Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING:
PROFILING UNDERGRADUATE STUDENTS IN A SEMESTER-LONG COURSE
Emily M. Grossnickle, Daniel L. Dinsmore, Patricia A. Alexander, and Alexandra List
University of Maryland, College Park
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
Abstract
The interrelations between knowledge, interest, and strategic processing have been well
documented. However, much remains to be learned about how these interrelations manifest
under particular conditions or how they change over time. Using Alexander’s Model of Domain
Learning (Alexander, 2003), the interrelations between these constructs at the beginning and end
of an undergraduate course were investigated to understand contextual and developmental
conditions. Data for 150 students were examined and 6 distinct clusters were established at the
beginning of the term. Membership in the various clusters was tracked from the beginning to
end of the course to understand developmental changes. Results suggested that the direction and
degree of changes in knowledge, interest, and strategic processing depended upon cluster
membership.
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
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Knowledge, interest, and strategic processing:
Profiling undergraduate students in a semester-long course
Well-rounded (adjective): showing interest or ability in many fields
Webster’s New World College Dictionary (1999)
Higher education aims to educate individuals broadly, across a variety of domains, as
well as intensively, within a selected area of study. The former of these aims is typically
manifest as a general education curriculum to be completed in addition to courses within a major
area of study (Warner & Koeppel, 2009). The purpose and nature of general education has
recently come into the public spotlight as colleges and universities have sought to revamp the
general education requirements, and as current requirements have been criticized for being too
general or too specific (ACTA, 2009; Bourke, Bray, & Horton, 2010). As colleges seek to
produce individuals who are well-rounded with regard to coursework, it is important to consider
the nature of development for students in these general education courses.
The aim of this study was to explicate differences in the knowledge, interest, and
strategies of acclimating learners at the beginning and conclusion of an undergraduate general
education course and to understand how those differences manifest. Variations in these
foundational variables at the point of entry into a new domain and their interplay over time are
key to understanding expertise (Alexander et al., 1997; Hidi & Renninger, 2006). Although we
would not assume that students engaging in general education course would become experts in
the domain (or anything close to that), inquiry into the nature of development across a semester
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should provide further understanding of the individual differences as students enter and leave
such general education courses.
Alexander’s Model of Domain Learning (MDL; 2003; Alexander, Jetton, & Kulikowich,
1995; Alexander et al., 1997) asserts that the interrelation between knowledge, interest, and
strategic processing in an academic domain evolves as an individual progresses through three
stages (i.e., acclimation, competence, proficiency/expertise). According to the MDL, individuals
in acclimation have limited knowledge of the domain they are studying, rely on surface-level
strategies, and display little personal interest in the field. As individuals become more
competent, individual interest should grow; subject-matter knowledge increase quantitatively and
become more cohesive or principled; and more deep-processing strategies should be employed
(Alexander, 2003). Such changes have been documented in prior investigations of the MDL
involving such domains as history, physics, and literacy.
The interrelation among knowledge, interest, and strategic processing has been well
documented in the educational psychology literature (Ainley, Hidi, & Berndorff, 2002;
Alexander, Jetton, & Kulikowich, 1995; Alexander et al., 1997; Lawless & Kulikowich, 2006).
Students enter learning situations with varying levels of prior knowledge, interest, and strategic
processing related to the domain. As students progress through courses, knowledge-based tests
serve as a marker of their progress. Assessments of knowledge determine whether students have
met satisfactory requirements and denote their progress within an area of study. Yet, knowledge
alone does not indicate growth within a domain. Interest and strategic processing also play
important roles in academic development and indicate a students’ advancement.
The relation between knowledge, interest, and strategic progressing are not independent.
Rather, they interact during an individual’s development in an academic domain. Thus, it is
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hypothesized that as individuals’ knowledge of a domain increases across the stages, there is a
greater likelihood of increasing interest in the domain and more willingness to exert the effort
required to process content deeply. Reciprocally, as individuals exert greater strategic effort of a
deep-processing kind, there is enhanced likelihood that subject-matter knowledge will expand
and deepen.
Knowledge in the MDL is identified as domain knowledge or topic knowledge
(Alexander, 2003; Murphy & Alexander, 2002). Domain knowledge is the breadth of knowledge
that an individual has with regard to the field in relative to the whole of knowledge in the field.
For instance, domain knowledge encompasses the breadth of knowledge that one has about the
field of educational psychology. Topic knowledge is distinguished from domain knowledge by
depth of knowledge. Compared to domain knowledge, which is broad and encompasses the span
of the field, topic knowledge is detailed knowledge about particular topics within a domain. For
instance, an individual may have an abundance of topic knowledge about motivation but
relatively little domain knowledge of the field of educational psychology as a whole. Individuals
in acclimation may have detailed knowledge about particular topics, but their overall knowledge
of the domain is fragmented as compared to individuals who have reached proficiency. As
individuals become immersed in the study of a domain, their domain and topic knowledge are
hypothesized to increase (Alexander, 2003).
