johnson, S. D. (in press). Personality as a predictor of
college performance. Educational and Psychological Measurement.
~~~~~~~~
By JOSEPH R. FERRARI, DePaul University , SUZANNE M, BAMONTO, University of
Oregon and BRETI L. BECK, Bloomsburg University
By JOSEPH C. WESLEY, RAYMOND N. WOLFE, AND CARRIE N. ERWIN, State
University of New York, College at Geneseo
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Title: Personality types, learning styles and educational goals.
Subject(s): LEARNING -- Technique; LEARNING strategies
Source: Educational Psychology, 1991, Vol. 11 Issue 3/4, p217, 22p, 4
charts, 5 diagrams
Author(s): Miller, Alan
Abstract: Outlines a personality typology, provides a coherent system
within which to construe and conduct research upon learning styles and the
implications of theory for educational goals couched in terms of learning
styles. Learning styles; `New' personality model; Cognitive dimension;
Affective dimension; Conative dimension; Model of personality types;
Versatility and personality dynamics; Implications for educational goals.
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PERSONALITY TYPES, LEARNING STYLES AND EDUCATIONAL GOALS
ABSTRACT
Attempts to broaden conceptions of learning styles to represent more
adequately individual differences in motivation/emotion, as well as
cognition, are limited by a paucity of relevant theory. Personality
theories should, but do not, provide a satisfactory conceptual framework
for this endeavour. In an attempt to remedy this situation, a new
personality typology is outlined which, it is argued, provides a coherent
system within which to construe and conduct research upon learning styles.
The implications of the theory for educational goals, couched in teens of
learning styles, also are discussed.
One limitation of education, especially higher education, is that it
overemphasises analytical, intellectual (cognitive) training at the expense
of affective and conative development (Collier, 1988). Similarly, many
well-established conceptions of `learning styles', such as Pask's serialist
(operation)-holist (comprehension) distinction, reflect this cognitive
emphasis (Entwistle, 1981). However, there is more to learning, both as a
process and as a goal, than mere cognition. At the very least, one needs to
consider the effect of motivation and emotion on cognitive development. Or,
to take the matter further, perhaps there is a need to give equal weight to
affective and conative, as well as cognitive, development in formulating
educational goals. In other words, it could be argued that some form of
comprehensive personality development should be the central focus of
education (Rauste-Von Wright, 1986). If one accepts this line of argument,
however, one is left with the problem of, amongst other things,
reconceptualising the notion of `learning style' to reflect these broader
goals. Whilst the idea of a personality-based learning style is not new, I
shall argue here that those that have been developed are, in various ways,
inadequate to the task. The paper begins, therefore, with a brief review of
such learning styles, followed by an outline of a new personality model
which, I believe, provides a more useful conceptual basis for understanding
individual differences in learning. A more detailed discussion of this
theory, along with its conceptual and empirical roots, is available in
Miller (1988, 1991).
Learning Styles
Carl Jung's personality theory is commonly used as a conceptual basis for
learning styles, either directly, through the use of the Myers-Briggs Type
Indicator, or indirectly, as a source of insights about individual
differences (Huff et al., 1986). It is appropriate, therefore, to start
with an evaluation of this theory.
Jung's typology (1923) is derived from three dimensions; a basic attitude
(extraversion-introversion) and two functional (sensation-intuition,
thinking-feeling) dimensions. Unfortunately, the rambling nature of Jung's
writing lends itself to various interpretations, or multilations, as Caine
et al. (1981) put it. For instance, the extraversion-introversion dimension
has been depicted, on the one hand, as a direction-of-interest concept, one
that contrasts an external with an internal orientation to life. Thus the
extravert is said to subordinate inner life to external necessity with
attention being directed at `objective' happenings. The extravert responds
to external demands rather than inner promptings. Introverts, however, are
depicted by Jung as being quite the opposite, concerned primarily with
their own subjective reality, rather than with objective reality. On the
other hand, some elements of Jung's writing on the matter have led others
to interpret extraversion-introversion in terms of individual differences
in surgency and impulsiveness' more a question of temperament than
interest. Attempts to disentangle this conceptual confusion have led to the
conclusion that the predilection of individual theorists for their
particular interpretation of the dimension precludes any consensus on the
matter (Caine et al., 1981). Depending on one's usage, therefore,
extraversion-introversion can refer to individual differences in `social
extraversion' (temperament/emotionality) or `direction-of-interest'
(motivation). In constructing `learning styles', it is important to
recognise the difference between these psychological domains, especially if
one wishes to modify preferred learning styles in some way. A temperamental
interpretation implies a more genetically-based and, hence, less
modifiable, personality characteristic than does the motivational
interpretation. In my own theorizing, to be discussed shortly, I have
separated the two interpretations, locating them within different
personality domains.
Jung's two functional dimensions represent attempts to account for
individual differences in perception and judgment, with unsatisfactory
results it seems. Thus, the perceptual dimension contrasts sensation
(perception by means of the senses, preference for realism, concern with
objective factual detail, acute observation), with intuition (perception by
means of insight, preference for patterns and wholes, and imagination over
facts). Similarly, the judgment dimension contrasts thinking (analytic,
logical, objective judgments; a concern with value-free `truth') with
feeling, (value judgments predominate; sensitivity to explanations in value
terms) (McCaulley, 1981). Unfortunately, the complex nature of Jung's
`functions' has created a number of problems (Storr, 1973). For instance,
the separation of irrational (perception) from rational (judgment)
functions seems odd in light of modern views on information processing. As
I've argued in Miller (1991), one would expect, for instance, that someone
who is analytic in perceptual style would be analytic in memory and
thought. In other words, there appears to be little ground for assuming, as
does Jung, that individual differences in perception and thought lie on
orthogonal dimensions. A more reasonable formulation, therefore, would be
to contrast sensation/thinking (akin to an analytical style) with
intuition/feeling (holistic style). Such a re-organisation seems to `work'
reasonably well for sensation/thinking since both functions emphasise an
analytic concern for detail and logical thought, yet they do not refer to a
`pure' cognitive style since both include conative factors. Thus, sensing
is said to involve a preference for hard realistic facts while thinking is
associated with objectivity and tough-mindedness. Similarly, while
intuition, clearly, is couched in terms of holistic perception, feeling is
virtually a pure description of subjectivity. In other words, Jung fails to
separate his three dimensions, confounding his `functions' with
extraversion-introversion, the basic attitudes to life. Empirical support
for this latter contention comes, for instance, from Coan's (1979) finding
of a significant correlation between objectivity-subjectivity and thinkingfeeling, as well as Forisha's (1983) conclusion that sensation/ thinking is
correlated with objectivity, while subjectivity is related to
intuition/feeling. It follows that, without considerable modification,
Jung's theory does not serve as a coherent basis for conceptualising
learning styles.
As an example of a learning style model that has been influenced by Jung, I
shall focus on Kolb (1984) who recognises two orthogonal, `adaptive
orientations': prehension (the grasping or salting hold of experience) and
transformation (the manipulation of experience). Both dimensions reflect a
cognitive emphasis, corresponding as they do to Piaget's figurative
(prehension) and operative (transformation) aspects of thought (Kolb, 1984,
p. 41). In turn, the prehension dimension contrasts apprehension and
comprehension modes, while reflective and active modes of transformation
are recognised. Combinations of these adaptive modes or learning styles are
said to result in the acquisition of different forms of knowledge (Fig. 1,
Table 1).
Kolb's concrete experience (CE)--abstract conceptualization (AC) dimension
appears to be internally consistent as a cognitive style that contrasts
analytical-verbal (AC) with holistic-concrete (CE) modes. Thus, Kolb's
association of AC with Jung's sensing and thinking, and CE with feeling and
intuition, is consistent with my earlier comments on Jung. However,
reference to the valuing of relationships to others (CE) and the preference
for scientific rigour (AC), which are conative elements, suggests that
Kolb, too, tends to confound conation with cognition (Table I). When we
turn to the active experimentation (AE)-reflective observation (RO)
dimension, it is evident that Kolb wishes to make an external-internal
distinction, akin to Jung's direction-ofinterest conception of
introversion-extraversion. In fact, Kolb takes great pains in stressing his
interest in Jung's epistemological distinctions, rather than the later
interpretations of Jung's work that focus on social extraversion (Kolb,
1984, p. 53). Thus, AE is said to reflect a practical, pragmatic, activism,
one that emphasises doing rather than observing. In contrast, the RO mode
is one that favours understanding over action, and truth over utility. The
fact that the RO mode is said to enjoy intuitive thought, and to rely on
one's own thoughts and feelings in forming opinions (qualities also
ascribed to the CE mode) is, perhaps, a minor quibble with Kolb's
conception. I conclude, therefore, that Kolb's model is a good reading of
recent notions of the analytic-holistic cognitive style and the
extraceptive-intraceptive version of Jung's extraversion-introversion.
There are, however, two deficiencies in Kolb's model and similar models of
learning styles currently in vogue (Huff et al., 1986). First, the
descriptions of each dimension have very little syntax. What I mean by this
is that the dimensions lack theoretical development, especially in light of
the massive empirical literature pertinent to them. Secondly, Kolb's model,
as well as others, omit the darker, more intractable elements of human
personality found, for instance, in Eysenck's conceptions of neuroticism
and psychoticism (Eysenck & Eysenck, 1985), both of which are important in
the educational process. In my own personality model, a description of
which follows, I have tried to deal with both of these issues.
A 'New' Personality Model
The search for adequate conceptions of personality is an ancient one.
However, until recently, many observers of the scene found the end-products
of all this effort profoundly disappointing. Millon (1981), for instance,
observed that little consensus had been achieved about the number or kind
of dimensions needed to represent personality adequately, a consequence of
the excessive subjectivity of researchers whom, he suggested, simply invent
traits to suit their predilections. The net result is that "catalogs of
convenience have replaced meaningful taxonomies of personality traits
amongst most of the current generation of social and personality
researchers" (McCrae & Costa, 1987). One interesting feature of this
research, however, is that some traits seem to recur as their authors
discover ancient precedent or simply reinvent notions temporarily
forgotten. The possibility that these recurring traits may represent basic
or fundamental human characteristics is recognised in the substantial
degree of unanimity emerging recently around what have become known as the
`robust' personality traits.
Conley (1985), Digman and Inouye (1986), and McCrae and Costa (1985, 1987)
summarise extensive recent work in the United States that has confirmed the
earlier findings of, amongst others, Norman (1963) that the personality
domain can be represented adequately by five robust factors (Table II). In
an independent review originating in Britain, of work along the same lines,
Brand (1984) comes to similar conclusions (Table II). The difference
between the two models is due to the preference of some researchers for
splitting the energy surgency/extraversion factor into sociability and
willfulness components (McCrae & Costa, 1987). What I have done in my own
theorising is to adopt these five (or six) factors as hypotheses about the
nature of basic (genotypic) traits and use some of them in developing a
three dimensional typology.
One problem with trait lists, such as those in Table 2, is that there is no
indication of meaningful relationships amongst the traits listed. In
constructing a personality model one has to find some way to depict such
relationships without making the typology too complex, and thereby
impractical, or too simplistic. In seeking an organisational principle that
would accommodate those demands, I'm attracted, along with Buss and Finn
(1987), to the old tripartite system that has informed psychology over the
centuries, one that recognised three psychological domains; cognition,
affection and conation. The use of this system and the selection of one
genotypic dimension to represent each domain results in a relatively
simple, but comprehensive threedimensional typology. There are those who
argue that the recognition of cognition/ affection/conation as separate
processes seriously misrepresents the unity of psychological processes and,
worse, once you have broken the individual into pieces you are faced with
the unenviable task of putting Humpty Dumpty back together again (Bruner,
1986; Revelle, 1983; Santostefano, 1986). Rather than reverting to an
outmoded way of thinking, they say, it would be better to find other ways
of dealing with the multiple processes within each of us. While this is an
admirable sentiment, until other ways are found, one has recourse only to
more traditional approaches which involve the analytic separation of
processes followed by attempts to describe their many interactions. My
personality typology, therefore, is composed of cognitive, affective and
conative dimensions.
The Cognitive Dimension
The identification of an intellectual factor as an important genotypic
trait (Table II) underscored the need to include a cognitive dimension in
the typology. Unfortunately, the precise composition of this factor remains
controversial. From Brand's (1984) British perspective, it is depicted as
general intelligence, g and assessed in terms of mental ability. In
contrast, the U.S. work on the intellect factor is based, not on ability
measures, but on reports of the qualities thought to be associated with
intelligence, which include artistic and intellectual interests, cultural
sophistication, inquiring intellect, and openness to experience. As a
result, American factor analysts have had great difficulty in achieving
consensus on the nature of this fifth factor, although there is agreement
that it is not a matter of ability. We are presented with something of a
dilemma, therefore, whether to represent the cognitive dimension as general
intelligence or in terms of the non-intellective correlates of
intelligence. I chose to do neither, preferring, instead, to think of the
dimension in terms of cognitive styles. There are two reasons for this.
First, ability and intellective conceptions of `intelligence' refer to what
people are capable of or what they prefer to `cognise'. In other words,
they are content aspects of personality. Unfortunately, the content domain
is so large that it is difficult to decide how best to represent it. For
instance, there are many kinds of `intelligence' and much dispute over the
most suitable ways in which to assess each kind (Eysenck & Eysenck, 1985).
In contrast, the stylistic aspects of behaviour (i.e. how people behave)
can be represented parsimoniously by relatively few variables (Royce &
Powell, 1983). Secondly, the available evidence appears to indicate that
cognitive styles exhibit strong cross-situational consistency and are
(according to Bem, 1983) among the most promising genotypic traits one
might include in a personality typology.
The most suitable cognitive style dimension almost selects itself, for as
Brand (1984) notes: "A serious possibility is that there are omnipresent
differences between people in whether they attend narrowly to (self-)
selected aspects of reality or whether they are more broadly attentive" (p.
195). The distinction alluded to here, between cognitive narrowness and
broadness, is ancient and is one that has not only recurred over the
centuries, but also continues to play a major role in the way that
cognitive differences are depicted (Coan, 1979). Of the many labels that
could be used for this style dimension, I prefer analytic-holistic, c, a
distinction also recognised by Kolb in his CE-AC dimension.
In developing this analytic-holistic conception further, I have attempted
to pull together many disparate empirical and conceptual elements from both
the cognitive style and cognitive science literatures into a model of
individual differences in cognitive processing (Miller, 1987, 1991). The
latter proposes that, at each stage of cognition (Fig. 2), one can identify
different cognitive styles (Fig. 3). In other words, the analytic-holistic
dimension is comprised of a set of cognitive styles, each of which
contributes to a consistent individual difference in cognitive processing
(Table III). I should point out that there have been several attempts at
this kind of analysis in the recent past, with varying degrees of success
(Fowler, 1977; Kagan & Kogan, 1970; Royce & Powell, 1983). However, I
believe that the model offered here is plausible as well as being useful in
organising research on cognitive styles.
The Affective Dimension
Two of the robust traits (Table II) have an emotional flavour, namely:
surgency/ energy and emotional stability/neuroticism. Indeed, they are
identical to Eysenck's Extraversion and Neuroticism, respectively, which
are considered to be dimensions of temperament or emotional style. The
possibility of representing the affective dimension of the model in terms
of emotional styles is appealing for, like cognitive styles, they offer a
parsimonious way of depicting a specific personality domain. Given that two
generic traits have been identified, however, the question is which should
be selected for inclusion in the model. My decision has been to use both.
Since the reasoning behind this is too convoluted to summarise more than
briefly here, the reader is directed to Miller (1991) for more adequate
details.
As noted earlier, the trait dimension of `neuroticism' is considered to be
an important component of personality. It appears, in the research
literature, under a number of guises including `emotional instability',
`anxiety' and, as I prefer to regard it, `emotionality' (Brand, 1984).
Although the dimension is evidently an `emotional' one, we need to be more
precise about its relationship to the concept of `emotion'.
Although there are many definitions of the term `emotion' (Epstein, 1984),
there is some agreement that emotions can be conceived of as having at
least three components: physiological mobilisation (arousal), subjective
experience (feeling) and behavioural expression (affect). One can ask two
different questions about these: what combinations of arousal, feeling and
affect occur, and how do people differ in the way in which these components
function? The first of these questions has to do with emotional content and
is about what kinds of emotions occur. The term `emotionality', however, is
clearly not a matter of what emotion is being experienced, but is more
closely linked to the second question: How are emotions experienced and
expressed? Thus, `emotionality' might properly be seen as an emotional
style.
Emotional styles have been studied under the rubric of temperament, where
there is some agreement that one of its major aspects is the intensity of
emotional response (Goldsmith & Campos, 1982; Lerner & Lerner, 1983;
Rothbart & Derryberry, 1981; Thomas, 1985). This emphasis stems, no doubt,
from Allport's (1961) pre-feminist conception of temperament as: " . . .
the characteristic phenomena of an individual's emotional nature, including
his susceptibility of emotional stimulation, his customary strength and
speed of response, the quality of his prevailing mood, and all
peculiarities of fluctuation and intensity of mood . . . " (p. 34).
