Preferred Learning Styles of Working Adults
Dale E. Thompson
Terri Owens
Professor Cecelia Thompson,
University of Arkansas, USA
Learning styles are often used to describe and explain the learning process. Knowles, Holton,
and Swanson (1998) describe learning style as “the broadest range of preferred modes and
environments for learning” (p. 162). Dunn and Griggs (2000) state that “learning style is a
biologically and developmentally determined set of personal characteristics that make the
identical instruction effective for some students and ineffective for others.” (p. 9)
In contrast, Gregorc (1985a) uses learning styles to describe “behaviors, characteristics and
mannerisms which are symptoms of metal qualities used for gathering data from the
environment.” (p. 179) Gregorc concluded that “cognition is bipolar: it results from dualities
related to perceiving, ordering, processing and relating of information” (Terry 2002:157) His
work with adults expands the concept of learning styles to lifelong learning and characteristics of
persons which enhance or is detract from working relationships and productivity in the
workplace.
Claxton and Murrell (1987) also agree that considering learning styles in the workplace is useful.
“It enables administrative leaders to be more insightful about using staff members in ways that
call on their greatest strengths.” (p. iv)
Gregorc (1982) outlines four learning channels: concrete sequential, abstract sequential,
concrete random, and abstract random. Concrete sequential (CS) individuals relate best to the
physical, hands-on world and think in ways that are methodical, ordered, and predictable
(Gregorc 1982a). They prefer hands-on activities and may also have a tendency for perfection.
Abstract sequential (AS) individuals mentally outline, correlate and compare, and categorize data
in a manner unsurpassed by other styles using their analytical abilities (Gregorc 1982a). They
prefer guided assignments and detailed plans, as well as nonrestricted environments. Abstract
random (AR) individuals prefer order that is nonlinear, harmonious, and non-traditional (Gregorc
1982). They have the natural ability to work well with people (Gregorc and Butler 1984). These
individuals work best when allowed to be creative and display their emotions. Concrete random
(CR) individuals are intuitive, insightful, and easily make transitions from fact to theory
(Gregorc 1982). Concrete random individuals may be risk takers, investigative, and experimental
(Butler 1987). These individuals prefer a busy environment, many types of people, and enjoy the
role of mentor.
Some people are strong in one learning style and referred to as unimodal. However, many
individuals have strengths in two learning styles. These bimodal individuals are able to operate
effectively in more than one channel. Their learning preferences are more varied which increases
their ability to relate to others.
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Purpose of the Study
The purpose of this study was to determine the differences in predominate learning styles of
adults employed in business, health, manufacturing and education occupations. A secondary
purpose was to determine if there were significant differences in learning styles among adults
with different characteristics, including occupation, gender, years of work experience and
education.
Methodology
Instrumentation
The Gregorc Style Delineator (Gregorc 1985b) was used for this study because it is a researchbased self-analysis instrument for adults as well as the results are easy to understand. The
instrument contains a word matrix, which is the means for identifying a person’s learning style.
In addition to the matrix, it contains key ideas about learning styles, the purpose of the style
delineator, and characteristics of the four mediation channels.
The word matrix consists of 10 groups of words. Each group contains four words, and the
participants rank them 4, 3, 2, and 1; 4 being most descriptive, 1 being least descriptive. The
words are indicators of the four learning styles. To rank order the words in a set, the participants
react to their first impression. There are no right or wrong answers. The prearranged word
matrix in the instrument determines the total score for each learning style. A total score of 27 to
40 points indicates a dominant learning style. Intermediate style scores range form 16 to 26
points, and low style scores range form 10 to 15 points. The reliability of the instrument was
assessed in terms of internal consistency using standardized alphas and in terms of stability using
a test retest correlation coefficient. The standardized alpha range is 0.89 to 0.93. The correlation
coefficients between the first and second test range were from 0.85 to 0.88 for the four scales
(Gregorc, 1982b).
Data Collection
The researchers collected data at a number of workshops and classes attended by working adults.
The workshops and classes began with a presentation covering general information about
Gregorc learning styles and their importance in understanding the way we learn new skills
needed on the job and how we work with other people.
After explaining the purpose of the delineator and giving instructions regarding completing the
instrument, participants were asked to score the Gregorc Style Delineator matrix. The researchers
adhered to the instructions provided by the Gregorc Style Delineator: Development, Technical
and Administration Manual (Gregorc 1982b) while administering the instrument.