Interest is often depicted as being one of two types: situational or individual (Hidi, 1990),
with both forms having distinct cognitive and affective components (Hidi & Renninger, 2006).
Situational interest is defined by feelings of enjoyment accompanied by momentary arousal or
attention sparked by features of the environment (Hidi, 1990). Complexity, novelty, and
surprisingness are environmental factors often associated with heightened attention that occurs
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with situational interest (Berlyne, 1960; Silvia, 2005). In contrast, individual interest is depicted
as an enduring disposition toward particular content (Silvia, 2006). Included as defining features
are knowledge of and value for the object of interest (Hidi & Renninger, 2006). Individual
interest involves the enjoyment and attention consistent with situational interest, but is carried
with the individual to different contexts, rather than relying on environmental cues for initiation.
In her Model of Domain Learning, Alexander (2003; Alexander et al., 1995) posits that
as an individual advances in his or her development within a domain, there is interplay between
individual and situational interest. When one is new to a field, the role of situational interest is
important as a means for engagement with learning in the domain. As an individual develops,
the role of situational interest, although still present, becomes significantly less crucial to one’s
involvement. On the contrary, individual interest follows a path by which it increases throughout
development. As individuals become more interested and invested in a domain, they learn more,
and as they continue to learning, their interest increases. It is individual interest which is
relevant for this study.
Strategic processing is defined as procedural knowledge that individuals utilize in an
effort to gain understanding (Murphy & Alexander, 2002). Alexander (2003) defines two
qualitatively different types of processing strategies that develop as an individual progresses in a
domain: surface-level strategies and deep-processing strategies. In the literature, these strategies
have often been related to reading, as processing text is an essential means of learning within
many domains, although there is the expectation that surface-level and deep-processing strategies
are foundational to all domains of learning. Surface strategies are used to make sense of the
problem at hand (Alexander et al., 2005). For the domain of reading, such strategies may include
rereading, changing reading rate, and rehearsing points to remember. Deep strategies are used to
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integrate, transform, or evaluate a problem (Alexander et al., 2005; Nolen & Haladyna, 1990).
Again within the domain of reading, these strategies would include elaborating on main ideas,
identifying an underlying argument, and providing justification for a claim. As acclimating
learners in a domain, individuals rely on surface processing strategies to make sense new ideas
and information. As knowledge is gained, individuals are able to utilize more deep processing
strategies and gain deeper, more integrated understandings.
For this study, educational psychology was the domain of focus and afforded the
opportunity to consider how the interplay of knowledge, strategies, and interest play out over
time in a relatively novel domain. Although we would expect these students to have encountered
some of the content being implemented throughout their K-12 and undergraduate experiences,
we would not anticipate that they have engaged in prior academic study of the domain.
The purpose of this study is to expand on previous research of the MDL by examining
profiles of students according to their domain knowledge, interest, and strategic processing in an
educational psychology course. It is predicted that all of the students enrolled in the course will
be acclimating learners in the field of educational psychology. However, rather than assuming
that all students are similarly acclimated learners when they enter the course, the purpose was to
examine whether different profiles of students emerged at the beginning of the semester
according to their knowledge, interest, and strategic processing. Moreover, as knowledge,
interest, and strategic processing have been shown to be important for learning, the different
profiles of students were examined for changes from the beginning to the end of the semester in
terms of these dimensions. Are certain combinations of knowledge, interest, and strategic
processing at the beginning of the semester more conducive to gains in knowledge, interest, and
strategic processing by the end of the semester in a general education course such as educational
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psychology? Finally, it was hypothesized that knowledge, interest, and the use of deep-level
strategies would increase from the beginning to the end of the semester for the students as a
whole. The hypothesis is consistent with the MDL, which suggests that as students progress in
their study of a domain, their knowledge, interest, and deep-level strategic processing increase
and their reliance on surface strategies decreases.
The research questions that guided this study were as follows:
1. How do students’ domain-specific knowledge, knowledge analogies, interest, strategic
processing, and text comprehension in the field of educational psychology differ at the
beginning and end of a semester-long course?
2. Based on students’ domain-specific knowledge, knowledge analogies, interest, strategic
processing, and text comprehension, what clusters are found at the beginning and end of the
course?
3. How does the degree of change in students’ domain-specific knowledge, knowledge
analogies, interest, strategic processing, and text comprehension over the semester relate to
their initial cluster membership?
Method
Participants
Participants in this study were 167 undergraduates at a large mid-Atlantic university. Of
the 167 participants, 15 were missing either a complete pretest or complete posttest and two were
missing one or more entire measures. As a fundamental aim of this study was to examine
differences across the semester, these individuals were excluded from the analysis, leaving 150
participants. All participants were enrolled in an educational psychology course. For the
students at this university, this course functions as a general elective and is not required for
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students in any particular major. Due to the elective nature of this course, participants were
studying a wide variety of majors from across the university and are not typically education
majors. The average age of participants was 21.6 years and the ethnic makeup of the sample was
as follows: 93 European American (62.0%), 17 African American (11.3%), 18 Asian American /
Pacific Islander (12.0%), 11 Hispanic (7.3%), and 11 other (7.3%). Participants selecting
multiple ethnic categories were classified as other. One hundred six females (70.7%) and 43
males participated; 1 student did not provide his or her sex. The characteristics of the
participants were consistent with the overall makeup of the university.