Emotionality, therefore, may be seen as a temperamental or emotional style
that reflects the intensity of emotional experience. It follows that this
intensity should cut across emotional content. That is, it should be
associated with a variety of emotions, both positive and negative. However,
there is a tendency, in the literature, to equate `emotionality with
`neuroticism'. The latter reflecting an intense experience of the more
negative emotions (Eysenck & Eysenck, 1985). For example, Buss & Plomin
(1984) regard emotionality as primordial distress, defining it as: " . . .
the tendency to become upset easily and intensely. Compared to unemotional
people, emotional people become distressed when confronted with emotionladen stimuli--the stresses of everyday life--and they react with higher
levels of emotional arousal. It follows that they should be harder to
soothe" (p. 54). Why emotionality should be conceptualised primarily in
terms of distress puzzles Zuckerman (1985) who reflects that most of our
theories do not account for positive emotions, but focus, instead, on what
he calls the unhappy triad of fear, anxiety and depression (FAD). The
reason for this is not clear, for, as Rothbart and Derryberry (1981)
comment, a style definition implies that those who show intense distress
would also be expected, at some other time, to show intense positive
emotion. Evidence in favour of this latter assumption has been marshalled
by Larsen and Diener (1987) who, in proposing the stable individual
difference of affective intensity, argue that " . . . the intensity of an
individual's affective responsiveness generalizes across specific emotion
categories implying a general temperament dimension of emotional reactivity
and variability" (p. 1). The affective dimension of my personality model,
therefore, combines elements of social extraversion and neuroticism into an
affective intensity dimension labelled emotional stability-instability.
The Conative Dimension
The term conation appears to have dropped out of common usage along with
the demise of faculty psychology, being replaced by the more familiar term
motivation. However, conation and conative carry implications of an
effortful, striving, self-willing form of behaviour rather than the
expression of some biological urge or a mechanical response to situational
pressures. In other words, conation has to do with volition, a
psychological concept that has languished in the back-waters of
psychological theory for decades and is only now beginning to re-surface
(Westcott, 1985). The selection of a conative dimension for my model is
aided by Brand's (1984) observation of what he calls a certain negative
tension between his will and affection factors which suggests the
possibility of a higher-order will-affection dimension that is clearly
conative in nature. The contrast he has in mind is between striving for
autonomy/power/masculinity (will) and dependence/co-operation/femininity
(affection). One of the attractive features of a will-affection dimension
is that it bears a striking resemblance to William James' (1907) toughtender mindedness, a dichotomy that is said to date back to the SocraticSophist dialogues of ancient times and continues to the present day in
various guises (Kimble, 1984). In conceptualising the conative dimension of
the present model. I set myself the task of developing a more modern
version of this grand polarity. Rather than using James' term tough-minded
v. tender-minded as a label for the dimension, one that has accumulated an
excess of pejorative baggage, I prefer Coan (1979) and Cotgrove's (1982)
more neutral term objective-subjective.
My conception of objectivity-subjectivity is firmly rooted in the
intrapsychic conflict tradition in personality theory, the most notable
versions of which are associated with Angyal and Bakan (Maddi, 1976). Both
theorists consider that we are riven by a persistent conflict between two
inherent and opposing sets of drives (what Maddi calls core tendencies). On
the one hand, we are compelled to assert our individuality, to separate
ourselves from others and to curtail our dependence on them (autonomy,
agency). On the other hand, we experience the urge to join with others in
co-operative, intimate and, often, dependent relationships (surrender,
communion). In other words, human behaviour is seen to result from a
continuing conflict between innate sets of self-assertive and selfabnegating drives, all of which operate at an unconscious level. Maddi
explains further that, while the two great forces may seem to be
irrevocably opposed, psychological health or integrity requires that some
compromise be found. The most successful integration would seem to be one
in which both core tendencies are represented as much as possible in living
one's life.
What I shall assume, therefore, is that objectivity and subjectivity
represent concrete expressions of the more abstract tendencies toward
agency and communion. There have been many empirical studies of the
'concrete expressions', resulting in a profusion of findings under various
rubrics. The problem we face in attempting to discuss this information is
finding some systematic way of doing so. For instance, amongst the terms
that have been used to label motivational dispositions, one finds: motive,
goal, interest, value, expectation, purpose, plan, desire, wish and
intention, to name but a few. To cut a long story short (see Miller, 1991,
for details), I propose that objectivity-subjectivity be seen as a motive
dimension, one that is comprised of three subdimensions: power-love,
emotional detachment-empathy and extraception-intraception. Thus, the
objective individual is conceptualised as power-seeking, emotionally
detached and prone to imposing his/her frame of reference on events. Taken
to an extreme, objectivity reflects Eysenck's Psychoticism, a tough-minded,
often cruel, impersonal orientation to others. In contrast, the subjective
individual seeks loving, more empathetic relationships with others, an
orientation aided by his/her intraceptive perspective on life. This too can
be taken to an extreme, one that exhibits itself as an excessive dependency
on others. The implications of these value orientations for educational
methods and goals will be discussed shortly.
A Model of Personality Types
The three generic dimensions outlined above have been incorporated into the
personality model shown in Fig. 4. In describing the personality types so
generated, I find it convenient, following Cotgrove (1982), to define main
types in terms of the analytic holistic and objective-subjective dimensions
(Fig. 5), assuming further, that each of these main types exhibits stable
and unstable variants. A brief description of the main types follows.
The objective-analytic type (OA)
The objective aspect of this type is reflected in the adoption of a toughminded orientation to life in which the central concern is the achievement
of a sense of agency, a sense of control over oneself and one's immediate
environment. This goal may be pursued by seeking power over others, or by
resisting the attempts by others to exert power over oneself. In either
case, the attainment of power is facilitated by establishing a degree of
control over one's emotional and cognitive interactions with other people.
Typically, the OA type achieves this by a process of distancing. Thus,
emotional control is attained by maintaining emotional detachment, the
advantage of which is that limited emotional involvement reduces the
likelihood that one will be bothered by the vagaries of one's own emotions
or by emotional pressure exerted by others. Similarly, cognitive control is
sought through an extraspective stance. The potential confusion engendered
by taking another person's viewpoint into account is thereby avoided, as
are the upsetting consequences of introspection, the exploration of one's
subjective world. An illusion of control is sustained, therefore, by
focusing on the exterior world of objective certainty, the world of outward
appearance and physical reality. As a consequence, a mechanistic world view
is developed in which simple cause-effect relationships are sought as a
means of understanding and control.
The analytic component of the OA type can be viewed as a strategy for
achieving objective ends. If one holds a mechanistic view of life, in which
both people and things are subject to simple rules of physical causality,
then one way to ensure the control of events is to pay painstaking
attention to the minutiae of external experience. This goal is facilitated
by an analytical style in which factual detail is sought in a relatively
circumscribed field. Thus 'objective' facts are thought to provide the key
to understanding and control, whereas intuitions and impressions are not.
When these emotional and cognitive control strategies show signs of
failing, and the emotional/subjective world begins to intrude, then
defensive coping is brought to bear. Typically, the OA type would be
inclined toward the use of articulated defenses, after the fashion of a
surgeon's scalpel, carefully separating unruly emotions from thoughts so
that disturbing events are made emotionally bland while remaining
cognitively amenable. Characteristic forms of defence are, therefore,
emotional isolation, intellectualisation and rationalisation. In summary,
the OA type is empirical, reductionist, impersonal and obsessive. It
follows that members of this category might be labelled reductionists, a
prototypical example being the analytical scientist, one who seeks
understanding and control through the collection of "objective data" in a
narrowly defined segment of the external, physical world.
The objective-holistic type (OH)
The OH and OA types share the same objective, impersonal manipulative
orientation, but power/control is achieved using different strategies.
Rather than an attempt to understand and control reality by seeking factual
information (as one finds in the OA type), the illusion of control is
achieved by the development of schemes, theories, systems of thought and/or
fantasies, all of which serve to organise and control 'reality'. In
contrast to the OA type who seeks to document reality, therefore, the OH
type may seek to impose a system of order on to it, or to seek to force the
surrounding environment to comply with the fit into his/her model of how
things should be. When the mismatch between fantasy and reality becomes too
great and the OH type's conceptual schemes are threatened, then defences of
a global nature, such as denial and repression, are used to suppress this
unpleasant truth.
Prototypical examples of this schematist category are the intellectual
model builders, rationalists and philosophers who weave speculative tales
about physical reality. A case in point is the philosopher-novelist Ayn
Rand, famous for her philosophical system of 'objectivism' a homage to the
power of will, striving and personal accomplishment. Yet, in her private
life, Ayn Rand was, apparently, either incapable or unwilling to engage in
introspection with the result that she understood little of her own
behaviour and its effects on others. Nor was she sensitive to the emotional
states of others, unless these were brought to her attention forcefully
(Brander, 1986).
The subjective-holistic type (SH)
The subjective aspect of this type is reflected in a primary concern with
establishing communion through intimate, nurturing relationships with other
people and with the surrounding environment. Given this urge to blend and
join, and the implication that the self of the SH type is relatively
'permeable', then there is less concern with protecting the self from the
influence of others. Indeed, SH types seek to establish a sense of self by
joining with others in what the more objective types derisorily refer to as
'dependent' relationships. Regardless of how one might label this
behaviour, it does seem that SH types, rather than seeking power over
others, strive to empower others through nurturing behaviour. This is
facilitated by a well-developed cognitive and emotional empathy, all of
which implies a main interest in subjective experience be that of one's own
inner reality or the inner, psychological world of others. Feelings and
personal impressions are given priority over the details of 'objective
reality'.
When subjectivity is coupled with an holistic style, then one finds a lack
of interest in the analysis of personal experience and a concomitant
preference for experiencing subjective reality intuitively or globally. It
is possible that SH types view analysis as another form of separation, an
alienating experience that they prefer to avoid. As a result, introspection
(in the sense of analysis of subjective experience) is kept to a minimum,
although intraception remains a major orientation. The absence of analysis
and the lack of concern about emotional control results in personal
reactions intruding into thought, making the latter evaluative,
emotionally-tinged and intensely subjective. Thus, the romantic lives in an
impressionistic, often imaginative, world of personal anecdote and
unanalysed subjective experience.
Given this interest in communion, and the dislike of separation, it follows
that the primary fear for the SH type would be separation anxiety which, in
adults, would be generated by an inability to establish intimate contact
with others, especially loved ones. The defensive reaction to separation
anxiety, and the unwarranted intrusion of the impersonal, objective world,
would be massive repression and denial, the use of unarticulated defences
so characteristic of all holistic types.
The subjective-analytic type (SA)
The SA type shares with the preceding SH type a primary concern with
establishing communion, a focus on subjective experience and a lack of
interest in, or distaste for, the objectified, impersonal world. The
difference between the two types lies, however, in the strategy used to
achieve communion and in their level of tolerance of separation anxiety.
Thus, the adoption of an analytical style by the SA type appears to presume
that contact with others is best achieved through understanding and
knowledge. It is as if the subjective-holist emphasises emotional empathy
while the subjective-analytist emphasises cognitive empathy. It follows
that the SA type engages in the analysis of personal experience, a
psychologically-minded search for the source of one's inner life and that
of others. As a consequence, there is a tendency to withdraw into a
reflective, narrowly preoccupied world of introspective thought at the
expense of engagement in the broader reality. Since analysis has the effect
of distancing oneself from the thing being analysed, I would presume that
the SA type has a greater tolerance of separation anxiety than one would
find in the SH type, although such tolerance would be much less than that
found in objective individuals. Where intimacy is frustrated and objective
reality intrudes into the introspective mindscape, then the SA type has a
particular problem. Of all four types, the SA person has the greatest
difficulty in summoning effective defensive coping. Their commitment to
communion, with its implication for the integration of parts of the self
into an homogeneous unit, mitigates against the deployment of articulated
defences, while their inclination to introspective analysis prevents the
use of global defenses. Thus, the SA type has difficulty in protecting
himself/herself from what Smail (1984) calls the horrors of psychological
honesty.
Subtypes
Within each of the four main types, at least two subtypes can be formed
from extreme positions on the emotionality dimension. Thus, one would see,
for example, emotionally stable and unstable forms of the
analytic/objective (reductionist) type, and so on. The distinction afforded
by these subtypes draws attention to the relative intensity of people's
lives. As mentioned earlier, the emotionally unstable individual is prone
to distress, reacting to the world with fear and anxiety, the implication
being that defence mechanisms would play a major role in keeping this
within bounds. For those who are unable to use defence mechanisms
effectively, one would expect to find lives of great distress and
unhappiness, as Smail (1984) has described at some length. At the opposite
extreme are those who appear to react little to the world around them, an
unresponsiveness that may verge on apathy. I would speculate that their
problem is not so much to allay anxiety, but to convince those around them
that they are actually emotionally alive, assuming of course, that they
would wish to do so. Recognition of these subtypes allows us to distinguish
between, for example, the emotionally unstable and defensive reductionist
(the classical obsessive), and a more bland, emotionally inert
reductionist. Something of the sort has been recognised in Maslow's (1966)
contrast between safety science and growth science. I would speculate that
safety science emanates from emotionally unstable reductionists. While
growth science is associated with the emotionally stable reductionist (and
others).
Versatile types
The present model allows one to recognise what might be called versatile
types, individuals who have achieved some harmonious balance between the
conflicting motives underlying objectivity-subjectivity; who are not
excessively emotionally stable or unstable, and who are capable of
employing both analytical and holistic styles where appropriate. Although
such psychic efficiency may be rare (Hudson, 1968), it could be argued that
versatility is a desirable educational goal. Whether this is, in fact, a
practical ideal is a matter to which we now turn.
Versatility and Personality Dynamics
Versatility, the ability to adapt flexibly to life's demands, is a common
theme in conceptions of the optimal personality (Coan, 1974). Intrapsychic
theorists, for instance, extol the virtues of some judicious mix of agency
and communion as an ideal compromise in life (Maddi, 1976). A similar
sentiment is to be found in the cognitive and learning style literatures,
where stylistic versatility is lauded (Entwistle, 1981).It follows that
many style-based systems of teaching encourage students to make more use of
styles other than those they normally prefer (Huff et al., 1986). There are
claims of success in such endeavours (Kolb, 1984, p.206), but I remain
skeptical. If learning styles are defined more comprehensively as
personality styles (or types), then formidable obstacles stand in the way
of change. It may be possible to achieve some superficial behavioural
changes amongst most students, but I doubt that these would be anything
other than ephemeral. This conclusion is based on what is known about the
relationships between styles and personality dynamics.
Far from being simple habits that can be changed at will, some believe
learning styles to be complex adjustments to life that are learned early in
life and remain held in place, as it were, by demands of psychodynamics
(Hudson, 1968, 1970; Witkin & Goodenough, 1981). Hudson, for instance,
depicts convergent and divergent styles as forms of psychological defence.
If this is so, then it is likely that attempts to modify an individual's
style could generate varying degrees of distress and/or hostility. The
reason for this is clear enough. The control of anxiety within tolerable
limits is a central feature of human adjustment (Maddi, 1976). One strategy
for achieving this end is to screen everyday events for their `threat'
value, using selective inattention to avoid anything troublesome that
promises to disturb our peace of mind (Goleman, 1985). Over time, we
develop characteristic styles of selective inattention (defences) which, in
turn, form the bases of personality styles. For instance, to reiterate some
earlier points, the psychodynamic thrust of each of my four main polarities
appears to be:
(1) the analytic style is a way of seeking certainty through the pursuit of
detail within a circumscribed domain, thereby avoiding the uncertainty and
attendant anxiety generated by the larger reality.
(2) the holistic style seeks certainty in flights of fancy, elaborate
schemes which provide an illusion of control and an escape from troublesome
empirical reality;
(3) the objective style focuses on the material, impersonal world thereby
avoiding the anxiety created by the irrational, unpredictable world of
emotion and subjectivity;
(4) the subjective style, in contrast, avoids the harshness of objective,
material reality in favour of the security and warmth of personal
relationships.
This notion of style-as-defence and the particular interpretation offered
above is afforded some degree of support by research on personality
disorders. (For more details on the empirical and conceptual structure of
personality disorders and the reasoning behind the arguments offered here
the reader is directed to Miller, 1991). For instance, many of the
personality disorders recognised in DSM-III can be construed as extreme
forms of my four main types (Fig. 6, Table IV). In other words, personality
disorders appear to arise in cases of stylistic `specialisation' where
individuals appear to have difficulty in switching styles to accommodate
changing circumstances. They suffer, it seems, from an inadvertent excess
of a dominant style. It is interesting to note that descriptions of each
disorder are consistent with conceptions of my main types. Thus, the
compulsive disorder (OA) exhibits an excessive and persistent concern with
factual detail and routine coupled with a rejection of emotional
involvement with others, presumably to avoid a feeling of loss of control
over life events (Millon, 1981; Pollak, 1979; Shapiro' 1965). The
histrionic disorder (OH), on the other hand, shows an emotionally labile,
shallow, exhibitionism, one that carefully avoids the chastening influence
of factual reality (Millon, 1981; Pollak, 1981; Shapiro, 1965). Recently
referred to as a masochistic type, the dependent disorder (SH) couples
ingenuous docility with excessive needs for affection and nurturance.
Refuge from the difficulties presented by the material world is sought
within a dependent relationship (Millon, 1981). Finally, the avoidant
disorder (SA) is one of social anxiety, sensitive perceptiveness and
excessive rumination. Such individuals seek, but do not find, security
through emotional contact with others. I he harshness of the objective
world is avoided through withdrawal into an introspective realm (Millon,
1981; Smail, 1984).
In summary, the more stylistically `specialised' an individual, the more
difficult will it be to encourage versatility. This is because
specialisation serves a defensive function in protecting the individual
from anxiety. Since stylistic specialisation is common amongst students, it
is unlikely that many will welcome concerted efforts to modify their
entrenched styles. This will be particularly true amongst those emotionally
unstable students who are, according to my model, easily distressed and,
therefore, heavily defended. What, then, are the implications of all this
for educational goals?
Implications for Educational Goals
Let me begin this section with a personal anecdote. Some years ago, I had
the dubious pleasure of teaching at a small agricultural college. Along
with other members of my department, I shared the task of developing some
psychosocial understanding amongst agricultural students destined to become
advisors within the government system. The material I covered in lectures
was, to my mind, relatively innocuous, a smattering of ideas from
psychology. Nothing prepared me for their reaction to this modest
endeavour. A small minority, less than a third, showed interest, while a
middle group were quite indifferent. It was the remaining third or so who
were the most interesting and disturbing, for their reaction was one of
sullen hostility. To say the least, I was startled by the viciousness of it
all and only later came to realise that what to me was relatively innocuous
material was, to them, profoundly disturbing. It seems I was asking them to
delve into the subjective-emotional realm, something that was anathema to
them. I suspect that if I had been foolish enough to try to modify their
personal styles, rather than simply presenting some ideas for discussion,
the reaction would have been even more negative. This experience, which has
been repeated in other situations, together with the implications of the
above model, leads me to the following conclusions.
