Learning Styles of Postsecondary Students Enrolled in Vocational Technical Institutes
University of Arkansas
University of Arkansas
University of Arkansas
University of Arkansas
Adult students are different in many ways. They differ in their view of the world, how they make judgments, and how they form values (Hand, 1992). There is wide acceptance of individual differences in ability, motivation, values, attitudes, and personality (Perry, 1994). Therefore, the purpose of our study was to determine if there was a predominant learning style of business education, health occupations, and trade and industrial students.
"Each person is unique and complex, and yet each person is predictable, too. It's the predictable side of people that announce their style" (Guild & Garger, 1985). (Gregorc 1982a) recognizes that every human being has universal qualities common to all others and yet each is unique.
These predictable, common patterns form our typical approaches to life tasks and make up our individual styles. These basic patterns influence many aspects of personal and professional behavior. In general, they are called personality styles. When they affect learning, we refer to learning styles. When the patterns are reflected in teaching, we call them teaching styles and our particular management patterns are called leadership styles (Hand, 1992).
Within any educational institution, the acceptance of style is important.
Knowing that people see different things helps us to communicate with more depth. Knowing that people have different beliefs and values helps us to understand the various interest and needs of a diverse school population. Accepting the diversity of style can help us to create the atmosphere and experiences that encourage each individual to reach his or her full potential. (Guild & Garger, 1985)
If individuals know and understand their personal style, they will be more effective learners. (Gregorc 1985b) also believes they will increase personal responsibility for thoughts and actions and better understand their relationships with others.
Research about learning styles helps educators and learners understand the learning process. "Most models are designed around one or two characteristics on a bipolar continuum, suggesting that people are either one way or another" (Dunn, 1990).
Gregorc delineated four learning/teaching channels-concrete sequential, abstract sequential, abstract random, and concrete random (Gregorc & Butler, 1984). Perceptual abilities are the means through which a person grasps information. These emerge as abstractness and concreteness. "Ordering abilities are the ways in which you authoritatively arrange, systematize, reference and dispose of information" (Gregorc, 1985b). These emerge as sequence and randomness.
Concrete sequential students 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 students 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, audiotapes, and detailed syllabi; as well as nonrestricted environments.
Abstract random students prefer order that is nonlinear, harmonious, and non-traditional (Gregorc, 1982a). They have the natural ability to work well with people (Gregorc & Butler, 1984). These students work best when allowed to be creative and display their emotions.
Concrete random students are intuitive, insightful, and easily make transitions from fact to theory (Gregorc, 1982a). Concrete random students may be risk takers, investigative, and experimental (Butler, 1986). These students prefer a busy environment, many types of people, and enjoy the role of mentor.
Some learners are strong in one learning style. However, many adults have strengths in two learning styles. These bimodal learners are able to operate effectively in more than one channel. Their learning preferences are more varied which increases their ability to relate to instructors and classroom environments.
Stewart and Felicetti (1992) found the dominant learning style for upper division students concentrating in marketing were concrete sequential and abstract random. Likewise, Watson (1997) found the learning style of interior design students to be concrete sequential followed by abstract random. Cromwell (1996) found that concrete sequential followed by abstract random were the dominant learning styles of undergraduate education students. Harasym, Leong, Juschka, Lucier, and Lorscheider (1996) found concrete sequential followed by abstract random to be the dominant learning style of nursing students enrolled in an introductory human anatomy and physiology course. Duncan (1996) also found 42% of practical nursing students were dominant concrete sequential learners and 54% of baccalaureate nursing students were dominant abstract random learning style. Wells and Higgs (1990) found similar results with baccalaureate nursing students. The predominant learning style for first semester students was concrete sequential followed by abstract random and the predominant learning style for fourth semester students was abstract random followed by concrete sequential. In a study by Spoon and Schell (1998) 79% of students preparing for or concurrently enrolled in vocational classes were teacher-centered learners. This preference for structure indicates concrete sequential learning style.
A number of reasons exist why consideration of learning styles is important. "It is the learner that [sic] does the learning - not the teacher. For this reason . . . teaching should be centered on the learners" (Clark & Starr, 1996). Hitch and Youatt (1993) described learning style as the physical, social, and environmental elements that help an individual to learn effectively. Educators can teach to style by delivering instruction in a variety of ways, providing opportunities for students to use their style strengths or preferences, and encouraging students to show what they have learned in ways that use their style preference (Hand, 1992).
It is just as important for adult learners to know, understand, and use their personal style. This allows them to enhance their positive qualities and diminish negative ones. It also helps them to recognize the styles of others and use this information to strengthen communication and relationships. If the adult learner is armed with knowledge of learning style, they can adjust to the style of the teacher. For example, a concrete sequential learner expects concrete examples, not theories or abstractions while an abstract sequential teacher emphasizes ideas, concepts, and theories (Gregorc, 1997; Gregorc & Butler, 1984). The concrete sequential learner who recognizes these style differences could ask their abstract sequential instructor to provide some concrete examples.
In a study of community college freshman "those students who receive instruction in studying congruently with their learning style preferences achieved significantly better than those students in either limited exposure or no exposure groups" (Nelson, Dunn, Griggs, Primavera, Fitzpatrick, Bacilious, & Miller, 1993). It was also found that "providing students with a method for studying that can lead to their improved academic achievement increases the ability to exercise control over their own progress" (Nelson et al., 1993).
Purposes of the Study
The major purpose of our study was to determine the predominant learning style of students enrolled in postsecondary technical education institutes in Arkansas as measured by the Gregorc Style Delineator. The secondary purpose was to determine if there were significant differences in learning styles among students with different characteristics: (a) program area, (b) work experience, and (c) gender.