Measures
Topic interest scale. Subject-matter interest was assessed via a 13-item topic interest
measure. The topics were designed to cover a wide array of topics covered in the course, such as
motivation, memory, classroom management, and learning strategies. For each topic students
were directed to make a slash on the 100mm line ranging from not interested to very interested.
A principal components factor analysis indicated that all 13-items loaded on the same factor,
therefore a summation score was used for the analyses.
Multiple-choice knowledge test. Domain knowledge was measured via a 10-item
multiple choice test with four response options per question. This test was designed to measure
students’ declarative knowledge in educational psychology. A sample item is as follows:
Noah performs substantially better than the rest of his classmates in all subject areas.
Further, he does not appear to work that hard for his grades. This difference between
Noah and his classmates may be attributed to:
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
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a. motivation
b. general intellectual ability
c. specific academic aptitude
d. spatial reasoning
(b is the correct answer)
The items on the test were drawn from topics covered within the educational psychology
course in which the students were enrolled. One point was awarded for each correct answer for a
maximum score of 10.
Domain knowledge analogies test. Domain knowledge was further assessed through
use of seven analogical reasoning problems. The items on the test were drawn from topics
covered within the educational psychology course in which the students were enrolled. The
analogies followed a standard A:B::C:
format.
The analogy items were scored on a 1 to 7 rubric (see Alexander, Murphy, &
Kulikowich, 1998). Responses ranged from 1 (no response) to 7 (correct response), with points
in between awarded for responses of increasing levels of sophistication and domain-specificity.
For example, 3 was awarded for a response outside the domain of educational psychology and 5
was awarded for a response in the domain of educational psychology that was different from the
correct response. See Appendix A for sample questions and a complete scoring rubric. The first
and fourth authors scored a random sample of analogies for 41 students (25% of the sample).
The interrater agreement was .92. After achieving interrater agreement the first author coded the
remainder of the responses.
Texts. Two text passages were selected for inclusion in this study. Participants received
only one of the passages at each time point and the order of the passages was counterbalanced.
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
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One text addressed the topic of motivation and the second addressed the topic of strategic
processing. The texts were of comparable length and difficulty (e.g., word count, paragraphs,
sentence structure, and technical language) and both texts addressed topics covered within the
educational psychology course in which the students were enrolled.
Strategic processing inventory. The strategic processing inventory consisted of 20 textprocessing strategies. This measure was taken from a study by Murphy and Alexander (2002).
Strategies related to translating or reading in a different language (i.e., “translated words or
phrases to another language” and “summarized the passage in another language”) were not
included because they were not selected by any of the participants. Additionally, the strategies
“consulted reference materials” and “looked up vocabulary” were omitted because students were
not allowed to access these sources while completing the task. Participants were directed to
check (✓) any strategy that they used and to put an asterisk (*) next to the strategies that they
found most helpful. A 0 to 2 scale was used to score strategic processing for each of the
strategies. Strategies marked with a check were awarded 1 point and strategies marked with an
asterisk were awarded 2 points. Unmarked strategies received 0 points.
The strategies were separated into subgroups of deep processing strategies and surface
processing strategies. This division has been theoretically justified as accounting for different
types of processing (Alexander, 2003) and this distinction is an important component of the
MDL. Examples of surface processing strategies included changed reading rate and rehearsed
main ideas. Deep processing strategies included items such as related to background knowledge
and reflected on reading. There were seven total surface processing strategies and nine deep
processing strategies. The strategies were combined in a random order on the checklist. A list of
all possible strategies are included in Appendix A.
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
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Domain-specific text recall measure. At the beginning of the study, participants were
instructed that they could spend as much time reading the text as they wanted. After reading,
students completed a free recall of the text without being able to look back at the text. They
were asked two separate questions designed to have them identify both the main ideas and
supporting details. The first question asked students to “list what you consider to be the main
points of the passage you read” and the second question asked students to “jot down anything
else you remember from the passage you studied.”
Procedure
Participants completed all of the measures twice during the semester. The first
administration was during the second week of a fifteen-week semester and the second
administration was following the thirteenth week of the semester. After receiving and signing
the informed consent, participants were provided with a packet including a demographics
questionnaire, interest measure, knowledge tests, and the text passage. They were instructed that
there was no time limit for completing the items and that when they had completed the first
packet they would receive a second packet. The second packet contained the strategic processing
inventory and the recall measure. Participants were not allowed to look back at the text while
completing either the strategic processing inventory or the recall measure. The measures were
completed during their regularly scheduled class and all measures were completed in a single
session.
Results
Students’ Pretest to Posttest Performance
Before conducting the analyses of interest, differences between students who read the
strategy passage first were compared with students who read the motivation passage first to
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ascertain whether different levels of strategic processing were reported for the different passages.