First, I believe that wholesale attempts to encourage stylistic versatility
in all students is not only a waste of time and resources, but also can be
psychologically damaging. Extremely specialised students should be left
alone, secure within the confines of their dominant mode. Certainly,
attempts should be made to adjust teaching to suit these styles, but not to
change them. It follows that versatility is a reasonable goal for those who
are already predisposed to it. In other words, to those that hath shall be
given. The agenda for research, in such circumstances, would be to find
ways of identifying the specialised and the proto-versatile, thereby
determining who should be left alone. Secondly, it would seem that treating
learning styles as cognitive styles, bereft of affective, motivational and
defensive implications, is naive. Teaching systems based on such
assumptions, therefore, are likely to be ingenuous and, possibly,
dangerous. Thirdly, many ethical questions are raised by attempts to modify
styles, personality or otherwise. Separating out relatively flexible
students for special treatment smacks of elitism and would be
controversial. Similarly, any tinkering with personality styles would
require informed consent, some ongoing discussion between teacher and
learner about the purpose and methods of education. The last time I heard
of this happening on any scale was in 1968. Finally, a genuine concern for
personality development as a goal of education would require that teaching
becomes a form of counselling over and above the mere transmission of
information. This is unlikely to happen since the traditional separation of
`intellect' from `personality' is too entrenched in academic circles.
However, versatility is, I believe, an eminently sensible educational goal,
one that is achievable, perhaps, in isolated pockets where there are
teachers who have the necessary understanding and commitment to their
students.
Correspondence: A. Miller, Psychology Department, University of New
Brunswick, Fredericton, NB, Canada E3B 6E4.
TABLE I. Kolb's learning orientations[1]
1. An orientation toward concrete experience focuses on being involved in
experiences and healing with immediate human situation in a personal way.
It emphasises feeling as opposed to thinking; as a concern with the
uniqueness and complexity of present reality opposed to theories and
generalisations; an intuitive, 'artistic' approach as opposed to the
systematic, scientific approach to problems. People with concreteexperience orientation enjoy and are good at relating to others. They are
often good intuitive decision makers and function well in unstructured
situations. The person with this orientation values relating to people and
being involved in real situations, and has an open-minded approach to life.
2. An orientation toward reflective observation focuses on understanding
the meaning of ideas and situations by carefully observing and impartially
describing them. It emphasises understanding as opposed to practical
application; a concern with what is true or how things happen as opposed to
what will work; an emphasis on reflection as opposed to action. People with
a reflective orientation enjoy intuiting the meaning of situations and
ideas, and are god at seeing their implications. They are good at looking
at things from different perspective and at appreciating different points
of view. They like to rely on their own thoughts and feelings to form
opinions. People with this orientation value patience, impartiality, and
considered, thoughtful judgment.
3. An orientation toward abstract conceptualisation focuses on using logic,
ideas and concepts. It emphasises thinking as opposed to feeling: a concern
with building general theories as opposed to intuitively understanding
unique, specific areas; as opposed to an artistic approach to problems. A
person with an abstract-conceptual orientation enjoys and is good at
systematic planning, manipulation of abstract symbols, and quantitative
analysis. People with this orientation value precision, the rigor and
discipline of analysis ideas, and the aesthetic quality of a neat
conceptual system.
4. An orientation toward active experimentation focuses on actively
influencing people and changing situations. It emphasises practical
applications as opposed to reflective understanding; a pragmatic concern
with what works as opposed to what is absolute truth; an emphasis on doing
as opposed to observing. People with an active-experimentation enjoy and
are good at getting things accomplished. They also value having an
influence on the environment around them and like to see results.
[1] From Kolb (1984).
TABLE II. The robust personality traits[*]
USA
UK
Surgency
talkative-silent
sociable-reclusive
adventurous- cautions
Energy
talkative-silent
sociable-unsociable
adventurous- cautions
Agreeableness
good natured-irritable
mild-headstrong
co-operative-negativistic
Affection
trusting-suspicious
affectionate-hostile
co-operative-uncooperative
Conscientiousness
responsible-undependable
persevering-quitting
tidy-careless
Conscience
responsible-irresponsible
persistence-quitting
order-disorder
Emotional stability
calm-anxious
composed-excitable
poised-nervous
Neuroticism
calm-anxious
composed-excitable
poised-nervous
Intellect
intellectual-non-reflective
imaginative-simple
artistically
sensitive-insensitive
Intelligence
general intelligence ('g')
cognitive ability
analytical capacity
Will
independent-dependent
dominating-submissive
strong willed-weak
[*] Modified from Digman & Inouye (1986) and Brand (1984).
TABLE III. Relationship between cognitive styles
Cognitive process
Analytic style
Pattern recognition
Selective attention
Analytic
Field independence
Representation
Organisation
Verbal/analytic
Conceptual differentiation
Retrieval
Classification
Analogical reasoning
Judgment
Convergence
Serial
Tight
Actuarial
Holistic
Field
dependence
Visual/analog
Conceptual
holism
Divergence
Holistic
Loose
Intuitive
TABLE IV. Personality disorders
Disorder
Objective/subjective
Compulsive
objective: preoccupied with self-control; avoids
introspection and attains little self-insight;
seeks emotional detachment; little ability to
express warmth and tenderness; rigidly structures
environment; overly concerned with
rules, procedures and formalities.
Antisocial
objective: power-oriented, tough, unsentimental;
obtains gratification by humiliating
and dominating others, callous, insensitive
and vindictive; absence of self-insight;
contemptuous of intimacy, compassion,
emotional warmth.
Paranoid
objective: inordinate fear of losing independence
and power to shape events in accord
with grandiose sense of self; mistrusts others,
seeks to avoid entrapment by becoming hard,
obdurate, vigilant; lacks self-insight,
compassion, warmth.
Narcissistic
objective: shows interpersonal exploitativeness,
uses others to indulge self; emotional
detachment, low empathy, lack of regard for
others; avoids introspection and lacks
self-insight.
Histrionic
objective: avoids introspection; experiences a
barren intrapsychic world, an inner emptiness;
compensates by actively seeking attention,
reassurance; manipulative, seductive;
intensely extraceptive.
Dependent
subjective: overly strong needs for affection
and nurturance; non-competitive, avoids
autonomy; subordinates own needs to those of
others; emotionally warm, tender, considerate;
friendly, obliging, generous, obsequious.
Avoidant
subjective: desires affection and acceptance
but socially anxious; empathetic; intensely
sensitive to rejection, humiliation; uncertain
about to the introspective world of thoughts
and feelings.
Disorder
Analytic/holistic
Compulsive
analytic: exhibits a narrow, small-minded
outlook; a preoccupation with trivial detail
and objective 'facts', all of which preclude the
possibility of developing a broader perspective.
Antisocial
analytic and holistic: most exhibit clarity and
logic in their thinking (implying analytic
capacity) but rarely exhibit foresight; the
success of some variants implies a versatile
cognitive style.
Paranoid
analytic and holistic: lives in a world of
fantasy and delusion composed of fixed
beliefs, and unrealistic perceptions (holistic);
hypervigilant, intense and narrow search for
confirmation of expectations (analytic).
Narcissistic
holistic: preoccupied with pretentious,
unrealistic fantasies; takes liberties with the
'facts' in refashioning 'reality' to his/her own
liking. Imaginative, cognitively expansive.
Histrionic
holistic: prone to flights of (romantic) fantasy;
thought processes scattered; little interest in
careful analysis; pays fleeting attention to
detail; inability t think in a concentrated,
logical fashion.
Dependent
holistic: tends to be naive, unperceptive,
uncritical; inclined to see only the pleasant
side of troubling events; minimally introspective,
a pollyanna perspective on life.
Avoidant
analytic: sensitive, acutely perceptive observer;
hyperalert to feelings and intentions of
others; vigilant scanning for signs of rejection;
tends to be excessively introspective and
self-conscious.
Disorder
Emotional stability/instability
Compulsive
unstable: sits stop a powder-keg of inner
turmoil, his/her greatest task being to control
the intense feelings that lurk below a cloak of
respectability. Commonly prone to anxiety
disorders.
Antisocial
unstable: frequent signs of emotional distress
and dysphoria; an irrascible temper that flares
easily into fury and vindictiveness.
Paranoid
unstable: finds it difficult to relax; appears
tense, edgy, irritable, disputations, factious
abrasive; prone to extremes of mood and
general anxiety disorders.
Narcissistic
stable: affect is generally relaxed; a pervasive
sense of well-being; a buoyancy of mood; does
not characteristically develop anxiety
disorders.
Histrionic
unstable: lively, dramatic and exhibitionistic;
highly labile emotions, overly reactive, easily
excitable, capricious and given to angry
outbursts or tantrums; intensely expressive.
Dependent
stable: a pacific temperament; docile, friendly,
but with a tendency to maudlin sentimentality.
Avoidant
unstable: experiences recurrent anxiety and
mood disharmonies, affective dysphoria,
easily distressed by rejection; upset by lack of
social ease; prone to anxiety disorders.
Note: based on description from Disorders of Personality:
DSM--III Axis II by T, Millon, 1981 (New York, Wiley).
FIG. 1. Kolb's model of learning styles (from Kolb, 1984).
FIG. 2. An information processing model of cognition.
FIG. 3. A model of cognitive styles and cognitive processes (from Miller,
1987).
DIAGRAM: FIG. 4. A three-dimensional model of personality.
DIAGRAM: FIG. 5. The four main types (A = analytic; H = holistic; O =
objective; S = subjective).
DIAGRAM: FIG. 6. Personality types nd disorders.
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~~~~~~~~
By ALAN MILLER, Psychology Department, University of New Brunswick,
Fredericton, NB, Canada E3B 6E4
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Title: Effects of a learning styles and strategies intervention upon atrisk middle school students...
Subject(s): ACADEMIC achievement -- Psychological aspects; LOCUS of
control; LEARNING, Psychology of
Source: Journal of Instructional Psychology, Mar95, Vol. 22 Issue 1, p34,
6p, 1 chart, 1 diagram, 2 graphs
Author(s): Nunn, Gerald D.
Abstract: Examines the effects of a year-long learning styles/strategies
intervention course on achievement and locus of control of at-risk middleschool students. Characteristics of at-risk students; Significant
improvements within the at-risk intervention group; Implications for
educators.
AN: 9505042097
ISSN: 0094-1956
Database: Academic Search Premier
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EFFECTS OF A LEARNING STYLES AND STRATEGIES INTERVENTION UPON AT-RISK
MIDDLE SCHOOL STUDENTS ACHIEVEMENT AND LOCUS OF CONTROL
The present paper examines effects upon achievement and locus of control of
at-risk middle school students (N = 103) enrolled in a year long learning
styles/strategies intervention course. Results indicated significant
improvement within the at-risk intervention group in grade point average
and locus of control, i.e., decreased externality. Implications and further
research is discussed.
American education is currently addressing the needs of students termed
"at-risk", and faces ongoing challenges to enrich school experiences for
these students to capture talents human resources (Rumberger, 1987). In
1985-86, there were more than 600,000 youth who dropped out of school with
an anticipated cost of $120 billion in lost revenue during theft lifetimes
(Hamby, 1989). Within this context, schools have been increasingly called
upon to provide for a continuum of student needs which exceed traditional
educational parameters. Students considered to be at-risk may be considered
so for a variety of reasons which may include issues of learning problems,
academic achievement, motivation, cultural differences, mental health
issues, teenage pregnancy, and drug abuse which make adjustment to the
school environment difficult (Mills, Dunham, & Alpert, 1988). Ehly & Perish
(1990) have, therefore, considered "at-risk" a concept which includes a
variety of factors detrimental to educational opportunity and school
completion.
Observers of the educational system call for substantive restructuring of
how education is provided for students, and in particular, how education
addresses needs of students already at-risk for serious school problems
(Raebeck, 1990). Research into effective educational practices lends
support for strategies that promote active involvement, encourage selfmanagement of learning, enable insight regarding student strengths, nurture
internal locus of control and strengthen individual differences as they
relate to the school experience (Lindgren, 1980; Stipek & Weisz, 1981). The
concepts of learning style and learning strategies have shown promise in
addressing these areas of educational need.
According to Keefe (1988), learning style is "...an umbrella term
encompassing cognitive, affective, and physiological/environmental
dimensions." (pg. 5). Learning style research has as it's goal a "...more
personalized and effective system of education" (Keefe, 1982, pg. 2). As
Brandt (1990) has pointed out, the last ten years have witnessed
considerable experimentation with learning styles and their relation to
student lemming. Proponents of this approach believe that, through the
process of exploring learning styles, positive effects upon student
motivation and achievement are produced. Advocates also believe that
learning style intervention exemplifies the concept of individualization by
providing a unique glimpse into how a student learns. The approach is
further seen as proactive in nature rather than diagnostic-remedial, by
emphasizing the importance of working with student strengths rather than
weaknesses.
Derry (1989) defines learning strategies as "A
for accomplishing a learning goal" (pg. 5). As
can be thought of as specific tactics by which
manipulate, and perform in relation to defined
complete plan one formulates
such, learning strategies
students organize, retrieve,
learning outcomes.
Learning strategies form discrete instructional methods by which a student
can take control over their learning and performance. Research in this area
has attempted to bridge the gap between what is known about effective
teaching/learning practices and student application of strategies.
Proponents of learning strategy models point to the benefits of teaching
specific ways students may learn more effectively rather than the
traditional focus upon instructional process and content (Cook, 1983;
Derry, Jacobs, & Murphy, 1987). Such learning may have greater relevance
for students, and teaches them that learning is something in which they
have a definite role and purpose. As the Strategies Intervention Model
(SIM) in Figure 1 illustrates, learning strategies may be thought of as
critical themes which can lead to specific skills in acquisition, storage,
motivation, and expression/demonstration of competence regarding knowledge
obtained in school (Deshler & Shumaker, 1987).
The present study has, therefore, examined effects of utilizing these
approaches with students who have had problematic histories of school
failure which places them "at-risk" for continued difficulties as well as
dropping out in the future. The goal of this study was to determine how
systematic application of learning styles and strategies instruction
affects student success in school as measured by school achievement and
perceived student locus of control.
Students. In all, 103 students (59 males and 44 females), in grades 7 & 8
voluntarily participated in this study. These students were selected
randomly from school rosters to represent at-risk and nonat-risk
populations. Racial background of the total group consisted of: White =
93.2%; Black = 5.83%; Native American = 0%; Asian = 0%; Hispanic = .97%.
Design. A Nonequivalent Control-Group Design was used in this study (Borg
and Gall, 1989). Students representative of at-risk and non at-risk
populations were assigned to three comparison groups: At-Risk Intervention
(i.e. problematic school performance with intervention); At-Risk
Nonintervention (i.e. problematic school performance without intervention),
and a General Education Control (i.e. students demonstrating average
academic performance without intervention).
Measures. Locus of control was assessed with the Nowicki-Strickland Locus
of Control Scale (Nowicki & Strickland, 1973). This instrument is a well
researched locus of control instrument, and has demonstrated satisfactory
reliability and validity (Nunn, 1986; Nunn, 1987; Nunn, 1988; Nunn, 1989).
Measures of achievement were taken from current and previous grade reports
kept in student files.
Procedure. The Learning Styles/Strategies Intervention Course was staffed
by experienced teachers at the middle school, and met for one class period
every other day during the school 6-day cycle for the school year. The
primary focus of this course was to help students apply learning styles and
strategies to facilitate positive adjustment to school. All students in the
intervention course had their learning styles assessed, profiled, and
interpreted for them. The Learning Styles Inventory (Canfield, 1988) was
used as a measure of learning style. The instrument is an easily
administered, self-scoring inventory which yields comparative scales
related to: Conditions of Learning, e.g. peer, goal setting, independence,
competition; Area of Interest, e.g. numeric, qualitative, people,
inanimate; Mode of Learning, e.g. listening, reading, direct experience;
Expectation of Grade, e.g. A, B, C,D; and Learner Typology, e.g. Social,
Independent, Applied, Neutral. The Strategies Intervention Model (SIM)
(Deshler & Shumaker, 1987) was used to systematically focus upon strategies
which would compliment learning styles and improve performance. Strategies
which focused upon acquisition, storage, motivation, and expression of
competence made up the primary curriculum. Also, throughout the year,
students were encouraged to conference with teachers to set goals and
problem-solve ways to improve their performance by using strategies and
styles reinforced in the intervention course.
Results
Pre-post outcomes were analyzed using a two-factor repeated measures ANOVA
with comparison groups of At-Risk Intervention (AR/); At-Risk
Nonintervention (ARN); and General Education Control (GEC). With regard to
effect upon Grade Point Average, a significant treatment effect was
obtained, F(2,88) = 43.14, p<.0001, as well as a significant treatment X
repeated measure interaction effect F(2,88) = 4.79, p<.01. With respect to
the interaction effect, students in the ARI group significantly improved
grade point averages (90-91 GPA = 1.78 vs. 91-92 GPA = 1.95), while
students in the ARN group significantly decreased their performance (90-91
GPA = 2.09 vs. 91-92 GPA = 1.8), with no significant change in the GEC
group (90-91 that the ARI group decreased externality scores (Mean Fall 91
= 16.85 vs. Mean Spring 92 = 12.96) while the ARN group increased
externality scores (Mean Fall 91 = 16.14 vs. Mean Spring 92 = 19.79), and
the GEC group did not change significantly (Mean Fall 91 = 12.65 vs. Mean
Spring 92 = 13.06).