After the Gregorc Style Delineator (Gregorc, 1985b) was administered, the researchers described
characteristics and learning preferences associated with each mediation channel (concrete
sequential, abstract sequential, concrete random, and abstract random). The workshop and
classes concluded with a question and answer session. Frequently asked questions included:
“What if I have a high score in more than one area? Does my learning style change as I get
older? Can I change my learning style?”
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Participants’ scores were transferred to a learning styles summary sheet by the researchers. They
also recorded the participants’ occupational area, gender, and years of work experience on the
summary sheet. Participants were not identified by name; only group data are reported in this
study.
Findings
Two hundred fifty seven employed adults participated in the study. They were all residents of
the state of Arkansas. One hundred ten (forty-three percent) were working in business careers
such as clerical, computer technology, office management, secretarial or banking. Twenty-six
(ten percent) were employed in the health or medical field such as nursing, nursing assisting,
therapist, or lab technicians. Fifty-five (twenty-one percent) were engaged in
manufacturing/trade and industrial areas such as transportation coordinator, supervisor in a
factory, and food processing. Fourteen (five percent) were in education positions such as youth
services, and social services, employment training and non-profit. Fifty-two (twenty percent)
others worked in other areas such as law enforcement, agriculture, food service, traffic
management, and state government.
Predominant Learning Style
The major purpose of our study was to determine if there was a predominant learning style of
adults working in different occupational areas. Of the 257 cases, 124 (forty-nine percent) were
unimodal; 129 (fifty-one percent) were bimodal. Four participants did not exhibit a dominant
learning style. For all respondents, there was no predominant learning style.
In the whole study, more than half of the participants were dominant in the concrete sequential
learning style. One hundred forty five (fifty-six percent) of the participants scored as dominate
in the concrete sequential learning style. The numbers of participants dominant in the other three
learning style areas were evenly distributed. Eighty-three (thirty-three percent) of the
participants were dominant in abstract sequential. Eighty (thirty-one percent) participants were
dominant in concrete random. Eighty-six (thirty-four percent) were dominant in abstract
random. Since fifty-one percent of the participants were bimodal and dominant in two areas, the
percentages reported in the findings add up to more than one hundred percent.
Occupation
Many of the 110 participants employed in business careers were concrete sequential. Fifty-four
(forty-nine percent) were dominant in concrete sequential. Equal numbers of business
participants were dominate in the random areas. Thirty-one (twenty-eight percent) were abstract
random, while 32 (twenty nine percent) were concrete random. Only 19 (seventeen percent)
business participants were dominant in abstract sequential.
The percentages of dominant areas were similar for participants employed in the health or
medical field. Concrete sequential was dominant for 16 (sixty-two percent) of participants,
while random areas were equally divided with 7 (twenty-seven percent) abstract random and 6
(twenty-three percent) concrete random. Only 5 (nineteen percent) were abstract sequential.
Like the previous two areas, approximately half (29, fifty-three percent) of participants in
manufacturing/trade and industrial areas were dominant in concrete sequential. Unlike the
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previous occupational areas, more manufacturing/trade and industrial participants were dominant
in abstract sequential (14, twenty-five percent) than abstract random (14, twenty-five percent)
and concrete random (9, sixteen percent).
Participants employed in education were quite different. More were dominate in concrete
random (7, fifty percent). The three remaining areas were approximated the same with 5 (thirtysix percent), followed by concrete sequential (4, twenty-nine percent) and abstract sequential (3,
twenty-one percent)
The dominant area for other participants, who were employed in a range of jobs, was concrete
sequential. Twenty-six (fifty-two percent) were dominant in concrete sequential Eighteen
(thirty six percent) were dominate in abstract sequential, 15 (thirty percent) were dominate in
concrete random, and 12 (twenty-four percent) were dominate concrete random.
People in the various occupations vary in the degree to which they have a single dominant
concrete-sequential or concrete-random learning style, but score similarly on any of the other
learning styles. Employees in education-related occupations have lower concrete sequential style
scores that those in any of the other occupational groups. (p<.05) Educators also score higher on
the measure of concrete random learning style that those in health and manufacturing (p<.05).