A between-group MANOVA indicated no significant differences in the combination of surface
and deep strategy use for students in either of the counterbalanced text conditions (Wilk’s
Λ=.987, p=.75); therefore, all students were combined into a single sample for the remaining
analyses.
Scores on the text recall measure were equated across passages using z-scores. These
scores were examined for the effect of receiving each passage at either pretest and posttest. A ttest for independent groups revealed a significant difference in text recall scores for the
motivation passage (t=-2.00, p=0.47); individuals scored higher when receiving this passage at
pretest than when receiving this passage at posttest. Due to the nature of this difference, analysis
of text recall was not included in the analyses. The mean z-scores are for both conditions are
included in Table 2.
The relations between the variables at both time points were examined using Pearson’s
correlation coefficients. As expected, pretest and posttest scores were significantly correlated for
all individual measures. Significant correlations between the domain knowledge and knowledge
analogy measures were found for both time points. Additionally, surface and deep processing
strategy use was significantly correlated at pretest (r=.352, p<.001) and posttest (r=.279, p<.001).
Further, surface strategies at pretest were correlated with deep processing strategies at posttest
(r=.200, p=.014). Further, interest at posttest was also significantly correlated with deep
processing strategies at posttest (r=.214, p=.009). The complete correlation matrix is presented
as Table 1.
Changes in domain knowledge, interest, and strategic processing from the beginning to
the end of the course were examined. A summary of pretest and posttest values for all variables
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is presented as Table 2. Over the course of the semester, student domain and knowledge
analogies increased while interest and strategic processing decreased. To examine whether these
changes were significant, an analysis of the effects of each variable at pretest on the combined
posttest scores was observed, followed by analyses of the effects of each individual pretest
variable on the individual posttest scores. This permitted an examination of the cumulative
effects as well as the individual effects of the variables. A repeated-measures MANOVA was
run with knowledge, interactive knowledge, interest, surface processing strategies, and deep
processing strategies as the dependent variables and time as the repeated, within-subjects
variable.
Results from repeated-measures t-tests comparing pretest to posttest scores were in
accordance with some of the predictions. Domain knowledge at pretest significantly differed
from domain knowledge at posttest (t=-8.91(149), p<.001) and knowledge analogies at pretest
significantly differed from knowledge analogies at posttest (t=-12.18(149), p<.001). Students’
domain knowledge and knowledge analogies increased from pretest to posttest. This increase
was expected since the students were enrolled in a course in which the topics of the knowledge
questions were covered as part of the course content. Interest, surface processing strategies, and
deep processing strategies were not significantly different from pretest to posttest. Students’
interest remained high from pretest to posttest. Both types of strategy use declined slightly from
pretest to posttest, albeit not significantly.
Cluster Analysis
A cluster analysis was run to investigate whether profiles of student knowledge, interest,
and strategic processing emerged at the beginning of the semester. Due to the exploratory nature
of this investigation a hierarchical cluster analysis was used first to determine an approximate
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number of clusters. An agglomerative procedure with average linkage was used to form the
clusters. Domain knowledge, knowledge analogies, interest, surface processing, and deep
processing were included in the cluster analysis. Due to the variability in values for the scales
used, scores on all of the measures were transformed to z-scores (CITE). Upon examination of
the dendrogram it was determined that a two or five cluster solution was most appropriate. The
proportional increase in the squared Euclidean distances were examined to converge evidence
and also determined these values as an appropriate number of clusters. A two cluster solution
was eliminated as a possibility because it would not meet the needs of profiling smaller groups of
students based on their beginning of the semester scores. Therefore, the five cluster solution was
compared to four and six cluster solutions using K-means cluster analysis. After examining
mean scores for all variables it was determined that the six cluster solution provided the most
meaningful description of the students at the beginning of the semester.
To verify the replicability of the six cluster solution, the sample was randomly divided
into two samples of equal size and the six cluster solution was examined for each sample. Upon
inspection of the means of the variables for the clusters in each sample, it was determined that
the six cluster solution was replicated well for both samples. To provide further evidance, a
discriminant function analysis of the merged sample was used to determine whether individuals’
transformed scores could accurately predict their cluster membership and provide additional
validation for the five cluster solution. Probabilities were weighted according to cluster group
sizes. 90.8% of the cases were accurately classified. This further supported retention of the six
cluster solution. The six clusters are described below.
Descriptions of the Clusters
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Students in the five clusters differed in terms of their knowledge, interest, and use of
surface and deep processing strategies. All of the scores are reported at pretest. The
configurations of knowledge, interest and strategies differed across groups. A total of 150
students were classified. Table 3 provides the pretest and posttest raw score averages for each of
the clusters.
Cluster 1: High achievers. Students in cluster 1 had high scores across all measures at
pretest. They had relatively high domain knowledge and knowledge analogy scores, high
interest, and high strategy use for both deep and surface strategies. There were 17 students in
this cluster. Due to their relatively high knowledge and individual interest, the MDL would
suggest that these students are beginning the semester in a relatively advanced stage of
acclimation. At this stage it is typical for students to rely on a high level of surface strategies in
addition to accessing deep processing strategies.