Discussion
The present analysis has provided tentative support for the effectiveness
of this intervention in significantly improving the school GPA = 3.18 vs.
91-92 GPA = 3.17).
Locus of control also revealed significant main effects for Treatment
F(2,80) = 3.12, p <.05; for the Repeated Measure F(1,80) = 3.99, p <.05,
and was significant for Treatment X Repeated Measure interaction F(2,80) =
4.49, p<.05). Mean comparisons indicated adjustment of at-risk students by
increasing grade point averages and decreasing external locus of control.
It appears that, in this instance, a combined learning styles and
strategies approach demonstrated salutory effects upon students who might
otherwise decrease their performance in school.
Further replication of this research is needed to verify its utility in
promoting educational success with at-risk students, as well as to
determine the degree to which these effects are retained and generalized
after the student no longer receives such intervention. Also, knowing the
importance of early intervention in the lives of at-risk students, studies
which modify and attempt to implement a similar approach at the elementary
school level could provide educational researchers with even more proactive
interventions which may help to establish effective learning strategies and
perceptions of personal control earlier in the school curriculum.
Correspondence concerning this article should be addressed to Gerald D.
Nunn, Area Education Agency 6, 909 South 12th Street, Marshalltown, Iowa
50158.
Figure 1. Strategies Intervention Model: Learning Strategies Curriculum
Acquisition
e.g. Work Identification
Skills, Paraphrasing, Visual
Storage
e.g. Mnemonic skills,
Paired Associates,
Listening/Notetaking
Motivation
e.g. I-Plan, set goals,
conference, share
strategies
Expression and
Demonstration of
Competence
e.g. Writing Skills,
Test Taking Skills,
Error Monitoring
Adapted from: Deschler, D. & Schumaker, I. An instructional
model for teaching students how to learn. In J.L. Graden,
J.E. Zins, & M.J. Curtis (Eds.). Alternative educational
delivery systems: Enhancing instructional options for all
students. Unpublished manuscript.
Figure 2. Learning Styles and Learning Strategies Intervention Course
Legend for chart:
A - Learning Styles/Strategies
B - Learning Styles/Strategies
C - Outcomes
A
B
C
Conditions
Interests
Expectations
Typology
Attention focusing
Schema builing
Idea elaboration
Practice
Self-monitoring
Conferencing
SIM-Model Focus
Acquisition,
School
Adjustments
My Style?
My Goals
My Progress
Grades
Locus of control
Expression, & Storage of
Knowledge via Strategies
Figure 3. Effects of intervention upon grade point averages.
Legend for chart:
A - Pre GPA
B - Post GPA
A
Group
ARI
ARN
GEC
B
Grade Point Average
1.78
2.09
3.18
1.95
1.8
3.17
Figure 4. Effects of intervention upon locus of control
Legend for chart:
A - Pre LOC
B - Post LOC
A
Group
ARI
ARN
GEC
B
Grade Point Average
16.85
16.14
12.65
12.96
19.79
13.06
References
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~~~~~~~~
By Gerald D. Nunn
Gerald D. Nunn, Area Education Agency 6, Marshalltown, Iowa.
------------------------------------------------------------------------------Copyright of Journal of Instructional Psychology is the property of
Instructional Innovation and its content may not be copied or emailed to
multiple sites or posted to a listserv without the copyright holder's
express written permission. However, users may print, download, or email
articles for individual use.
Source: Journal of Instructional Psychology, Mar95, Vol. 22 Issue 1, p34,
6p, 1 chart, 1 diagram, 2 graphs.
Item Number: 9505042097
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Title: An investigation of students' learning styles in various
disciplines in colleges and universities.
Subject(s): COLLEGE students -- Education -- United States
Source: Journal of Humanistic Counseling, Education & Development, Dec94,
Vol. 33 Issue 2, p65, 10p, 2 charts
Author(s): Matthews, Doris B.
Abstract: Studies the learning style of college majors by the use of the
Canfield model as a discipline. Information on use of other strategies for
selection of students; Examination of individual learning styles by
researchers; Discussion of demographic characteristics provided in Learning
Styles Inventory Manual; Demonstration study which consisted of selection
of 2,332, four year college and university students.
AN: 9708111815
ISSN: 0735-6846
Database: Academic Search Premier
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Section: RESEARCH
AN INVESTIGATION OF STUDENTS' LEARNING STYLES IN VARIOUS DISCIPLINES IN
COLLEGES AND UNIVERSITIES
This study investigates the learning styles of college majors using the
Canfield model. Disciplines differed singificantly regarding style.
Students majoring in mathematics and science fell into the applied
categories more often than those students majoring in humanities, social
science, and education, who fell mainly into conceptual categories. Within
majors, there were also sex and race differences.
The intensifying need for balancing high educational standards and equity
among diverse student populations remains a concrete challenge to educators
in higher education. Sliding enrollments and tight economic constraints
force educators to look for alternatives in the guidance services that they
provide as well as for innovative approaches to classroom instruction
(Claxton & Murrell, 1987; Robbins & Smith, 1993). A traditional approach to
maintaining such high standards has been to guide students to pursue majors
in which they were predicted to be successful based on high school grades
and standardized test scores, as well as other variables (Chartrand, 1991;
Holland, 1985). Although this approach protects the projected academic
standards of the college or university, it does not take into consideration
the possibility that predicted success is as much a matter of inflexible
instructional approaches as it is sets of characteristics and conditions in
the history of students (Bonham, 1989; Grasha, 1984; Lowman, 1993;
Matthews, 1991; Miller, Alway, & McKinley, 1987).
Educators (Banks, 1988; Claxton & Murrell, 1987; Hoyt, 1989) have contended
that many more students may be successful in majors that traditionally
would have excluded them if those students were admitted and the teachers
in those majors provided more flexible, innovative instructional approaches
that more nearly matched learner typologies. Such matching of course
content, assignments, and methods of presentation to specific learning
styles of students, would enhance the achievement and degree of perceived
program satisfaction of students and, therefore, retention in college.
"The term 'learning style' refers to the affective component of the
educational experience which motivates a student to choose, attend to, and
perform well in a course or training exercise" (Canfield, 1988). It is a
personal trait that develops from inherited characteristics, previous
experience, and the demands of the present environment (Kolb, 1981, 1984).
Although widespread agreement supports the notion of the existence of
individual learning styles, learning style researchers often define the
concept differently. Gregorc (1984) emphasized distinctive behaviors and
dualities. Kolb (1984) specified hereditary equipment, past experience, and
the environment. Canfield (1988) discussed conditions, content, modes, and
expectations. A style is a fairly stable, consistent way of learning across
a variety of activities, experiences, and demands of the present
environment (Kolb, 1981, 1984). According to Dunn, DeBello, Brennan,
Krimsky, and Murrain (1981), models have different characteristics, but
tend to overlap in many aspects.
The Canfield model is an instructional preference approach, based on
components of Maslow's hierarchy of needs and McClelland's theory of
achievement motivation (Claxton & Murrell, 1987). This approach served as
the model for this research. Therefore, relevant research discussed in the
following section uses Canfield's Learning Styles Inventory to measure
students' learning style. One of the most popular suggestions to come out
of learning style research is to use information derived from the Learning
Styles Inventory as a tool for faculty in modifying classroom instruction
and conditions to bring instructional style more in line with preferences
of students. According to Holland (1985), people tend to be happy in
occupations and stay in positions longer when job skills match personal
traits. Likewise, proponents of learning style (Claxton & Murrell, 1987)
contended that students are happier and stay in school longer when
instructional requirements match their learning styles.
The practice of matching college teaching conditions to the learning styles
of students, however, is a complicated affair and raises several questions.
Do learning styles of students differ across academic majors? Do men and
women differ in learning styles? Do African Americans and Caucasian
Americans differ in leaming styles?
Research on learning styles of students in various disciplines has been
reported previously in the literature. Canfield (1988) reported significant
differences among groups of students enrolled in various majors in
collegiate settings. Biberman and Buchanan (1986) examined learning styles
within the area of business and found that the styles of majors in
accounting and economics/finance varied from majors in marketing and
management. Wunderlich and Gjerde (1978) studied learning styles and career
choice in medicine and found that no association existed between the two in
the medical field. When nontraditional nursing students (RN baccalaureate)
and traditional learners (basic baccalaureate) were compared, Merritt
(1983) found that the preferences of basic students were significantly
higher than those of RN students on several scales of the learning style
instrument used to assess them. Walker, Merryman, and Staszkiewicz (1984)
found that undergraduate majors in vocational education needed more
external motivation, direction and structure, time limits, and instruction
by the auditory mode than that needed by graduate students. Pettigrew
and Zakrajsek (1984) found that physical education majors preferred
authority, iconics, and direct experience compared to other majors in
education.
In describing demographic characteristics in the Learning Styles Inventory
manual, Canfield (1988) reported that the Learning Styles Inventory has
been normed separately for men and women because they tend to express
substantial differences in preferences on all the scales of his instrument.
For example, in an early study using the Learning Styles Inventory,
Brainard and Oremen (1977) reported significant differences between men and
women within several majors in a community college.
To date, no research using the Learning Styles Inventory has been reported
that compares the learning style of African Americans and Caucasian
Americans in different college disciplines. Furthermore, all previous
studies have compared students on each of the 21 scales independently
rather than using learner typologies or the two major learning style
dimensions. Typologies and the two dimensions to the learning style
instrument are a recent addition to the model (Canfield & Knight, 1983).
Therefore, research comparing differences across college disciplines using
the learning styles or typologies of students derived specifically from the
Canfield Learning Styles Inventory is unavailable in the literature.
The purpose of this study then was to examine the learning styles of
students in various disciplines in several colleges and universities in a
Southern state. The following research questions were addressed:
1. Do students in academic disciplines differ in their style of learning?
2. Do male and female students differ in learning styles within the
disciplines?
3. Do African American and Caucasian American students differ in learning
styles within the disciplines?
METHOD
Sample
The sample for this study consisted of 2,332 students in selected 4-year
colleges and universities in a Southern state. Students came from three
state-supported institutions and two private schools. There were 1,055 men
and 1,277 women. African Americans numbered 1,114 and Caucasian Americans
numbered 1,218. Although there were some younger (673) and older (157)
students, the majority (1,502) were in the age category of 19 to 24. Of
those in the sample, 41% of the students had fathers with a high school
education or less, whereas 59% of the students' fathers had some college or
degrees in higher education. A similar pattern was true for the educational
level of mothers. Of those sampled, 42% of their mothers had a high school
education or less and 58% had degrees in higher education or some college
education. The majority of students came from homes in rural areas (42%) or
small towns (26%) with a population of less than 20,000.
The design used cluster sampling with classes selected from the courses
being offered on the schedule within majors for the spring semester at the
five institutions. The sample represented approximately 9% of the 27,000
students in the institutions.
The number of students in the sample by discipline was 381 (Education), 510
(Mathematics), 450 (Science), 499 (Business), 178 (Humanities), and 314
(Social Science). Table 1 presents the numbers and percentages of students
in each discipline by race and sex.
Instrumentation
Learning Styles Inventory. The Learning Styles Inventory, developed by
Canfield and Knight (1983), is a self-report questionnaire of 30 items that
allows students to describe features of their educational experience that
they most prefer. Each item has four choices that the student ranks on a 4point scale with 1 = most-liked and 4 = least-liked choice.
The choices that students make on the instrument are summed to form 21
scales. The scales include 8 preferred Conditions for Learning (peer,
organization, goal setting, competition, instructor, detail, independence,
and authority); 4 preferred Areas of Interest (numeric, qualitative,
inanimate, and people); 4 preferred Modes of Learning (listening, reading,
iconic, and direct experience); and 5 preferred Expectations for Course
Grades (A, B, C, D, and total expectation).
Learner typologies, or styles, are computed in three steps (Canfield, 1988;
Gruber & Carriuolo, 1991). First, raw scores on the 21 scales of the
Learning Styles Inventory are converted to T scores. Second, by using T
scores from 10 of the scales, a score is computed for each of two continua
or dimensions: the Applied Conceptual continuum and the Independent-Social
continuum. The Applied-Conceptual continuum comprises the horizontal axis
and a score for this continuum is computed by the formula: Total Score =
organization + qualitative + reading direct experience - inanimate iconic. The Independent-Social continuum makes up the vertical axis and its
score is computed by the formula: Total Score = peer + instructor - goal
setting - independence. Third, a specific learner typology is determined by
the intersection of the two continua. That is, by using the two scores
generated on the Applied-Conceptual and Independent-Social dimensions, a
learning style can be located in one of nine categories: social (likes to
learn with people), independent (likes to learn alone), applied (likes to
learn by making theories operational), conceptual (likes to learn with
language-oriented experiences), social/applied (likes to learn with people
using hands-on experiences), social/conceptual (likes to learn with people
using language-oriented experiences), independent/applied (likes to learn
with hands-on experiences alone), independent/conceptual (likes to learn
with language-oriented experiences alone), and neutral preference (no
preference of style).
Canfield (1988) discussed validity and reliability in the manual. The
Learning Styles Inventory has high reliability. A study of internal
consistency using item analysis from a sample of 1,397 college students
produced correlations ranging from .87 to .97. Split-half reliability
results using this same sample produced values ranging from .96 to .99.
The validity of the Learning Styles Inventory has been determined in two
ways. The first is the power of the Learning Styles Inventory to
discriminate meaningful group differences in learning style preferences.
Canfield (1988) reported that "hundreds of administrations of the Learning
Styles Inventory...give solid preliminary evidence that the preferences
discriminated by scales and sets of scales do relate to the academic and
career choices of those tested" (p. 38). The second is whether teaching a
student with techniques that match his or her learning style improves
achievement and satisfaction with learning. Again, Canfield (1988) reported
a variety of studies that supported this assumption.
Student Demographic Questionnaire. The researcher constructed a demographic
questionnaire to obtain pertinent information such as sex, age, race,
educational level of mother and father, and hometown size.
Procedure
During the spring semester of 2 succeeding years, students at the private
and public institutions answered the Student Demographic Questionnaire and
the Learning Styles Inventory. Each institution had a facilitator who
assumed the responsibility for coordinating with faculty and then
administering the instruments to select classes. When the facilitators
completed the administration of instruments, they returned them to the
researcher for scoring and data analysis.
Inasmuch as enrollment numbers in specific majors were small, similar
majors were combined into six academic disciplines for purposes of
analysis. General mathematics, engineering, computer science, and
architecture were combined into a discipline called Mathematics. The
discipline of Science consisted of majors in biology, chemistry, nursing,
pharmacy, forestry, and agricultural science. Majors in economics,
agribusiness, management, banking and finance, marketing, accounting,
office occupations, and home economics made up the discipline of Business.
Humanities was composed of the majors in art, music, English, foreign
language, history, and drama. Education consisted of majors in early
childhood, elementary, secondary, special, and physical education. Majors
in psychology, sociology, social welfare, political science, and criminal
justice were combined into the discipline of Social Science.
Analysis of Data
Selected statistical procedures were used to analyze the data. Percentages
were computed to show the proportion of students in the nine learning
typologies within the disciplines among the six categories. Likewise,
percentages determined the proportion of students in the three categories
along the two continua. The chi-square test was used to determine if
significant differences existed between the proportion of students in the
learner typologies within disciplines for sex and race, as well as among
disciplines.
To explore more fully the differences among disciplines, the researcher
studied categories along each continuum. By using the directions by
Canfield (1988) to determine placement on the two continua, three
categories emerged on each continuum. The two continua categories are:
Applied-Neutral-Conceptual and Independent-Neutral-Social. Total Scores on
each dimension were used to place students into these categories as
follows:
Applied-Neutral-Conceptual continuum
1. Applied: a score of less than -15
2. Neutral: a score from -15 to +15
3. Conceptual: a score greater than +15
Independent-Neutral-Social continuum
1. Independent: a score of less than -10
2. Neutral: a score from - 10 to + 10
3. Social: a score greater than +10.
FINDINGS
The first research question asked if students were different among the six
academic disciplines in terms of nine learner typologies identified by the
Learning Styles Inventory. Table 2 shows the number and percentage of
students in each category of learner typology across disciplines. A visual
inspection of Table 2 shows that differences exist in proportion of
students in the various categories for the disciplines. The chi-square
statistical test showed differences to be significant, X(2) - 190.08, p <.
001.
Another way of examining these differences is to compare the six
disciplines on the two learning style dimensions. The chi-square test
showed that the differences were significant (X(2) 161.41, p <.001) among
the proportion of students in the three categories on the AppliedConceptual continuum across the six disciplines. Students majoring in
mathematics and science tended to fall into the applied category more often
than those majoring in humanities, social science, and education who tended
to fall more frequently into the conceptual category. Differences among the
proportion of students in the three categories on the Independent-Social
continuum were not significant.
The second research question asked if there were differences in style of
learning between men and women within disciplines. There were significant
differences in mathematics (X(2)- 16.25, p < .05), business (X2 -- 29.56, p
< .001); social science (X(2) 16.80, p < .05), and education (X(2)- 16.31,
p < .05). Although, both men and women in mathematics tended to be applied,
women were more independent in their style of learning than were their male
counterparts. In business, both groups tended to be conceptual in style,
but more men fell in the social category than did women. In social science
and education, high proportions of men and women were in the social or
social combination categories, but women tended to select the conceptual
and independent categories more frequently than did men. Young men and
women who selected science or humanities as their area of concentration
differed little in learning style.
The third research question asked if there were race differences in
learning styles within the disciplines. When comparisons were made, African
Americans and Caucasian Americans tended to differ significantly in
mathematics (X(2) 46.01,p < .001) science (X(2)- 21.75, p < .01), business
(X(2) 33.85, p < .001); and social science ( X(2) - 17.57, p < .05). In
mathematics and science, African Americans had more conceptual styles of
learning than did Caucasian Americans who favored the applied styles.