Gender
The majority of participants were female (174, sixty-nine percent). Seventy-nine (thirty one
percent) were male. Table 1 reports the dominant areas by gender
Table 1: Dominant Learning Style by Gender
Concrete Sequential
Abstract Sequential
Concrete Random
Abstract Random
Female
Male
85 (49%)
32 (18%)
46 (26%)
55 (32%)
43 (54%)
27 (34%)
22 (28%)
14 (18%)
Both males and female were more often dominate in concrete sequential. However, they differed
on the abstract areas. Females were more often dominant in abstract random, while males were
more often dominate in abstract sequential.
Men’s scores on abstract-sequential learning style exceeded those of women (p<.05). Women’s
abstract-random single dominant learning style scores were higher than men (p<.05).
Higher percentages of men than women exhibit bimodal learning styles. The majority of women
in this study have a single learning style (90, fifty-two percent), while the majority of men have
at least two (45, fifty-seven percent). Larger shares of women with bimodal styles are
characterized by a concrete-sequential and abstract-random combination (CS-AR) (30, thirty-six
percent) and an abstract-random and concrete-random combination (AR-CR) (21, twenty-five
percent). In both cases, women compose eighty percent of those with CS-AR and AR-CR
combinations. Men compose eighty-eight percent of those whose learning styles are a
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combination of abstract sequential and concrete-random (AS-CR). The differences were
statistically significant (p<.05).
Experience and Education
People with twenty-one years of experience or more had higher concrete-sequential learning
style scores than those with one to ten years of experience (p<.08). The participant’s level of
education was unrelated to dominate learning style.
Discussion
Within this group of working adults, two distinct and nearly-equal groups emerged. One group
was dominant in a single style (49%) while the other group was dominant in two styles (51%).
Of those dominate in only one learning style, the majority scored highest in concrete sequential.
These results were similar to earlier studies of postsecondary instructors and student in at
vocational technical institutes in Arkansas. (Orr, Park, Thompson & Thompson, 1999;
Thompson, Thompson, Orr, Thompson & Park, 2002).
Over half of the employees in this study were bimodal. More men were bimodal than women.
These bimodal individuals are able to operate effectively in more than one style. This increases
their ability to relate to different work environments and coworkers.
This study found differences between male and female workers. These differences could be
considered when assigning tasks in the workplace. Women might have a preference for more
random, nonlinear activities and working with people. In contrast, men may prefer analytical
tasks. However, team members with diverse learning styles could contribute effectively to the
overall accomplishment of tasks.
In any work environment, a group of people will exhibit all of the learning style characteristics
described by Gregorc. These characteristics will affect the talents of employees, how individuals
work together, what encourages growth, individual dislikes, and what creates stress among
employees. It is important to consider these personal characteristics and modes of thinking when
training and supervising employees,
References
Butler, K.A. (1987) Learning and Teaching Style in Theory and Practice, Columbia, CT: The
Learner’s Dimension.
Claxton, C.S. and Murrell, P.H. (1987) Learning Styles: Implications for Improving Educational
Practices, College Station, Texas: Texas A&M University, Association for the Study of Higher
Education.
Dunn, R. and Griggs, S.A. (2000) Practical Approaches to Using Learning Styles in Higher
Education, West Port, CT: Bergin and Garvey.
Gregorc, A.F. (1982) An Adult’s Guide to Style, Columbia, CT: Gregorc Associates.
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Gregorc, A.F., and Butler, K.A. (1984) ‘Learning is a matter of style’, Vocational Education
Journal 59 (3):27-9.
Gregorc, A.F. (1985a) Inside Styles: Beyond the Basics, Columbia, CT: Gregorc Associates.
Gregorc, A.F. (1985b) Gregorc Style Deliniator: a Self-assessment Instrument for Adults,
Columbia, CT: Gregorc Associates.
Knowles, M.S., Holton III, E.F., and Swanson, R.A. (1998) The Adult Learner: the Definitive
Classic in Adult Education and Human Resource Development, Houston: TX: Gulf Publishing
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Orr, B., Park, O., Thompson, D., and Thompson, C. (1999). Learning styles of postsecondary
students enrolled in vocational technical institutes. Journal of Industrial Teacher Education
36(4); 5-20.
Terry, M. (2002) ‘Translating learning style theory into developmental education practice: an
article based on Gregorc’s cognitive learning styles’, Journal of College Reading and Learning
32(2):154-76.
Thompson, D., Orr, B., Thompson, C., and Park, O. (2002). Preferred learning styles of
postsecondary technical institute instructors. Journal of Industrial Teacher Education 39(4); 6378.
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