Cluster 2: Struggling learners. In contrast to the high achievers, cluster 2, the
struggling learners, had relatively low scores across all pretest measures. The struggling learners
had relatively low scores for both the domain knowledge and the knowledge analogies tests.
They had relatively low interest and reported low strategy use for both deep and surface level
strategies. There were 32 students in this cluster. Their low reported strategy use was likely
linked to their low level interest in the domain. These students are typical of learners early in the
acclimation stage.
Cluster 3: Knowledge reliant. Students in cluster 3, the knowledge reliant cluster, had
above average to high domain knowledge and knowledge analogy scores. However, in
comparison to the high achievers, the knowledge reliant individuals reported very low interest
and low surface-level and deep-processing strategies. There were 26 students in this cluster.
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Cluster 4: Interest reliant. Students in the interest reliant cluster also had relatively
high domain knowledge and knowledge analogy scores. Further, their interest was relatively
high. However, their reported use of surface-level and deep-processing strategies was
comparatively low. Despite having high levels of knowledge and interest, these students
reported using a limited number of strategies in their attempts to perform a domain related task.
There were 32 students in this cluster.
Cluster 5: Effortful surface processors. Students in cluster 5 had relatively low domain
knowledge and knowledge analogy scores, and they reported relatively average interest. In
terms of strategies, the reported using a high number of surface-level strategies but relatively few
deep-processing strategies. There were 17 students in this cluster.
Cluster 6: Effortful deep processors. This final cluster consisted of 26 students with
relatively average to below average domain knowledge and knowledge analogies, and relatively
low interest. As compared with the students in the effortful surface processors cluster, these
students reported using high numbers of surface-level strategies and very high numbers of deepprocessing strategies.
Individuals in the high achievers, knowledge reliant, and interest reliant clusters had
relatively high knowledge as compared to relatively average knowledge in the effortful deep
processors cluster and compared to relatively low knowledge in the struggling learners and
effortful surface processors clusters. High interest was observed in the high achievers and
interest reliant clusters. Relatively average interest was observed for individuals in the
struggling learners and effortful surface processor clusters, while individuals in the knowledge
reliant and effortful deep processor clusters expressed relatively low interest. With regard to
strategy use, the struggling learners, knowledge reliant, and interest reliant clusters had relatively
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low strategy use while the high achievers cluster had high strategy use. This was consistent for
both surface and deep strategies in these four profiles. The two effortful processor clusters
demonstrated different levels of strategy for deep and surface strategic processing. Both of these
clusters were average to above average in terms of their strategic processing; however,
individuals in the effortful surface processors cluster reported a relatively high amount of
surface-level strategies in contrast to low levels of deep-processing strategies. However,
individuals in the effortful deep processing cluster reported high numbers of surface-level
strategies in conjunction with an extremely high level of deep-processing strategies.
Students’ Pretest to Posttest Performance Within Clusters
A summary of pretest and posttest scores for each cluster is included in table 3. Followup repeated measures t-tests were run for all clusters to determine whether there were significant
changes in knowledge, interest, or strategy use within each cluster. Data are presented separately
for each cluster.
High achievers. Repeated-measures t-tests indicated that there were significant changes
from pretest to posttest in terms of domain knowledge (t=-4.34(16), p=.001), knowledge
analogies (t=-3.27(16), p=.005), surface-level strategies (t=5.791(16), p<.001), and deepprocessing strategies (t=3.74(16), p=.009). Both knowledge measures increased, but both types
of strategy use decreased. Interest did not differ significantly from pretest to posttest.
Struggling learners. Repeated-measures t-tests indicated that there were significant
changes from pretest to posttest in terms of domain knowledge (t=-6.96(31), p<.001), knowledge
analogies (t=-5.56(31), p<.001) and surface-level strategies (t=4.50(31), p<.001). Changes in
these three variables from pretest to posttest were all significantly positive. There were no
significant differences in interest or deep-processing strategies from pretest to posttest.
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Knowledge reliant. Repeated-measures t-tests indicated that there were significant
changes from pretest to posttest only in terms of knowledge analogies (t=-4.87(25), p<.001).
These students began the semester with a relatively high level of knowledge and maintained that
high level of knowledge across the semester. There were no significant difference in interest or
strategy use from pretest to posttest.
Interest reliant. Repeated-measures t-tests indicated that there were significant changes
from pretest to posttest in terms of knowledge analogies (t=-3.85(31), p=.001) and interest
(t2.33(31), p=.027). Knowledge significantly increased, while interest significantly decreased.
There were no significant differences in domain knowledge scores or strategy use from pretest to
posttest.
Effortful surface processors. Repeated-measures t-tests indicated that there were
significant changes from pretest to posttest in terms of domain knowledge (t=-6.75(16), p<.001)
and knowledge analogies (t=-8.77(16), p<.001). Both types of knowledge significantly
increased. There were no significant differences in interest or strategy use from pretest to
posttest.