Majors in both races in business and social science preferred social and
conceptual styles, but African Americans had higher percentages in the two
aforementioned categories. There were no significant differences between
the races in humanities or education.
DISCUSSION
Research verified that academic disciplines composed of a variety of
college majors have different proportions of students with markedly
different learning styles. For instance, students with majors in humanities
such as art, music, drama, dance, and English tended to reveal themselves
as conceptual learners. On the other hand, students in mathematics courses
such as computer science, engineering, and mathematics tended to exhibit a
learning style described as more applied than those in the humanities.
Although approximately one quarter to one third of students in every major
tended to fall into the social category on the social to independent
continuum, the majority of students in these majors preferred the
independent style of learning. Men and women tended to differ in their
learning styles within several disciplines. For example, differences
existed in mathematics, business, social science, and education.
Race differences occurred within disciplines, also. African Americans and
Caucasian Americans differed significantly in mathematics, science,
business, and social science.
The findings of this study have several important implications. Differences
in learning styles across academic disciplines suggest that students select
majors that match their learning styles and enhance their perceived
potential for success. A first reaction might be to use this finding to
guide students into majors whose content,
conditions, and instructional approaches match the learning styles of
students. This solution might ameliorate the issue of academic quality,
because students would be expected to have greater success under these
conditions. It would likely increase problems of equity, however, when
educators also examine the differences within certain disciplines among men
and women and African Americans and Caucasian Americans. For example, men
enrolled in mathematics at a rate of almost 21/2 times that of women (32%
vs. 13%). On the other hand, women were enrolled in education at a rate
almost four times that of men (23% vs. 8%).
Important differences existed when comparisons were made of enrollments of
Caucasian Americans and African Americans in various disciplines. Caucasian
Americans were enrolled in mathematics at twice the rate of African
Americans (28% vs. 15%), whereas African Americans were enrolled in
business at more than twice the rate of Caucasian Americans (30% vs. 13%).
If one of the goals of education is to provide greater opportunities to a
wider diversity of students (for example, by getting more women and African
Americans into mathematics-oriented majors and more men into education
majors), then recruitment, selection, and retention approaches using
learning style as one variable would likely be appropriate. Career and
self-development counselors at colleges and universities can effect change
on campuses by using information from the Learning Styles Inventory. They
can help students understand the relationship of individual learning styles
and academic majors in terms of their organization, content, and
instructional approaches. Rather than guiding a student away from a major
if a mismatch is apparent, the counselor simply gives the student an
opportunity to make a more intelligent choice about various courses of
study. If the student chooses a major that is to a degree not congruent
with his or her learning style, the counselor can help the student identify
ways to strengthen those areas in which learning styles information
predicts that a problem might arise.
The counselor can also take a consulting role by facilitating faculty in
modifying their instructional delivery systems to address student
differences in learning styles. Wholesale modifications would not be
necessary because faculty could address the differences in student
preferences by using a variety of presentation formats, assignments, and
student participation approaches. Canfield (1988) provided many practical
suggestions to aid faculty in making these modifications.
Addressing differences in learning styles of students has several
advantages for meeting standards of high quality and equity among students
of diversity. First, if universities and colleges improve the academic
achievement and program satisfaction of students so that graduation rates
increase, then all of higher education will benefit. Second, if these
graduates are distributed across disciplines with more nearly equal
proportions of men and women and majority and minority groups than is the
case today, and if these graduates are better prepared for the demands of
the future world of work, then all of society will benefit. Finally, if
differences in learning and attitudes are addressed in all instructional
programs so that students have greater opportunity to choose appropriately
challenging and rewarding courses of study, every individual will benefit.
REFERENCES
Banks, J. A. (1988). Multi-ethnic education: Theory and practice. Needham
Heights, MD: Allyn & Bacon.
Biberman, G., & Buchanan, J. (1986). Learning style and study skills
differences across business and other academic majors. Journal of Education
for Business, 61, 303-307.
Bonham, L. A. (1989). Using learning style information, too. In E. R. Hayes
(Ed.), Effective teaching styles. San Francisco, CA: Jossey-Bass.
Brainard, S. R, & Ommen, J. L. (1977). Men, women, and learning styles.
Community College Frontiers, 5(3), 32-36.
Canfield, A. A., & Knight, W. (1983). Learning Styles Inventory. Los
Angeles, CA: Western Psychological Services.
Canfield, A. (1988). Learning Styles Inventory Manual. Los Angeles, CA:
Western Psychological Services. Claxton, C., & Murrell, P. H. (1987).
Learning styles: Implications for improving education practices. (ASHE-ER1C
Higher Education Report No. 4). Washington, DC: Association for the Study
of Higher Education.
Chartrand, L M. (1991). The evolution of trait-and-factor career
counseling: A person x environment fit approach. Journal of Counseling &
Development, 69, 518-524.
Dunn, R., DeBello, T., Brennan, P., Krimsky, J., & Murrain, P. (1981).
Learning style researchers define differences differently. Educational
Leadership, 38(5), 372-375.
Grasha, A. F. (1984). Learning styles: The journey from Greenwich
Observatory (1796) to the college classroom (1984). Improving College and
University Teaching, 32(1), 46-53.
Gregorc, A. F. (1984). Style as a symptom: A phenomenological perspective.
Theory into Practice, 23(1), 51-55.
Gruber, C. P., & Carriuolo, N. (1991). Construction and preliminary
validation of a learner typology for the Canfield Learning Style Inventory.
Educational and Psychological Measurement, 51(4), 29-36. Holland, J. L.
(1985). Making vocational choices: A theory of vocational personalities and
work environments. Englewood Cliffs, N J: Prentice-Hall.
Hoyt, K. B. (1989). The career status of women and minority persons: A 20year retrospective. The Career Development Quarterly, 37, 202-212.
Kolb, D. A. (1981). Learning styles and disciplinary differences. In W.
Chickering and Associates (Eds.), The modern American college. San
Francisco, CA: Jossey-Bass.
Kolb, D. A. (1984). Experiential learning. Englewood Cliffs, N J: PrenticeHall.
Lowman, R. (1993). The inter-domain model of career assessment and
counseling. Journal of Counseling & Development, 71, 549-553.
Matthews, D. B. (1991 ). The effects of learning style on grades of firstyear college students. Research in Higher Education, 32(3), 253-267.
Merritt, S. L. (1983). Learning style preferences of baccalaureate nursing
students. Nursing Research, 32(6), 367-372.
Miller, C. D., Alway, M., & McKinley, D. L. (1987). Effects of learning
styles and strategies on academic success. Journal of College Student
Personnel, 28(5), 400-404.
Pettigrew, F, & Zakrajsek, D. (1984). A profile of learning style
preferences among physical education majors. Physical Educator, 4(2), 8589.
Robbins, S. B, & Smith, L. C. (1993). Enhancement programs for entering
university majority and minority freshmen. Journal of Counseling &
Development, 71, 510-514.
Walker, T. L, Merryman, J. E., & Staszkiewicz, M. (1984). Identifying
learning styles to increase cognitive achievement in a vocational teacher
education program. Journal oflndustrial Teacher Education, 22(I), 27-40.
Wunderlich, R., & Gjerde, L. (1978). Another look at learning style
inventory and medical career choice. Journal of Medical Education. 53.4554.
TABLE 1
Number and Percentage of Students in Each Discipline
Race
Caucasian American
Discipline
Mathematics
Science
Business
Humanities
Social Science
Education
African American
Men
N
N
242
102
153
35
95
131
42
38
55
78
29
55
Women
%
%
47
20
34
8
19
26
24
21
17
25
8
14
Men
N
N
100
66
169
93
67
206
46
52
68
113
152
145
Women
%
%
20
13
38
20
14
41
25
30
22
36
40
38
TABLE 2
Number and Percentage of Students in Learner Typologlea by Major
Typology
Social/Conceptual
Social
Social/Applied
Conceptual
Neutral Preference
Applied
Independent/Conceptual
Independent
Independent/Applied
N
49
72
88
27
63
65
51
60
54
Mathematics
%
13.4
22.1
37.1
8.1
24.1
33.0
17.6
26.3
37.0
N
56
63
54
65
44
46
55
49
24
Science
%
15.3
19.3
22.8
19.6
16.9
23.4
19.0
21.5
16.4
Business
Humanities
Typology
Social/Conceptual
Social
Social/Applied
Conceptual
Neutral Preference
Applied
Independent/Conceptual
Independent
Independent/Applied
N
93
77
42
88
50
31
55
43
26
Typology
Social/Conceptual
Social
Social/Applied
Conceptual
Neutral Preference
Applied
Independent/Conceptual
Independent
Independent/Applied
N
63
45
14
57
36
16
46
30
16
%
25.4
23.6
17.7
26.5
19.2
15.7
19.0
18.9
17.8
Social Studies
%
17.2
13.8
5.9
17.2
13.8
8.1
15.9
13.2
11.0
N
41
17
10
42
16
4
33
14
5
Education
N
64
52
29
53
52
35
49
32
21
%
11.2
5.2
4.2
12.7
6.1
2.0
11.4
6.1
3.4
%
17.5
16.0
12.2
16.0
19.9
17.8
17.0
14.0
14.4
REFERENCES
Banks, J. A. (1988). Multi-ethnic education: Theory and practice. Needham
Heights, MD: Allyn & Bacon.
Biberman, G., & Buchanan, J. (1986). Learning style and study skills
differences across business and other academic majors. Journal of Education
for Business, 61, 303-307.
Bonham, L. A. (1989). Using learning style information, too. In E. R. Hayes
(Ed.), Effective teaching styles. San Francisco, CA: Jossey-Bass.
Brainard, S. R., & Oremen, J. L. (1977). Men, women, and learning styles.
Community College Frontiers, 5(3), 32-36.
Canfield, A. A., & Knight, W. (1983). Learning Styles Inventory. Los
Angeles, CA: Western Psychological Services.
Canfield, A. (1988). Learning Styles Inventory Manual. Los Angeles, CA:
Western Psychological Services.
Claxton, C., & Murrell, P. H. (1987). Learning styles: Implications for
improving education practices. (ASHE-ERIC Higher Education Report No. 4).
Washington, DC: Association for the Study of Higher Education.
Chartrand, J. M. (1991). The evolution of trait-and-factor career
counseling: A person x environment fit approach. Journal of Counseling &
Development, 69, 518-524.
Dunn, R., DeBello, T., Brennan, P., Krimsky, J., & Murrain, P. (1981).
Learning style researchers define differences differently. Educational
Leadership, 38(5), 372-375.
Grasha, A. F. (1984). Learning styles: The journey from Greenwich
Observatory (1796) to the college classroom (1984). Improving College and
University Teaching, 32(1), 46-53.
Gregorc, A. F. (1984). Style as a symptom: A phenomenological perspective.
Theory into Practice, 23(1), 51-55.
Gruber, C. P., & Carriuolo, N. (1991). Construction and preliminary
validation of a learner typology for the Canfield Learning Style Inventory.
Educational and Psychological Measurement, 51(4), 29-36. Holland, J. L.
(1985). Making vocational choices: A theory of vocational personalities and
work environments. Englewood Cliffs, N J: Prentice-Hall.
Hoyt, K. B. (1989). The career status of women and minority persons: A 20year retrospective. The Career Development Quarterly, 37, 202-212.
Kolb, D. A. (1981). Learning styles and disciplinary differences. In W.
Chickering and Associates (Eds.), The modern American college. San
Francisco, CA: Jossey-Bass.
Kolb, D. A. (1984). Experiential learning. Englewood Cliffs, N J: PrenticeHall.
Lowman, R. (1993). The inter-domain model of career assessment and
counseling. Journal of Counseling & Development, 71, 549-553.
Matthews, D. B. (1991). The effects of learning style on grades of firstyear college students. Research in Higher Education, 32(3), 253-267.
Merritt, S. L. (1983). Learning style preferences of baccalaureate nursing
students. Nursing Research, 32(6), 367-372.
Miller, C. D., Alway, M., & McKinley, D. L. (1987). Effects of learning
styles and strategies on academic success. Journal of College Student
Personnel, 28(5), 400-404.
Pettigrew, F., & Zakrajsek, D. (1984). A profile of learning style
preferences among physical education majors. Physical Educator, 4(2), 8589.
Robbins, S. B., & Smith, L. C. (1993). Enhancement programs for entering
university majority and minority freshmen. Journal of Counseling &
Development, 71, 510-514.
Walker, T. J., Merryman, J. E., & Staszkiewicz, M. (1984). Identifying
learning styles to increase cognitive achievement in a vocational teacher
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Wunderlich, R., & Gjerde, L. (1978). Another look at learning style
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~~~~~~~~
By Doris B. Matthews
Doris B. Matthews is a professor in the Department of Counselor Education
and Psychological Foundations at South Carolina State University, P. 0. Box
7215, Orangeburg, SC 29117.
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Title: The Naturalistic approach to learning styles.
Subject(s): LEARNING strategies
Source: College Teaching, Summer90, Vol. 38 Issue 3, p106, 8p, 5 charts
Author(s): Grasha, Tony
Abstract: Focuses on the author's research regarding students' learning
styles. How he conducted his research; Observational methods; In-depth
interviews; Analyzing learning projects; Choosing learning experiences;
Using metaphors or `practical poetry.'
AN: 9709144349
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THE NATURALISTIC APPROACH TO LEARNING STYLES
Learning styles are the preferences that students have for thinking,
relating to others, and for various classroom environments and experiences.
A large number of such preferences have been identified (Claxton and
Murrell 1987; Fuhrmann and Grasha 1983; Keefe 1982), and a comprehensive
literature exists on the uses of learning style information for diagnosing
students and designing instructional environments. Much of this work
employs self-report personality tests, opinion and attitude surveys, and
questionnaires designed to elicit preferences students have for particular
instructional procedures.
Over the past eighteen years, I have conducted research on the GrashaRiechmann Student Learning Style Scales, which is a questionnaire designed
to elicit information about student tendencies toward competition,
collaboration, independence, dependence, participation, and avoidance.
About five years ago, I began seriously to question the utility of these
questionnaires and tests. Some of my reasons involved the inadequate
reliability and validity of many instruments, the failure of some authors
to identify clear instructional procedures that would enhance certain
styles, and the relatively small effects in student achievement and
satisfaction that learning style information produced in many studies
(Grasha 1984; Grasha 1989).
A related factor in my growing disenchantment was the comments I would
receive from a minority of participants in my seminar and workshop sessions
on learning styles. In particular, those outside the fields of education
and the social sciences frequently would comment on their discomfort with
such approaches. To quote one participant from the English department of a
four-year college, "I can't relate well to those categories and the numbers
associated with them. They do not describe people as I know them. You are
putting students into little boxes and missing the essential qualities of
what makes someone a dynamic human being. It's too sterile an approach for
me."
My initial reaction was to acknowledge the comment, but after the seminar
or workshop I largely dismissed it. After all, personality profiles were a
legitimate part of my discipline, and if someone could not appreciate them,
so be it. The problem was that every time I heard a similar comment, it
became much more difficult to ignore. "Perhaps they have a point," I began
to say to myself. If what people were telling me was accurate, I was faced
with another dilemma: how to capture the "dynamic quality of what makes a
student a fully functioning human being."
My approach was based on quantitative measures of student learning styles.
My assessment of the situation suggested that to overcome my reservations
about current approaches I had to look for relatively more qualitative
methods for capturing the learning styles of students. Fortunately, I was
able to find some that already existed and was able to devise a couple of
additional procedures for achieving the latter goal.
My search began with the goal of finding procedures for assessing learning
styles that were not grounded in the responses to items on a personality
test. I was interested in approaches to assessing learning styles that were
grounded in the daily experiences of students and labeled such approaches
"naturalistic." I was able to identify four methods that met the latter
criterion. They were direct observations of student behavior, in-depth
interviews with students, the analysis of self-directed learning projects,
and the analysis of the guiding metaphors students used when describing the
teaching-learning process. Each led to valuable insights on the types of
learning styles students employed and to ways instructors could adapt to
them.
Observational Methods
The behaviors of students in a classroom can tell us a considerable amount
about their learning styles. Such observations can be made by participant observers and by using audio or video tape recordings of the interactions
that occur. The content of the observations can be analyzed and the
underlying themes identified. One such approach was taken by Richard Mann
and his colleagues (Mann et al. 1970). They audiotaped the interactions in
classrooms and analyzed the content for the presence of consistent themes
in how students dealt with teachers. A brief description of the learning
styles identified appears in Figure 1.
In examining Mann's learning styles to prepare myself for discussing them
in a series of workshops and seminars, I was struck by the presence of
three learning needs that underlie his typologies: (1) a need for structure
and dependency (e.g., as seen in the compliant and anxious-dependent
clusters), (2) an independent orientation or need to be away from the
influence of others (e.g., as seen in the independent, hero, and
discouraged worker clusters), and (3) a need to have the attention of
others, suggesting somewhat of a cooperative orientation (e.g., as seen in
the attention seekers).
Given the three orientations, it is possible to design course goals that
help students meet their goals. One suggestion I have made in those
seminars and workshop settings is for instructors to select the major goals
that they have for a course. Next, they must think of a way that each goal
could be achieved, employing instructional processes that emphasize either
dependency, independence, or cooperation among peers.
For example, one of my goals in introductory psychology is to have students
describe the three parts of our personalities (i.e., the id, ego, superego)
as developed by Sigmund Freud. They could learn about them through a
lecture (dependent orientation), by going to the library to search for
descriptions of each part in books written about or by Freud (independent
orientation), or by having a small group of peers read about Freud and
share their learnings with one another (cooperative orientation). (During a
term, instructors might alternate how they achieve various goals so that
the three orientations are represented in their teaching styles.)
In-Depth Interviews
Interviews are an excellent way to have students talk about their
experiences as learners. The narratives they generate are a rich source of
information about their attitudes about teaching and learning, the way they
learn, and the preferences they have for instructional techniques. Although
somewhat time-consuming, interviews yield a rich source of qualitative data
about the preferences students have for processing and acquiring new
information. (See Figure 2 for a format for lunchtime interviews.)
William Perry and his colleagues, for example, employed in-depth interviews
to assess the intellectual and ethical development of students (Perry
1970). In the process, they were able to categorize several patterns in the
cognitive components of students' learning styles. Probing interviews asked
questions such as: How has being in college changed the way you think about
yourself or the world? When learning about something you want to know, whom
do you rely upon for information? Can you describe what a really powerful
learning experience you had was like? The latter questions are only samples
of the types of things that are asked. For a more detailed description, see
Perry (1970) and the recent work Woman's Ways of Knowing (Belenky et al.
1986) for more specific examples of interview protocols.
Through interviews, Perry identified patterns in the cognitive components
of students' learning styles and the complexities involved in the way
people think. Perry's work illustrated how individuals moved from a
simplistic, categorical view of the world (e.g., wethey, right-wrong, goodbad) to a realization of the contingent nature of knowledge, relative
values, and the formation of lifetime commitments.
Three styles of thinking in college students emerged from the interviews
and were labeled in order of complexity as dualism (either-or thinking),
multiplicity (acknowledging multiple perspectives), and relativism
(knowledge is situational). Students in the classroom with a dualist
orientation tend to ask questions such as: Why do we have to learn so many
different points of view? Why don't you teach us the right ones? What is
the correct answer? Those who see the world more broadly acknowledge that
there are multiple perspectives, but they lack the capacity to apply
criteria other than personal beliefs to assess alternative points of view.
Students who reason in relative terms recognize that knowledge is
contextual and relative to a certain time and place. They are able to apply
disciplinary-related and other types of criteria to make such judgments.
In terms of teacher-student interactions, such styles have implications for
how information is taught. The majority of college students have a dualist
orientation or weakly developed abilities to assume different perspectives
on issues. Thus, they value concrete, specific, and less abstract
presentations of information. In addition, because faculty tend to think in
multiple and relativist terms, dualist-oriented students do not think the
faculty know very much. After all, how could these instructors know
anything if they are so fuzzy in their thinking and do not have the right
answers?
A challenge for faculty is how to encourage such students to expand their
ability to think in other modes. Exposing them to more abstract conceptual
lectures is unlikely to be effective, although it may make some faculty
members believe they are pursuing a correct course of action. Most students
are likely to become confused and to miss the point of a highly conceptual
argument. Instead, those with a dualist/ orientation need concrete examples
of abstract issues and to be gently challenged to expand their horizons.
They also need a forum to test new ways of thinking in an active manner,
and thus the classroom environment must include much more interaction than
it normally does.
Craig Nelson (1989), a biologist who has spent time integrating Perry's
styles into courses, suggests that instructors take the time gradually to
encourage the development of alternative modes of thinking. The process
involves introducing students to the uncertainties about knowledge in a
discipline and having them actively engage such ambiguities. He suggests
that students need frequent reminders that facts change over time, that
there are different ways to examine an issue, and that biases inherent in
theoretical perspectives color our views of the world. Students must, he
argues, discover such things as they struggle with uncertainties, and
teachers must facilitate this process of discovery.
One of the procedures Nelson suggests is to devote a considerable amount of
classroom time to structured smallgroup discussions designed to encourage
the development of alternative perspectives. One technique is to have
students prepare a worksheet that will form the basis for such small-group
discussions after completing an assigned reading.
The worksheet asks them to do the following: (a) Summarize the author's
overall argument and list each of the major points. (b) List what criteria
should be used to evaluate the adequacy of the arguments made. (c) Evaluate
the overall argument and the major points against the criteria. (d) Decide
whether to accept, reject, or withhold judgment on the adequacy of each
major point and the overall argument.
Small groups of five-to-seven students use the worksheet to structure their
discussions. About 50-60 percent of a class session is devoted to such
group interactions. The remainder of the time is used to clarify points, to
expand the students' analyses, and to present new information about a
topic. Evaluations of discussion assignments cover preparation and
participation, thus emphasizing participation and understanding.
Nelson argues that it is possible to encourage students to think in other
than a "black-and-white" fashion and to go beyond their tendency to use
personal opinion as evidence to support an argument. Research using similar
schemes to apply Perry's model in classroom settings generally supports
such thinking. In a review of the literature, King (1978) concludes that
the complexity of students' thinking can be affected by discussion and
problem-solving procedures that encourage active learning.
Analyzing Learning Projects
In his book, The Adult's Learning Projects, Allen Tough (1979) identified
the activities adults engaged in when they needed to learn something. The
types of learning projects might include learning how to run a computer,
fixing a leaky faucet, learning to play the piano, tennis, or golf, or
acquiring information about interior design. Tough's interest was in the
types of things adults wanted to learn and in the processes that they used
to acquire information and skills.
Based on interviews, Tough suggested that adults were more self-directed as
learners than teachers gave them credit for being. Also, personal
recognition and satisfaction were important motivators of that learning. He
argued that teachers of adult learners needed to take such tendencies into
account.
I was intrigued by his findings for two reasons. One was my suspicion that
they also were applicable to younger students. The other was that examining
how people designed learning projects would be a useful way to develop
information about their learning styles. The data could provide the basis
for classroom procedures.
I decided to test whether an analysis of learning projects could yield
information about learning styles. To do so, I developed a ninety-item
Learning Projects Checklist based in part on a shorter checklist employed
by Ann Davis Toppins (1987). A sample of fifty freshmen from a large
introductory psychology class was recruited to complete the checklist.
To begin, participants first listed and described three important skills or
domains of knowledge that they had learned on their own. They then checked
whether certain aspects of how people learn were a component of their own
learning processes. After completing the checklist, each wrote a narrative
describing themselves as learners based on information in the checklist. A
summary of the data from this study appears in Table 1.
The self-directed nature of the learning styles of college freshmen is
quite clear as well as the tendency to do other than take courses as the
primary way to learn. Their learning was primarily self-directed, related
to personal growth, and motivated by enhancing personal growth, satisfying
their curiosity and interest, and their desires to be successful. The
primary rewards were not grades but self-satisfaction and recognition by
others.
Asking students to develop a narrative description of the information in
the checklist also provides a summary of how individual students see
themselves as self-directed learners. Such narratives, as the sample shown
at the bottom of Table 1 illustrates, provide a more qualitative
description of their learning styles and can quickly give an instructor an
overview of how one or more students in a course prefer to learn.
In general, the information in Table 1 was in line with Allen Tough's
findings with adult learners and with data contained in a recent report by
Ann Davis Toppins (1987) on the implications of learning projects for
teaching graduate students. It was also compatible with a sample of older
undergraduate students (juniors, seniors, and adult students enrolled in
continuing education classes). It is safe to conclude that the preferences
for self-directed learning are not just a characteristic of "adult
learners" and are not only useful in the context of "adult education." I
believe that instructors teaching younger students also must consider such
tendencies in designing instructional processes.
An important challenge is how to allow students in the context of
traditional settings to select learning experiences for themselves and thus
allow them to meet their needs for curiosity, personal growth, and
recognition. I have tried to do this in several ways.
Choosing Learning Experiences
One is the initiation of personal growth contracts for graduate students in
my department's Social Psychology Graduate Program. Each graduate student
plans his or her course work and other learning experiences with a
committee of two faculty members and two advanced students. The written
contracts that result are re-viewed and modified regularly as needed. This
insures that students meet departmental requirements, but the contracts
also allow for students to design a broad range of educational experiences
beyond traditional course work (e.g., internships, off-site workshops,
consultantships, research projects, and keeping journals on significant
learnings during the year).
In my courses, I now make one-third of a student's course grade depend on a
learning project related broadly to course content that he or she designs.
For example, in my undergraduate applied psychology class, students must
find a social problem and work on solving it. The current academic year
projects found students setting up car pools for peers who live off campus,
helping international students become integrated into campus life, and
organizing a campaign to protest perceived inequities in student loan
procedures. The students contract with me in writing for the type of
project that they want to pursue, what principles of psychology will be
employed, and how they will evaluate their efforts.
In my graduate seminar on teaching processes, students must identify an
area of teaching that they want to explore in detail and contract for a
broad range of learning opportunities that will help them achieve their
goals. Outcomes of this learning are presented to the class in the form of
case studies, discussions that employ audio-visual materials, and a variety
of non-traditional discussion procedures. The mode of presenting is
something students also select, which has the added benefit of helping them
learn alternative methods for sharing information with others.
Using Metaphors or "Practical Poetry"
One of the essential differences between creative problem solvers and those
who are less creative is the use of metaphor (Grouchy 1987a, 1987b). Using
metaphors to describe a problem, creative thinkers are able to identify the
elements of a problem and to work on them in ways that are efficient,
unusual, and appropriate for the task at hand. Indeed, many discoveries in
science, technology, and other areas of daily life began with generating a
metaphor.
Albert Einstein, for example, used to remark that before he could develop
theoretical equations for his theory of relativity, he had to have a visual
image of the concept. Thus, his imagination had him riding beams of light
surveying objects below him and speculating on how observers from other
vantage points might view the same event. Sonar was developed during World
War II when naval researchers realized that a ship on the surface was
vulnerable to submarine attacks because it was "blind as a bat." Bats, of
course, locate their prey with sound waves, and thus the use of sound waves
as an object-detection device began.
In sports, swimmers' times began to drop dramatically twenty years ago.
Then the primary metaphor was viewing a swimmer's movements like an oar
pushing and pulling boat. The change involved conceptualizing a swimmer as
an airplane with propellers that move it through the air. Thus, strokes
became much more graceful and involved underwater hand movements that to
some extent mimic the motions of a propeller.
In general, metaphors tend to organize our thoughts and provide directions
for our actions in a variety of settings (Lakoff and Johnson 1976). I have
labeled such metaphors "guiding metaphors," and they play an important role
in understanding why certain teaching-learning processes are employed
(Grasha 1987a).
In classroom settings, faculty members often employ three metaphors to
describe the teaching-learning process (Pollio 1986). They are the
container model, "teaching fills student with knowledge"; the journey-guide
model, "faculty lead students on a journey through their courses"; and the
master-disciple model, "the master drills students in relevant skills and
they become willing apprentices." Each has advantages and disadvantages,
but when employed they provide a certain amount of direction and purpose to
the process, whether or not the outcomes are always desirable. (See Gregory
11987] and Kloss [1987] for specific examples of the implications of common
metaphors for teaching and learning.)
Students also have metaphors for how they perceive the teaching-learning
process and their roles in it. I have found that students are often very
articulate about their metaphors. In a recent study to test the generality
of a metaphor-generating process to assess curriculum and organizational
issues, students listed their metaphors and their implications for
teaching-learning. They did this in four stages.
First, eighty undergraduate students were randomly divided into two groups
of forty each and asked to think of a recent course that was judged to be
effective or ineffective. Second, each group was then asked to list the
words, images, and feelings that they had about their effective or
ineffective class. Some people have a difficult time generating metaphors,
and making a list helps them to organize their thinking. Third,
participants then developed a "guiding metaphor" that would summarize the
words, images, and feelings that they had generated. Fourth, students
listed specific classroom procedures associated with their guiding
metaphors. Finally, they were instructed to list changes in the words,
images, feelings, and guiding metaphors that they would like to see made in
their courses and the instructional implications of those changes.
In the latter stage, students revealed through metaphors what their needs
and preferences were as learners. In the process, they were stating
something about their learning styles. A thematic analysis of these words,
images, feelings, and guiding metaphors provided a qualitative summary of
the student learning styles and needs as expressed through figurative
language. A description of the results of this process for twenty students
selected from a sample of eighty individuals who participated in the
initial study appears in Figures 3 and 4. (See pages 112-13.)
The metaphor process described in Figures 3 and 4 can be adapted for
assessing the learning styles of students enrolled in a particular class. I
have used the process as a course evaluation device and as a tool to
examine the learning styles that my teaching methods generated. To do this,
students were asked to use my class as a frame of reference. The effective
and ineffective class instruction segment was not employed, but all other
features of the process were used.
Both sets of instructions provide valuable insights into students'
perceptions of the teaching-learning process as expressed through
figurative language. They also provide instructors with concrete
information about specific classroom procedures that students prefer and
those that match their learning styles. This is something that traditional
approaches to learning styles do not do directly. Users of such instruments
must often make educated guesses about what students would like. The
metaphor process outlined in Figures 3 and 4 allows students directly to
express their preferences for particular classroom experiences that are in
line with their guiding metaphors.
Thus far, my examination of naturalistic approaches to learning styles
suggests several things about their use in instructional processes.
Although the data are much more qualitative in nature than traditional
approaches, they provide useful insights into the thoughts and behaviors of
students. Indeed, the descriptions that they provide are much richer and
suggest a depth to student behavior that is largely absent from traditional
measurement techniques.
Just as there are different learning styles, I suspect that there are also
different preferences for how to measure them. At a recent workshop where
both approaches were presented, I asked participants to indicate which one
they most preferred. Most liked the naturalistic approaches, but 40 percent
were inclined to stick with traditional methods. "I'm more comfortable with
the objectivity they provide," one participant noted.
The underlying factor that explains such preferences may be whether or not
one tends to be a right- or left-brain thinker. Naturalistic approaches
tend to rely on making qualitative and somewhat intuitive judgments and do
not necessarily rely as much on what have been described as left-brain
capabilities, that is, the use of orderly and logical thought processes.
The issue is not which side of the brain or what approach to learning
styles is better. Both cerebral hemispheres are needed for people to
function, and both quantitative and qualitative assessment procedures
provide information about students that teachers sensitive to students'
needs cannot afford to ignore.
Figure 1. Learning Styles Identified by Richard Mann
Style
Description
Compliant
Typical student of the traditional classroom.
Conventional, trusting to authorities,
willing to go along with what the teacher
wants. Focuses on understanding material
rather than criticizing it or formulating
own ideas. Self-image is not well fined.
Anxiousdependent
Concerned about what authorities think of
them. Low self-esteem and doubtful of own
intellectual abilities and competence. Anxious
about exams and grades. Class comments and
hesitant and tentative.
Discouraged
worker
Intellectually involved but chronically
depressed and personally distant. Afraid
destructive impulses will lead to hurting
others.
Independent
Self-confident, interested, involved, tend to
identify with teacher and see teacher as a
colleague. Have a firmer self-image than
students in the above three clusters.
Hero
Intelligent, creative, involved, introspective,
struggling to establish identity, and
rebellious. Ambivalent toward teacher, erratic
in performance.
Attention
seeker
Possesses a social more than an intellectual
orientation. Wants to be liked, to please others
to get good grades. Both self-esteem and control
depend upon periodic reinforcement from others.
Silent student
Speaks in class only when sure teachers will
approve. Feels helpless, vulnerable, threatened
in relation to teacher, fears engulfment by
instructor but longs helplessly for teacher's
attention.
Sniper
Rebellious but more defensive and less creative
than the hero. Low self-esteem, afraid of
introspection, attracted to authoritarian class
structure. Uninvolved and indifferent toward
class; stresses fact that they were required to
take course. In class, tends to lash out and
then quickly to withdraw.
Figure 2. Interview a Student Over Lunch
Develop a set of
Use a combination of open- and closed-ended
question
questions. Be sure to probe for additional
information when students respond. One of
my favorite questions is, "What is the most
memorable learning experience you have had
and why?"
Answer your
questions in
writing first.
This is a good check on the clarity of
questions and allows you to determine if
any personal biases are likely to be present
when you subsequently interview students.
Identify personal
biases in your
answers and try
to keep them
under control
when interviewing
the students.
For example, if your responses emphasize the
lecture method, then you risk leading the
interview in that direction.
Take good notes
on each lunchtime session.
Jot down highlights during lunchtime and
after returning to your office, fill in the
details.
Read the notes from
each interview and
identify important
themes.
Bundle themes.
Collect themes across your lunchtime
interviews and put similar themes together.
Develop a
narrative based
on the themes
identified.
Include in your narrative student learning
style themes and the instructional
implications of what students are saying.
Share your
narrative with
participants.
Ask them to check it for accuracy and to add
issues they think were left out and/or that
should be added. Such responses should be in
writing.
Revise the
narrative
Use the information students provide to make
corrections to your summary of the
interviews.
Develop classroom
methods and
processes based
on your interview
data.
Be sure to let students know what conclusions
you have reached and what you plan to do
with the information you now have.
Figure 3. Metaphor Evaluation of Effective and Ineffective Courses
Ineffective classes
Effective classes
Words
Repetitive, uninformative, did not meet
expectations, boring,
unintelligible, onesided, confusing,
challenging, hard to
follow
Meaty, survival,
innovative, aggressive,
creative, experimental,
exciting, demanding,
challenging, interesting
informative, different,
integrated, complete
Images
Mafia, hookers, big
business, death,
skeletons, living in a
foreign country, watching
movie without sound,
intimidated audience,
jail
Basic training, wide-eyed
child, small friendly
groups, dynamic
interaction among
people, summer island,
actor on stage
Feelings
Bored, frustrated, lazy,
confused, angry, wasteful,
stupid, stressful, sad
Exhausted, anxious,
stressed, relaxed, happy
excited, thoughtful,
surprised, confident,
expectant, hurried
Guiding
metaphors
A bike without wheels;
train on a circular track
going nowhere; foreign
movie without the
subtitles and the
audience can't leave
the theater because
the doors are locked;
Adolph Hitler talking
and followers afraid
to ask questions
Basic training survival
course, point at which
three streams form one
one big river; survival
trip into wilderness on
foot; travelers taking a
pleasant trip back to
place where they were
born; explorers in a new
land
Classroom
procedures
Lecture dwells on
unimportant points;
rambling style of
presenting; talking over
my head; lecture without
ever asking questions;
student told to just
memorize material;
lectured with back class;
belittles student
questions
Class projects were on
real problems; course drew
from many previous
courses, reviewed
information; instructor
and class worked as a
team; lectures made
material relevant to
local issues; lots of
appropriate visual aids
The figure sumamarizes the words, images, feelings, and guiding
metaphors students used to described courses they considered
effective (i.e., enjoyed the course, got a lot out of it,
considered the teaching processes used above average) and those
that were ineffective (i.e., did not enjoy the course, were not
satisfied with their learning, and considered the teaching
processes used below average).