Effortful deep processors.
Repeated-measures t-tests indicated that there were
significant increases from pretest to posttest in terms of domain knowledge (t=-5.67(25), p<.001)
and knowledge analogies (t=-6.81(25), p<.001). There were significant decreases in deepprocessing strategies (t=5.06(25), p<.001). No significant changes were reported for interest or
surface-level processing.
Within the clusters, knowledge increased from pretest to posttest, with all but two
changes as statistically significant. However, the degree of increase in knowledge was not
universal across clusters. For instance, the effortful surface processor cluster’s average
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knowledge analogy score increased from 10.71 to 27.65 as compared to increases of less than 10
points for students in the other clusters. Only one cluster, interest reliant, had a significant
change in interest from pretest to posttest. These students experienced a significant decline in
interest (96.78 at pretest, 92.03 at posttest).
The students in the high achievers cluster experienced a substantial decline in strategy
use. By the end of the semester they were using fewer numbers of surface-level strategies (6.65
at pretest, 4.00 at posttest) and deep-processing strategies (6.71 at pretest, 4.53 at posttest) which
coincided with an increase in knowledge. The MDL suggests that increases in knowledge would
allow these students to rely less on surface-level strategies in order to make sense of texts in the
domain of educational psychology. These students may also have been more successful in their
use of certain deep-processing strategies, thus requiring them to use fewer strategies to make
sense of the text. The effortful deep processors also used significantly fewer deep-processing
strategies at posttest (9.52 at pretest, 5.92 at posttest), although they still remained above the
mean at posttest. On the other hand, the struggling learners demonstrated a significant increase
in surface processing strategies from pretest (M=1.72) to posttest (M=2.81). These students
reported using low levels of strategies at both time points as compared to the other clusters.
Discussion
The purpose of this study was to extend previous research of the MDL by examining
whether clusters of students emerged at the beginning of a semester long course in educational
psychology. Of particular interest were the developmental changes that students in different
clusters made as they progressed through their learning experiences. Understanding the nature of
individual differences in the undergraduate classroom is an important step in learning how to
cater learning environments of general education courses to meet diverse students. This study
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examined individual differences in knowledge, interest, and strategic processing at a level more
general than the individual, yet more specific than grouping all students together as acclimated
learners. Before dividing students into profiles, the sample was examined as a whole. Some of
the initial hypotheses with regard to pretest to posttest changes were observed. For instance, as a
whole, student knowledge increased from pretest to posttest. Conversely, interest did not
significantly increase as hypothesized. This may have been due to the complex relation between
interest and knowledge which is discussed as a future direction.
The second research question addressed whether emergent profiles of student knowledge,
interest, and strategy use could be identified at the beginning of a semester-long course. Situated
within the framework of the MDL, six profiles emerged as a result of clustering analyses. These
profiles were consistent with the stance of the MDL and aided in explicating differences in
individuals within the stage of acclimation. Due to comparatively high knowledge and interest,
students in the high achievers cluster were determined to be learners in mid-to-late acclimation.
Students in the struggling learners cluster were prototypical of students in early acclimation,
while students in the knowledge reliant, interest reliant, and effortful processor clusters contained
students with knowledge, interest, and strategic processing that positions them in early-to-mid
acclimation.
Different patterns of changes were exhibited by each of the beginning of the semester
profiles. All of the profiles demonstrated significant increases in domain knowledge and several
demonstrated increases in knowledge analogy scores. These increases were not equal across
groups. Students in the effortful surface processors cluster exhibited greater knowledge
increases than their peers. In terms of interest, students in the interest reliant cluster reported a
statistically significant decrease. This contrasts findings for the sample as a whole, which
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
20
suggested that interest did not change from pretest to posttest. Similarly, statistically significant
changes in strategic processing were noted for the high achievers, struggling learners, and
effortful deep processors, despite such changes being negligible in the entire sample. These
findings suggest that the changes that occur during a semester in terms of knowledge, interest,
and strategic processing are not equal for all profiles of students.
This study was not without limitations. The measurement of individual interest and its
relation to knowledge is complex (Hidi & Renninger, 2006). Are students answering the interest
items at the beginning of the semester, when their knowledge is relatively low, in the same way
in which they are answering the questions at the end of the semester after they have had specific
learning experiences and greater knowledge of each of the topics? Understanding how students
interpret interest questions at the beginning of the semester compared to the end of the semester
is an important step in understanding changes in interest progress through a domain such as
educational psychology. Further, despite completing a 15-week course, student posttest
knowledge was relatively low. It is possible that students took the questions more seriously at
the beginning of the semester when they were just becoming acquainted with their professor and
less seriously at the end of the semester. Since their results were not associated with their course
grade, there was less incentive to invest effort in demonstrating knowledge and utilizing
strategies when reading. It would be worthwhile in future studies to measure situational as well
as individual interest. Since it is posited that situational interest is of particular importance for
acclimating learners, it is important to examine the relation between the two types of interest and
students’ knowledge and strategic processing. As researchers and educators search for a balance
between understanding individual differences in the classroom and making sense of the diverse
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
21
learners in meaningful ways, the MDL continues to serve as a useful aid in making sense of the
complexity of domain learning and domain learners.