Figure 4. Metaphor Enhancement of Effective and Ineffective Courses
Suggested changes for
Suggested changes for
ineffective classes
effective classes
Additional
words
Enlightening, challenging,
exciting, real life
Supportive, more
information, emphaty
examples, diversity of
information, participation,
control, understanding,
knowledgeable
with students, more
student participation
Additional
images
Explorer discovering new
lands; youths gathered
around a wise person;
relaxed audience and
teacher; peace on earth
Survival; demigod
small groups around
table at a perfect
"happy hour"; students
have a bigger part in
moving pieces of
puzzle
Additional
feelings
Enriched, interesting,
motivating, enjoyable,
relaxed
Success, pressured
Changes in
guiding
metaphors
A downhill racer; a wise
person showing class how a
puzzle comes together;
traveling through a well-lit
tunnel; train heading toward
a specific destination
Basic training
survival course with
an end in sight; place
where three streams
one large, fast-moving
river
Changes in
classroom
procedures
Instructors insures that
students understand;
instructor shows concerns
for student needs; teacher
solicits questions and
explains concepts clearly;
instructor uses personal
experiences to make points
More empathy on
instructor's part for
students struggling to
understand difficult
material; more time to
learn information;
more discussion of
cause-effect
relationships; quizzes
would be more
difficult
Summary
statement
of learning
needs and
styles
In sum, students want clear
structure and goals;
material that is presented
in an exciting manner that
helps them to piece together
the divergent and sometimes
contradictory data in a
field; students are
personally challenged
through questions and
other activities
In sum, pace of
instruction is
increased and
information is made
more challenging;
instructor takes time
to insure that
students are keeping
up; small group
processes used for
discussion of
information
The figure sumamarizes the words, images, feelings, and guiding
metaphors students used to described courses they considered
effective (i.e., enjoyed the course, got a lot out of it,
considered the teaching processes used above average) and those
that were ineffective (i.e., did not enjoy the course, were not
satisfied with their learning, and considered the teaching
processes used below average). The metaphors they use for what
they want in their courses have important implications for the
students' learning preferences and for their learning styles.
Table 1.--Naturalistic Styles Checklist: Summary of Data for Learning
Projects
Number of times
checked
Information about learning
Category of learning
New skills/enhanced existing skills
188
Gained new knowledge/insight
133
Attitude change
83
Emotional change
58
Hours spent
More than 100 hours
59
Between 51 and 99 hours
34
Between 8 and 25 hours
29
Between 26 and 50 hours
27
Learning processes employed
Learning by doing
150
Observing a model
104
Asking a friend
95
Practice of physical skills
94
Learning related to
Leisure/social life
137
Personal growth
130
Work
59
Motivated by
Personal growth/status
200
Curiosity/interest/novelty
194
Desire to be successful
96
Problem to solve
76
Planned by
Self
131
Instructor/resource person
71
Friend/family member
68
Written instructions
33
Rewarded with
Self-satisfaction
169
Recognition by others
90
Grade/degree credit
52
Promotion/status
30
Risk required
Physical
84
Emotional
79
Psychological
76
Financial
42
Cognitive processes used
Thinking logically/rationally
107
Analyzing information
99
Using rules to guide thinking
89
Forming principles
80
Ways of relating to others
Participating/cooperating
186
Acting independent
99
Being friendly
94
Competing
72
Note: Responses from a sample of fifty University of Cincinnati
freshmen who were enrolled in a 180-student section of
introductory psychology. The students who participated were from
the Colleges of Arts and Sciences, Business Administration,
Engineering, and the College Conservatory of Music. Each
completed a ninety-item learning project checklist divided into
ten categories for each of three learning projects they had
completed over the past three years. The categories and the top
four items checked within each category are presented. The
numbers represent the total number of times that item was checked
across the three learning projects. A copy of the checklist is
available from the author.
Sample of self-descriptions: Individuals completing the checklist
were asked to write a brief narrative to describe themselves. The
statement below is how one of the participants completing the
checklist described himself. Participants were asked to review
their responses, to look for areas where they showed preferences
(i.e., checking an item for at least two of the three learning
projects they listed).
Learner 1: I like to acquire new skills and improve existing
ones. I readily ask friends for help, I read books for needed
information, and actively practice the things I am trying to
learn. I am motivated by curiosity and interest. My activities
are planned by myself, and the major reward is self-satisfaction.
I am most at risk in the emotional sphere.
REFERENCES
Belenky, M. F., B. M. Clinchy, N. R. Goldberger, and J. M. Tarule. 1986.
Women's ways of knowing. New York: Basic Books.
Claxton, C. S. and P. H. Murrell. 1987. Learning styles: Implications for
improving educational practices. ASHE-ERIC Education Report, College
Station, Texas.
Fuhrmann, B.S. and A. F. Grasha. 1983. A practical handbook for college
teachers. Boston: Little-Brown.
Grasha, A. F. 1984. The journey from Greenwich Observatory (1796) to the
college classroom (1984). Improving College and University Teaching
32(1):46-53.
Grasha, A. F. 1987a. The WIF metaphor generation process in curriculum
evaluation. Research Report, University of Cincinnati, Cincinnati, Ohio,
Institute for Consultation and Training.
Grasha, A. F. 1987b. Practical applications of psychology. Boston: LittleBrown.
Grasha, A. F. 1989. Learning styles: Implications for improving educational
practices. Teaching Sociology. In press.
Gregory, M. 1987. If education is a feast, why do we restrict the menu?
College Teaching 35 (Summer):101-05.
Keefe, J. 1982. Student learning styles and brain behavior. Reston,
Virginia: NASSP.
King, P. 1978. William Perry's theory of intellectual and ethical
development. New Directions for Student Services, 35-41. San Francisco:
Jossey-Bass.
Kloss, R. J. 1987. Coaching and playing right field: Trying on metaphors
for teaching. College Teaching 35 (Fall):134-39.
Lakoff, G. and M. Johnson. 1980. Metaphors we live by. Chicago: University
of Chicago Press.
Mann, R. D., B. E. Ringwald, S. Arnold, J. Binder, S. Cytrynbaum, and J. W.
Rosenwein. 1970. Conflict and style in the college classroom. New York:
Wiley.
Nelson, C. E. 1989. Skewered on the unicorn's horn: The illusion of a
tragic tradeoff between content and critical thinking in the teaching of
science. In Enhancing critical thinking in the sciences, edited by L. Crow.
Washington, D.C.: Society of College Science Teachers.
Perry, W. G., Jr. 1970. Forms of intellectual and ethical development in
the college years. New York: Holt, Rinehart and Winston.
Pollio, H. 1986. Practical poetry: Metaphoric thinking in science, art,
literature and nearly everywhere else. Teaching/ Learning Issues (Fall)
Knoxville, Tenn.: University of Tennessee Learning Research Center.
Toppins, A. D. 1987. Teaching students to teach themselves. College
Teaching 35 (Summer):95-99.
Tough, A. 1979. The adult's learning projects. Austin, Texas: Learning
Concepts.
~~~~~~~~
By Tony Grasha
Tony Grasha, an executive editor of College Teaching, is a professor of
psychology at the University of Cincinnati.
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Title:
Learning styles of the multiculturally diverse.
Subject(s):
LEARNING -- Cross-cultural studies
Source:
Emergency Librarian, Mar/Apr93, Vol. 20 Issue 4, p24, 9p, 7
charts, 6 graphs, 2bw
Author(s):
Dunn, Rita
Abstract:
Identifies specific learning styles common among various
cultures using the Learning Styles Inventory. Academic
learning styles of students from different cultures; Synthesis
of the findings of multicultural research with the
learning style inventory; Learning styles by performance groups
and sex; International study of the learning,
processing and leisure-time styles.
AN:
9706113892
ISSN:
0315-8888
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[Go To Citation]
LEARNING STYLES OF THE MULTICULTURALLY DIVERSE
Many researchers have analyzed how culturally different students begin to
concentrate, process and retain new and difficult
academic information -- their "learning style." They have reported that, in
all groups, gifted, average and high achievers' styles tend
to differ significantly[*]. In addition, although each of the ethnic and
racial groups involved in the studies included individuals with
widely diversified styles, certain populations included statistically more
individuals with clusters of specific learning style
characteristics than others (Dunn & Griggs, 1990).
Regardless of those revealed differences, within most families the learning
styles of spouses, their offspring and siblings tend to
differ dramatically. Furthermore, when individuals are taught with either
instructional resources or strategies that complement their
learning styles, their scores on standardized achievement and attitude
tests increase significantly and are accompanied by a
decreased number of discipline referrals. Indeed, the California
Achievement Test (CAT) reading and math scores, of essentially
low socioeconomic, elementary school students in the Brightwood Elementary
School, Greensboro, North Carolina, rose from the
30th percentile in 1986 to the 83rd percentile in 1988; they leveled off at
the 89th percentile in 1989. Indeed, Brightwood's students
have consistently shown 15% to 20% improvement above their own previous
test scores every year since the school began working
with learning styles. By 1989, its black students were 21% above the system
and the North Carolina state average! The only
change made for those children during that three to five year period was
the introduction of learning styles-based instruction
(Andrews, 1990, 1991).
Instruments Used to Identify Learning Style
Dunn, Dunn, & Price's Learning Style Inventory (LSI) and Productivity
Environmental Preference Survey (PEPS) were used to
identify students' learning styles in research conducted at more than 70
colleges and universities in the United States and abroad.
The Ohio State University's National Center for Research in Vocational
Education published the results of its two-year study of
instruments and reported that the LSI had "impressive reliability and face
and construct validity" (Kirby, 1979). Since 1979, the LSI
evidenced consistently high predictive validity (Dunn, 1990a). In a
comparative analysis of the conceptualizations of learning style
and the psychometric standards of nine different instruments that
purportedly measure learning style preference, only the LSI was
rated as having good or very good reliability and validity. Of the 18
instruments reviewed, including an additional nine concerned with
information processing, the LSI was one of only three with good or better
reliability (Curry, 1987). Perhaps because of that, Keefe
(1982) found that it "is the most widely used instrument in elementary and
secondary schools" (p. 52).
The LSI and PEPS define learning style in terms of individual student
reactions to 22 elements:
(a) the immediate instructional environment (Sound. Light, Temperature,
Seating Design);
(b) each person's emotionality (Motivation, Persistence, Responsibility
[conformity/nonconformity], Structure [internal/external]);
(c) social preferences (Learning Alone, in a Pair, with Peers, in a small
Team, with an Adult):
(d) physiological uniqueness (Perceptual preferences--auditory, visual,
tactual, kinesthetic); Intake (eating, drinking, chewing, biting);
Time-of-day energy highs and lows; Mobility versus Passivity needs (see
Figure 1). Processing inclinations are suggested by
correlations with Sound, Light, Design, Persistence and Intake (Dunn,
Bruno, Sklar, Zenhausern, & Beaudry, 1990; Dunn,
Cavanaugh, Eberte, & Zenhausern, 1982).
Synthesis of the Findings of Multicultural Research with the Learning style
inventory
The instruments used to identify learning styles in at least 15 independent
studies were either the LSI for students in grades 3-12 or
the PEPS for post high school adults. Subjects ranged from children to
adults in rural, urban and suburban areas of the United States
and foreign countries and were of low, middle, or high socioeconomic
status. The cultural groups represented within the United
States were gifted, average and underachieving black[**], white, Chinese,
Greek, Mexican and the general population of students as
a whole. The groups outside the United States were Chinese from Singapore,
Bahamians, Brazilians, Canadians, Cree Indians from
Manitoba, Israelis, Jamaicans, Latinos and Mayans from Guatemala, Koreans,
and the Philippines (see Table 1). Many populations
included statistically more or fewer individuals with specific
environmental, physiological, or social preferences than others (Dunn &
Griggs, 1990). In addition, certain ethnic or racial groups revealed
statistically different characteristics that warrant further analysis.
For example, Jalali (1989) reported on a population of 300 students from
each of four backgrounds -- Afro-[**], Chinese-, and
Greek-Americans from New York and Mexican-Americans from a predominantly
rural public school in La Joya, Texas. All were
fourth, fifth and sixth graders, and all but the Afro-Americans spoke an
other-than-English primary language at home. A series of six
graphs and associated tables of means compared each ethnic group. The score
on a particular element represents the mean
deviation of a group mean from the mean of all groups on that element. The
LSI original scores also were analyzed statistically by
means of Completely Randomized Analyses of Variance across the four
cultural groups on each of the LSI 22 elements of learning
style.
We cannot be certain of how to interpret these findings, but the mean
scores on the LSI elements for Afro-American and
Chinese-American children are presented in Table 2 (Dunn, Gemake, Jalali,
Zenhausern, Quinn, & Spiridakis, 1990). The two
groups differed significantly on 15 of the 22 LSI scales. The pattern of
these differences can be seen in Figure 2 where the profiles
of the two groups are almost perfect mirror images in terms of both
direction and extent! Note that, of this population of black and
oriental youngsters, almost three-fourths of one group's style is different
from the other's. Had these children been classmates, they
would have learned in diametrically opposite ways. What might have "worked"
with many in one group, would not have been
effective with many in the other.
For example, as a group, the Chinese-Americans were more alert in the
morning-- which was the worst time of day for the
Afro-Americans, who experienced their highest energy levels in the
afternoon. Afro-American children were more nonconforming
than the Chinese and, thus, required:
(a) an explanation of why what they were required to learn was important to
their teacher,
(b) being spoken to collegially rather than authoritatively; and
(c) instructional options. The Chinese-American youngsters required quiet
and a formal design while learning whereas many
Afro-American students preferred sound (music) and informal seating while
learning.
Temperature differences between the two groups were extreme, with AfroAmerican children requiring more warmth for comfort
than the Chinese (see Figure 2).
The Chinese-Americans were more able to undertake assignments
independently; Afro-Americans worked more effectively with
peers than by themselves and strongly preferred routines and patterns to a
variety of instructional approaches (which the orientals
preferred)! One group learned best by listening (auditorially) and the
other by experiencing (kinesthetically). Both groups could be
taught exactly the same thing, but the media (perceptual resource), length
and type of task, and environment needed to be different
for each. A word of caution: all blacks and all Chinese do not have the
same learning style. What this graph indicates is that, of this
sample of children, many in one group learned differently from many in the
other group. However, individuals in both groups learned
in the other group's majority style.
Consider the differences between the Afro-American and Chinese-American
youngsters and the similar--but not quite so extreme -differences between the mean scores on the LSI elements for the AfroAmerican and Mexican-American children, which are
presented in Table 3, and the comparison of their profiles in Figure 3. For
12 of the 22 elements, the differences between the two
groups reached significance. The graphic representation of the learning
style profiles showed the same mirror-image pattern noted
for the Chinese-American and Afro-American children, although the effect
was not as strong.
Comparisons of Afro-Americans and Greek-Americans in Table 4 and Figure 4
show that these two groups differed significantly on
only nine of the 22 subscales, and the profiles show more similarities than
differences. However, the statistical and graphic
comparisons of Chinese-American and Greek-American children in Table 5 and
Figure 5 reveal 13 significant differences between
those two groups on the 22 LSI elements. The comparison of these two
profiles indicated another mirror-image pattern quite similar
to the one between Chinese-American and Afro-American youngsters.
Given the differences between the groups depicted in Tables 2 through 4, it
is interesting to conjecture about the statistical
comparisons between the Chinese-Americans and the Mexican-Americans. Table
6 indicates that only nine of the 22 LSI elements
were significantly different between the two groups. Indeed, the comparison
of profiles shown in Figure 6 reveals more of a parallel
than a mirror-image pattern. On the other hand, examination of Table 7
shows that the Greek-American and Mexican-American
children had the fewest number of significant differences -- only six of
the possible 22. Graphic representations of the LSI profiles
shown in Figure 7 reveal a clear mirrorimage pattern.
It is easy to identify how children from different ethnic groups differ,
but what does it mean? Many of these same differences do, but
many others do not, persist in each area of the United States. Apparently
differences are also influenced by urban, rural and
suburban living (Ramirez, 1982: Tappenden, 1983), processing style (Cody,
1983; Dunn, Cavanaugh, Eberle, & Zenhausern, 1982;
Dunn, Bruno, Sklar, Zenhausern, & Beaudry., 1990: Dunn & Price, inpress)
and sex (Dunn & Price, in press; Lam-Phoon, 1986;
Mariash, 1983).
On the other hand, dear similarities exist among these students across the
board -- similarities that question conventional school
practices. For example, with the sole exception of Chinese and ChineseAmerican students, late morning and afternoon is a better
learning time than early morning for more than 70% of elementary school
students and 47% of secondary students, but it is an even
more effective concentration period for Mexican-Americans than for students
in the general population of the United States. (13%
of secondary United States students are "night owls"!)