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
22
References
Alexander, P. A., Buehl, M. M., Sperl, C. T., Fives, H., & Chiu, S. (2005). Modeling domain
learning: Profiles from the field of special education. Journal of Educational
Psychology, 96, 545-557.
Ainley, M., Hidi, S., & Berndorff, D. (2002). Interest, learning and the psychological processes
that mediate their relationship. Journal of Educational Psychology, 94, 545-561.
Alexander, P. A. (2003). The development of expertise: The journey from acclimation to
proficiency. Educational Researcher, 32, 10-14.
Alexander, P. A., Jetton, T. L., & Kulikowich, J. M. (1995). Interrelationship of knowledge,
interest, and recall: Assessing a model of domain learning. Journal of Educational
Psychology, 87, 559-575.
Alexander, P. A., Murphy, P. K., & Kulikowich, J. M. (1998). What responses to domainspecific analogy problems reveal about emerging competence: A new perspective on an
old acquaintance. Journal of Educational Psychology, 90, 397-406.
Alexander, P. A., Murphy, P. K., Woods, B. S., Duhon, K. E., & Parker, D. (1997). College
instruction and concomitant changes in students’ knowledge, interest, and strategy use: A
study of domain learning. Contemporary Educational Psychology, 22, 125-146.
American Council of Trustees and Alumni (ACTA, 2009). What Will They Learn? A Report on
General Education Requirements at 100 of the Nation’s Leading Colleges and
Universities. Retrieved from
https://www.goacta.org/publications/downloads/WhatWillTheyLearnFinal.pdf
Bourke, B., Bray, N. J., & Horton, C. C. (2009). Approaches to the core curriculum: An
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23
exploratory analysis of top liberal arts and doctoral-granting institutions. The Journal of
General Education, 58, 219-240.
Hidi, S. (1990). Interest and its contribution as a mental resource for learning. Review of
Educational Research, 60, 549-561.
Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational
Psychologist, 41, 111-127.
Lawless, K. A., & Kulikowich, J. M. (2006). Domain knowledge and individual interest: The
effects of academic level and specialization in statistics and psychology. Contemporary
Educational Psychology, 31, 30-43.
Murphy, P. K., & Alexander, P. A. (2002). What counts? The predictive powers of subjectmatter knowledge, strategic-processing, and interest in domain-specific performance. The
Journal of Experimental Education, 70, 197-214.
Nolen, S. B., & Haladyna, T. M. (1990). Personal and environmental influences on students’
beliefs about effective study strategies. Contemporary Educational Psychology, 15, 116130.
Pastor, D. A. (2010). Cluster analysis. In G. R. Hancock & R. O. Mueller (Eds.). The Reviewer’s
Guide to Quantitative Methods in the Social Sciences (pp. 41-54). New York: Routledge.
Silvia, P. J. (2006). Exploring the psychology of interest. New York: Oxford University Press.
Silvia, P. J. (2005). What is interesting? Exploring the appraisal structure of interest. Emotion, 5,
89-102.
Warner, D. B., & Koeppel, K. (2009). General education requirements: A comparative analysis.
The Journal of General Education, 58, 241-258.
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
Table 1
Pearson Product-Moment correlation coefficients for knowledge, interactive knowledge, interest, and strategy use at pretest and
posttest
1
2
3
4
1. Pretest domain knowledge
1.000
2. Posttest domain knowledge
.463**
3. Pretest knowledge analogies
.358** .185** 1.000
4. Posttest knowledge analogies
.210** .305** .424** 1.000
5
6
7
8
.039
.053
.027
-.027
1.000
6. Posttest interest
.048
.038
-.010
.092
.559** 1.000
7. Pretest surface strategies
-.034
.139
-.061
.043
.038
-.011
8. Posttest surface strategies
-.009
.119
-.014
.042
-.066
-.012 .397** 1.000
9. Pretest deep strategies
-.037
-.063
.094
.020
.044
.098
10. Posttest deep strategies
.016
-.006
-.042
.042
.104
* = Correlation is significant at the .05 level (two-tailed).
10
1.000
5. Pretest interest
** = Correlation is significant at the .001 level (two-tailed).
9
1.000
.352** .159
1.000
.214** .200** .279** .295** 1.000
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
Table 2
Means and standard deviations for knowledge, interest, and strategy use
Pretest
Posttest
Mean
SD
Mean
SD
Domain knowledge
5.27
1.55
6.41**
1.47
Knowledge analogies
23.43
8.08
31.44
6.78
Topic interest
84.43
16.33
83.80
15.29
Surface strategies
3.78
2.16
3.42
1.99
Deep Strategies
4.95
3.15
4.39
2.68
Motivation passage
-.12
.89
.15
1.14
Strategic processing passage
.18
1.07
-.15*
.95
Note. Domain knowledge score out of 10 maximum points; Knowledge analogies score out of 42 maximum points. Topic interest
score is average out of 130 maximum points (10 possible points per interest item); Surface strategies out of 14 possible points; Deep
strategies out of 18 possible points; Motivation and strategic processing passage are z-scores.