Females of all groups tend to stay with a task to completion (persistent)
more and more often than males. Males are more in need of
an informal seating arrangement than females and are far more likely to
appear "hyperactive" because of their high tactual,
kinesthetic and mobility needs. Those data are consistent with Branton's
(1966) findings that when a person is seated on a wooden,
steel, or plastic chair, approximately 75% of the total body weight is
supported on only four square inches of bone. The resulting
stress on the tissues of the buttocks often causes fatigue, discomfort and
the need for frequent postural change. Boys of every
cultural group are less able to sit still than girls; they also are less
well padded exactly where they need to be to permit comfortable
sitting at conventional desks, corroborating Restak's (1979) and Thies'
(1979) conclusions concerning the biological basis of learning
style.
Children of all cultural groups tend to be more motivated than their
teachers might suspect; they merely cannot achieve when they
are taught through strategies disparate with how they learn. Indeed,
Mexican-Americans appeared more motivated to learn than the
general population in the United States. Of all groups, these boys were the
most authority-and parent-oriented and required frequent
encouragement from their teachers. Direct parent involvement would be an
important component of the education of
Mexican-American males (Dunn & Price, in press). And, just as in the Dunn,
Gemake, Jalali, Zenhausern, Quinn, & Spiridakis
(1989) study, many Mexican-American students were far more peer-oriented
than the general population. Those peer-oriented
youngsters would be more likely to achieve well through small-group
techniques like Team Learning, Circle of Knowledge and
Cooperative Learning than with independent studies or Contract Activity
Packages.
Learning Styles of Low and High Performance Groups
Underachieving children of all cultural groups have certain learning style
characteristics in common: they enjoy a variety of
alternative instructional strategies rather than routines and patterns,
much prefer learning with a hands-on, experiential approach
than by listening to lectures or reading, have a short attention span,
appear to be hyperactive and in need of mobility, and often are
either teacher or peer motivated. They are rarely self- or authorityoriented; instead they prefer collegial adults. High achievers of all
cultural groups often are self- and authority-motivated, although Korean
(Suh & Price, in press) and Filipino (Ingham & Price, in
press) adolescents were highly self- and parent-motivated.
Among children from diverse cultural groups, high achievers remain on-task,
do what they are told, sit in their seats without much
hyperactivity and feel secure once routines have been established. On the
other hand, average students learn more easily by reading
or seeing rather than by listening, experience energy highs later in the
day rather than in the early morning, and want to achieve
despite the difficulties they experience with traditional schooling.
Contrast those characteristics with underachievers' traits. Many in
the latter group feel warm when classmates are comfortable, cannot sit
passively for any length of time and, thus, are thought to be
-- and are labeled --"hyperactive," rarely do what they are told because
they are nonconforming and resist authoritative directives,
and rarely complete tasks without supervision because they prefer working
on several things simultaneously, need options within
structure and appreciate breaks. Findings concerning learning style
differences among the various achievement levels appear to be
consistent across the board for students regardless of their cultural
background.
Learning Styles of Males and Females
On the other hand, there appear to be more differences between how boys and
girls learn than between cultural groups. Males
require more intake and mobility while learning than females, who are
considerably more persistent and conforming. It can be
argued that mobility and intake may be socially imposed attributes, but
experts proclaim them to be biologically based (Restak, 1979;
Thies, 1979) and persistence frequently correlates with an analytic, rather
than a global, processing style. In the same vein, females
have more of a preference for auditory instruction and males much prefer
tactual and kinesthetic learning. In this regard, an groups
differ significantly from Asians, who prefer significantly more learningby-listening than learning-by-doing -- which may, in some
way, contribute to the academic success of many Asian children in American
schools. Also, regardless of culture, many more boys
than girls can tolerate, and often prefer, sound in their instructional
environment; girls often require quiet while learning. This
phenomenon may be an outgrowth of girls' auditory strengths. They hear
better and thus are more distracted by noise (Pizzo, 1981;
Pizzo, Dunn & Dunn, 1990).
International Study of the Learning, Processing, and Leisure-Time Styles of
Gifted and Talented Versus Non-Gifted
Adolescents
As reported by Price (Dunn, Milgram, & Price, in press), a total of seven
gifted and non-gifted ethnic groups were compared on the
LSI. A systematic pattern of similarities was revealed across the 22
learning style areas for the seven groups studied in Brazil,
Canada, Guatemala, Israel, Korea, the Philippines and the United States.
The greatest similarity among the learning styles of the
gifted were evidenced in the areas of Self-, Parent- and Teacher-Motivated,
Persistence, Responsibility (conformity) and Learning
Alone -- elements that Thies (1979) described as being developmental,
emerging from one's experiences in life. There were greater
variations among the gifted in the areas of Sound, Light, Temperature,
Design, Perceptual Strengths and Mobility -variables that
Garger (1990), Griggs (1991), Restak (1979) and Thies (1979) indicated are
biologically imposed. Although the gifted revealed
essentially similar learning style characteristics in certain areas, they
differed among themselves in degree. For example, the gifted
in the Philippines were significantly more Parent- and Self-Motivated than
all other ethnic groups in that international study (Ingham
& Price, in press).
In addition, significant differences were evidenced between the learning
styles of gifted and non-gifted students in those seven
nations. In particular, although the gifted also preferred to learn
tactually and kinesthetically, they were perceptually strong in three or
four modalities -- including the auditory and visual, whereas the nongifted's perceptual strengths were only tactual or kinesthetic, or
tactual or kinesthetic first with a weak second auditory preference.
Relatively few students, either gifted or non-gifted, preferred learning by
listening. Most preferred learning either through active
participation (kinesthetically), with hands-on instructional resources
(tactually), or by reading (visual/analytic) or seeing charts and
illustrations (visual/global) (Dunn, Bruno, Sklar, Zenhausern, & Beaudry,
1990; Dunn, Cavanaugh, Eberle, & Zenhausern, 1982).
The gifted also were more self-motivated and, in this population of several
thousand, more nonconforming than the non-gifted
population. A different researcher, Lan Yong (1989), found that low
achieving, gifted, United States secondary students liked to eat
when studying, and that motivation, needing a variety of resources to
maintain interest, and learning tactually contributed to the
predictability of academic performance of gifted, secondary students.
Of special interest were the similarities evidenced across the
multicultural groups for each unique gifted population in science and
math, language, music, art, dance, leadership and how they spent their
leisure time. Although wide variations occurred among the
gifted in different talent areas, within each talent area the learning
styles of the gifted revealed strong patterns of similarity. Based
on this international study (Milgram, Dunn. & Price, in press), it may be
possible to:
(a) identify potentially gifted students early by diagnosing their learning
style characteristics; and
(b) determine with reasonable accuracy the areas of talent in which they
will excel.
There were other significant differences identified in Price's (in press)
concluding chapter but, overall, he indicated that the construct
of learning style based on the LSI, as translated into various languages,
was able to diagnose individual differences among the
different multicultural groups.
Next Steps
Studies by Guzzo (1987), L, (1983), Roberts (1984) and Vazquez (1985)
examined the learning styles of students in Brazil, the
Philippines, Bahamas and Jamaica, and Puerto Rico respectively.
Replications were undertaken in Brazil (Wechsler, in press) and in
the Philippines (Ingham & Price, in press), and new investigations were
designed for other nations (Brodhead & Price, in press;
Milgram & Price, in press; Sinatra, deMendez, & Price, in press; Suh &
Price, in press). We currently are awaiting the findings of
studies initiated in Egypt and Greece last year.
Continuing Questions
Regardless the diversity among cultures, the learning styles of:
(a) spouses;
(b) parents and their offspring; and
(c) siblings tend to differ.
Why that occurs is not clear, particularly in light of neurobiologist
Richard Restak's (1979) and psychologist Arming Tries' (1979)
independent assertions concerning the biological nature of much of learning
style. Tries epicifically reported that the environmental,
physiological and psychological characteristics the Dunns described in
their model were biological in nature (1979). Garger (1990)
suggested possible links between some of the Dunns' elements and
neurophysiology. Physicians Richard Crews, president of
Columbia Pacific University (1990), and Melvin D. Levine, Professor of
Pediatrics, School of Medicine (1990), each have similar
beliefs concerning the relationships between neurodevelopmental phenomena
and their implications for school learning. These are
advocates with convincing credentials. However.
(1) If learning style is biological, why do siblings often have styles
diametrically different from each other? Why don't the styles of
offspring necessarily reflect those of their parents? Why do people with
different learning styles tend to marry?
(2) If almost three-fifths of learning style is biological (the
environmental, physiological and psychological elements) (Thies, 1979),
to what extent are the developmental (emotional and sociological) elements
influenced by those thought to be biological? Do
individuals possess more or less sensitivity to either people or things
because of their biological makeup?
(3) If individuals have significantly different learning styles -- as they
appear to have -- is it not unprofessional, irresponsible and
immoral to teach all students the same lesson in the same way without
identifying their unique strengths and then providing
responsive instruction?
Twenty years ago, researchers at St. John's University asked the question,
"Will teaching to underachievers' learning styles impact
on their academic achievement?" That question has been answered
affirmatively many times (Andrews, 1990, 1991; Brunner &
Majewski, 1990; Dunn & Griggs, 1988: Dunn, 1990b; Harp & Orsak, 1990;
Lemmon, 1985; Middle School Reading Program, 1991;
Orsak, 1990; Perrin, 1990; Sinatra, 1990). Now that we have begun learning
about the learning styles of multicultural students,
certain things are clear:
(a) individuals do learn differently from groups;
(b) groups do learn differently from each other,
(c) responding to how students learn significantly increases their
achievement and attitude test scores;
(d) no learning style characteristic is better or worse than any other
learning style characteristic; and
(e) apparently all children can learn -- but they need to be taught to
their individual learning style strengths if they are to master new
and difficult academic material.
* In this report, the term "significantly" is used to indicate statistical
differences.
** The nomenclature used to describe each ethnic group in this manuscript
was used by the researcher in the investigation being
reported.
TABLE 1
Students of the Learning Styles of Students with Different Cultural
Backgrounds
Legend for Chart:
A
B
C
D
-
RESEARCHER, YEAR
GEOGRAPHIC REGION
ACADEMIC LEVEL
CULTURAL GROUP
A
B
C
D
Brodhead & Price (in-press)
Ottawa, Canada
Secondary
Urban, artistically talented
Dunn, Griggs, & Price (in-press)
United States
Secondary
General Population
Dun & Price (in-press)
Texas
Elementary
Mexican-Americans
Ingham & Price (in-press)
The Philippines
Secondary
High SES Secondary
Gifted/Nongifted
Jacobs (1987)
Southern U.S. High,
Middle, Low Achievers
Middle School
Afro-American Euro-American
Jalali (1989)
New York (Urban and
Suburban) Texas (Rural)
Elementary
Airo-American Chinese
American Mexican American
Lam-Phoon (1986)
Lansing, Michigan (Middle
SES) Singapore (Middle SES)
College
Asian-American Caucasian
Asian
Mariash (1983)
Northeast Manitoba
(Rural, ESL)
Secondary Elementary
Cree Indian
Milgram & Price (in-press)
Israel (General population)
Secondary
Giften/Nongifted High,
Average, Middle SES
Roberts
Bahama, Jamaica
Secondary
African Descent
Sims (1988)
California (Low SES, Oregon
(Rural Migrant; Low SES)
Elementary
Black American Mecian
American White
Sinatra, deMendez,
& Price (in-press)
Guatemala (General population)
Secondary
Mayan, Guatemalan
Suh & Price (in-press)
Korea
Secondary
Korean (High SES)
Vazquez (1985)
Puerto Rico
College
Puerto Ricans
Wechsler & Price (in-press)
Brazil
Secondary
Low, Middle SES Brazillians
NOTE: ESL = English as a second language.
Table 2: Statistical Comparison of the LSI Elements for
Afro-American and Chinese-American Children
ELEMENT
AFRO
AMERICAN
CHINESE
AMERICAN
15 sig
Sound
8.16
Light
8.40
Temp
12.20
Design
8.04
Motivation
20.80
Persistence
11.48
Responsibility
7.88
Structure
9.96
Alone
11.72
Authority
8.96
Variety
7.60
Auditory
8.08
Visual
6.36
Tactile
11.40
Kinesthetic
15.88
Intake
10.52
Morning
10.40
Late Morning
6.60
Afternoon
11.12
Mobility
8.68
Parent
11.28
Teacher
13.68
Table 3: Statistical Comparison of the LSI Elements for
Afro-American and Mexican-American Children
9.88[*]
8.32
9.96[*]
8.80[*]
19.64[*]
11.16
9.32[*]
10.12
17.44[*]
9.32
10.24[*]
8.84[*]
7.04[*]
12.00
16.72
8.64[*]
13.24[*]
8.52[*]
10.52
7.96[*]
10.52[*]
12.72[*]
ELEMENT
AFRO
AMERICAN
MEXICAN
AMERICAN
12 sig
8.16
8.40
12.20
8.04
20.80
11.48
7.88
9.96
11.72
8.96
7.60
8.08
6.36
11.40
15.88
10.52
10.40
6.60
11.12
8.64
7.76
9.40[*]
8.12
19.96
10.80
8.60
10.36[*]
15.40[*]
9.12
9.04[*]
8.68[*]
7.76[*]
11.24[*]
16.40[*]
9.72
12.92[*]
7.24[*]
10.68
Sound
Light
Temp
Design
Motivation
Persistence
Responsibility
Structure
Alone
Authority
Variety
Auditory
Visual
Tactile
Kinesthetic
Intake
Morning
Late Morning
Afternoon
Mobility
8.68
Parent
11.28
Teacher
13.68
Table 4: Statistical Comparison of the LSI Elements for
Afro-American and Greek-American Children
ELEMENT
AFRO
AMERICAN
8.00[*]
10.68
13.24
GREEK
AMERICAN
9 sig
Sound
8.16
Light
8.40
Temp
12.20
Design
8.04
Motivation
20.80
Persistence
11.48
Responsibility
7.88
Structure
9.96
Alone
11.72
Authority
8.96
Variety
7.60
Auditory
8.08
Visual
6.36
Tactile
11.40
Kinesthetic
15.88
Intake
10.52
Morning
10.40
Late Morning
6.60
Afternoon
11.12
Mobility
8.68
Parent
11.28
Teacher
13.68
Table 5: Statistical Comparison of the LSI Elements for
Chinese-American Children and Greek-American
8.44
7.40[*]
10.48[*]
7.60
19.66[*]
11.92
9.23[*]
7.72[*]
14.20[*]
8.84
8.76[*]
9.60[*]
6.92
10.88
15.60
9.00[*]
10.76
7.12
10.96
8.76
10.96
12.88[*]
ELEMENT
CHINESE
AMERICAN
GREEK
AMERICAN
13 sig
9.88
8.32
9.96
8.80
19.64
11.16
9.32
10.12
17.44
9.32
10.24
8.84
7.04
12.00
16.72
8.64
13.24
8.52
10.52
7.96
10.52
12.72
8.44[*]
7.40[*]
10.48
7.60[*]
19.66
11.92[*]
9.23
7.72[*]
14.20[*]
8.84
8.76[*]
9.60[*]
6.92
10.88[*]
15.60[*]
9.00
10.76[*]
7.12[*]
10.96
8.76[*]
10.96
12.88
Sound
Light
Temp
Design
Motivation
Persistence
Responsibility
Structure
Alone
Authority
Variety
Auditory
Visual
Tactile
Kinesthetic
Intake
Morning
Late Morning
Afternoon
Mobility
Parent
Teacher
Table 6: Statistical Comparison of the LSI Elements for
Mexican-American and Chinese-American Children
ELEMENT
MEXICAN
AMERICAN
CHINESE
AMERICAN
8 sig
Sound
8.64
Light
7.76
Temp
9.40
Design
8.12
Motivation
19.96
Persistence
10.80
Responsibility
8.60
Structure
10.36
Alone
15.40
Authority
9.12
Variety
9.04
Auditory
8.68
Visual
7.76
Tactile
11.24
Kinesthetic
16.40
Intake
9.72
Morning
10.40
Late Morning
12.92
Afternoon
7.24
Mobility
10.68
Parent
8.00
Teacher
13.24
Table 7: Statistical Comparison of the LSI Elements for
Greek-American and Mexican-American Children
9.88[*]
8.32
9.96
8.80[*]
19.64
11.16[*]
9.32
10.12[*]
17.44
9.32
10.24[*]
8.84
7.04[*]
12.00[*]
16.72
8.64[*]
13.24
8.52[*]
10.52
7.96
10.52
12.72
ELEMENT
GREEK
AMERICAN
MEXICAN
AMERICAN
8 sig
8.44
7.40
10.48
7.60
19.66
11.92
9.23
7.72
14.20
8.84
8.76
9.60
6.92
10.88
15.60
9.00
10.76
7.12
10.96
8.76
10.96
12.88
8.64
7.76
9.40[*]
8.12
19.96
10.80[*]
8.60
10.36[*]
15.40
9.12
9.04
8.68[*]
7.76[*]
11.24
16.40
9.72
12.92[*]
7.24
10.68
8.00
10.68
13.24
Sound
Light
Temp
Design
Motivation
Persistence
Responsibility
Structure
Alone
Authority
Variety
Auditory
Visual
Tactile
Kinesthetic
Intake
Morning
Late Morning
Afternoon
Mobility
Parent
Teacher
DIAGRAM: Figure 1. Simultaneous or Successive Processing
GRAPH: Figure 2: LSI Profiles, Chinese and Afro Students.
GRAPH: Figure 3: LSI Profiles. Afro and Mexican Students.
GRAPH: Figure 4: LSI Profiles, Afro and Greek Students
GRAPH: Figure 5: LSI Profiles, Chinese and Greek Students.
GRAPH: Figure 6: LSI Profiles, Chinese and Mexican Students.
GRAPH: Figure 7: LSI Profiles, Greek and Mexican Students.
PHOTO (BLACK & WHITE): Rita Dunn
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~~~~~~~~
By Rita Dunn
Dr. Rita Dunn is professor in the Division of Administrative and
Instructional Leadership and Director of the Center for the Study of
Learning and Teaching Styles, St. John's University, New York.
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