** = t was significant at the .001 level (two-tailed).
* = t was significant at the .05 level (two-tailed).
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
Table 3
Pretest means and standard deviations on all variables for the five clusters
High Achievers
Knowledge
reliant
(N=26)
Pre
Post
6.54
6.88
(.91)
(1.31)
Interest reliant
Effortful surface
Effortful deep
(N=17)
Pre
Post
5.29
7.06
(1.11) (1.71)
**
Struggling
learners
(N=32)
Pre
Post
4.13
5.66
(1.01) (1.49)
**
(N=32)
Pre
Post
6.59
6.84
(1.01) (1.39)
(N=17)
Pre
Post
3.65
6.18
(.93)
(1.38)
**
(N=26)
Pre
Pre
4.85
6.08
(1.46) (1.09)
**
Knowledge
analogies
28.24
(7.25)
21.91
(6.21)
25.92
(6.43)
27.97
(6.66)
10.71
(4.97)
22.42
(5.71)
Topic
Interest
99.61
94.48
(11.23) (11.33)
82.69
80.03
(11.09) (14.30)
66.62
70.95
(11.69) (12.69)
96.78 92.03
(8.33) (11.67)
**
85.52
84.80
(16.05) (15.95)
78.58
82.74
(15.77) (14.69)
Surface
Strategies
6.65
(1.58)
4.00
(1.37)
**
1.72
(1.14)
2.81
(1.93)
**
3.54
(1.27)
2.85
(1.78)
2.84
(1.05)
3.16
(1.96)
5.24
(2.28)
3.94
(1.87)
4.88
(1.80)
4.35
(2.33)
Deep
strategies
6.71
(2.23)
4.53
(2.43)
**
3.37
(1.96)
3.66
(1.98)
3.73
(1.65)
3.54
(1.79)
3.47
(1.97)
4.38
(2.72)
3.24
(2.17)
4.65
(2.32)
9.92
(1.81)
5.92
(3.80)
**
Domain
knowledge
33.71
(5.89)
**
30.16
(7.19)
**
32.58
(5.46)
**
32.87
(5.58)
**
27.65
(9.02)
**
31.12
(6.92)
**
Note. Means are listed first for all cells with the standard deviations in parentheses. . Domain knowledge score out of 10 maximum
points; Knowledge analogies score out of 42 maximum points. Topic interest score is average out of 130 maximum points (10
possible points per interest item); Surface strategies out of 14 possible points; Deep strategies out of 18 possible points
* = Pretest to posttest difference with cluster significant at the .05 level (two-tailed).
* = Pretest to posttest difference with cluster significant at the .01 level (two-tailed).
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
Appendix A
Interactive Knowledge Test
Samples:
Knowing what: Declarative knowledge :: Knowing how : Procedural knowledge
Jean Piaget: Cognitive Constructivism :: Vygotsky : Social constructivism
1. No response: Because the absence of a response conveyed no information and appeared to
suggest the least amount of effort on the part of the respondent, it was considered the lowest
order category.
2. Repetition: This category of response entails the duplication of one of the three given
terms of the analogy problem. As such, it provides little information and demonstrates
seemingly little effort on the part of the respondent.
3. Nondomain response: While the individual provides a nonrepetative response to the given
problem, the answer is not in the domain of educational psychology. This type of error was
judged more acceptable than the previous ones, since it did demonstrate some originality
(nonrepetitiveness) on the part of the respondent.
4. Structural dependency: In this response, the individual used some component (e.g., root
word) of a given term in constructing a response. As hypothesized, this type of error
evidenced some effort on the part of the respondent, as well as some understanding of the
development of technical terminology.
5. Domain response: This error demonstrates that the respondent has recognized that the
expected response falls within the domain of educational psychology, and has been able to
provide an answer that is related to that domain.
6. Target variant: This type of response is rated as a high-level error, since it is a form of the
correct answer. Errors in number (i.e., singular and plural) were a common form of error in
this category, because only the correct response (excluding misspellings) were judged as
correct.
7. Correct response: Correct response or misspelling of correct response.
Running head: KNOWLEDGE, INTEREST, AND STRATEGIC PROCESSING
2
Appendix B
Strategic Processing Inventory Strategies
Surface processing strategies
Deep processing strategies
Reread parts of the text
Imaged
Skipped parts that were difficult
Reflected on reading
Skipped parts that were boring
Used context to determine meaning
Changed reading rate
Looked for salient details
Rehearsed main ideas
Mentally summarized
Ignored words or phrases not critical to understanding
Used self-questioning
Made notes or underlined important information
Related to background knowledge
Made predictions
Elaborated on main ideas