A representative sample of the postsecondary students in business education, health occupations, and trade and industrial programs in all postsecondary institutes in Arkansas during the spring of 1997 was asked to participate in the study by attending a Gregorc learning styles workshop. Before agreeing to participate, students were informed that a professor from the university would be conducting a learning styles workshop, that attendance was not mandatory, and that the results would not be published identifying individual students.
The workshop began with a presentation covering general information about Gregorc learning styles and their importance in understanding the way we learn. The instrument was then administered to the participants. The professors adhered to the instructions provided by the Gregorc Style Delineator: Development, Technical and Administration Manual. After the instrument was scored, the professors described characteristics and instructional preferences associated with each mediation channel (concrete sequential, concrete random, abstract sequential, and abstract random). The workshop 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? and What if my learning style is very different than my teachers?"
All students who were in attendance on the day of the workshop participated in this study. A total of 322 students at 10 postsecondary institutions completed the Gregorc Style Delineator. No students completed the instrument more than once. Although records were not kept on how many students were absent the day of the study, we believe that the 322 students who participated were representative of the students enrolled in the classes.
One hundred seven (33.2%) were business education students, 127 (39.5%) were health occupations students, and 88 (27.3%) students were enrolled in trade and industrial programs. The majority of the students, 228 (70.8%) were female, while 94 (29.2%) were male. Only 13 males were enrolled in business education and health occupations while 7 females were enrolled in trade and industrial programs. The majority of the students, 199 (62%), had been working 1-10 years; 66 (20.6%) reported 11-20 years of work experience; and 35 (10.9%) reported 21 or more years of work experience. Only 21 (6.5%) of the students reported no work experience.
The Gregorc Style Delineator (Gregorc, 1985a) was used for this study because it is a self-analysis instrument for adults as well as the results are easy to understand. The instrument not only contains a word matrix; it also contains key ideas about learning styles, the purpose of the style delineator, and characteristics of the four mediation channels.
We participated in a Gregorc Learning Style workshop before administering the Gregorc Style Delineator (Gr egorc, 1985a) to the students. After we explained the purpose of the delineator and instructions were given regarding completing the instrument, students were asked to score the Gregorc Style Delineator matrix. Scores were transferred to a learning styles summary sheet that we developed. Students also recorded their program area, gender, and years of wage earning experience on the summary sheet. Students were not identified by name or school; only group data are reported in this study.
The Gregorc Style Delineator is a research-based self-analysis instrument, which consists of 10 groups of words in a word matrix. Each group contains four words, and the respondents rank them 4, 3, 2, and 1; 4 being the most descriptive, 1 being the least descriptive. The words are indicators of the four learning styles: concrete sequential, concrete random, abstract sequential, and abstract random. To rank order the words in a set, the respondents were asked to react to their first impression. There were no right or wrong answers. The pre-arranged word matrix in the instrument determined the total score for each learning style area. A total score of 27 to 40 points indicated a dominant learning style. Intermediate style scores ranged from 16 to 26 points and low style scores ranged from 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).
As a result of this test, some students were also designated as bimodal. A bimodal score was determined when a student was dominant in two learning style categories. When the two highest scores are within five or less points of each other and both scores are in the dominant range, then the student is bimodal in learning style (Gregorc, personal communication, May, 28, 1997).
Data were analyzed using the SPSS statistical computer package (Statistical Package for the Social Sciences, 1997). Frequencies, percentages, and mean scores were tabulated to determine the students' predominant learning styles. An analysis of variance (ANOVA) was used to determine differences between learning styles and students' years of work experience. T-tests were used to determine if there was any relationship between gender and learning styles. Chi-square analyses were conducted to determine if gender and program area had an effect in which the learning styles of students were bimodal. Fisher's test of Least Significant Differences was conducted to make pairwise comparisons among the means of the three program areas.
Predominant Learning Style
The major purpose of our study was to determine if there was a predominant learning style of business education, health occupations, and trade and industrial students. We found the concrete sequential style to be the predominant learning style in the overall group. Of those students with one dominant style, 78 (24%) of all students had a dominant style of concrete sequential, followed by concrete random 41 (13%), abstract random 40 (12%), and abstract sequential 13 (4%) (see Table 1). The results of this study are similar to studies by Stewart and Felicetti (1992), Watson (1997), Cromwell (1996), Harasym et al. (1996), Duncan (1996), Wells and Higgs (1990), and Spoon and Schell (1998).
One hundred thirty-seven (43%) students were found to be bimodal with dominance in two learning style areas (see Table 1). Bimodal exists when a student has two dominant scores within five points or less of each other and both scores are in the dominance range. Two students were trimodal with dominance in three learning style areas. Eleven students did not have a dominant score in any learning style area (see Table 1).
|Frequency and percentage of learning style and program areas|
|Learning Styles||Business education||Health Occupations||Trade & Industrial||n||%|
The concrete sequential mean score for all three program areas was 27.0. Overall, the health occupations students have the highest concrete sequential mean scores (27.4) followed by business education (26.9), and trade and industrial (26.5) (see Table 2).
The mean scores of the business education students in each learning style in rank order are concrete sequential (26.9), abstract random (25.8), abstract sequential (23.9), and concrete random (23.4). The mean scores for the health occupations students are concrete sequential (27.4), abstract random (26.1), concrete random (23.4), and abstract sequential (23.1). The mean scores for the trade and industrial students are concrete sequential (26.5), concrete random (25.9), abstract sequential (24.9), and abstract random (22.7) (see Table 2).
|Mean and standard deviation for learning style and program|
|Business Education||Health Occupations||Trade and Industrial||Overall|
Learning Styles by Program Area
As shown in Table 1, of the 78 students whose dominant learning style was concrete sequential, 26 were in business education, 37 were in health occupations, and 15 were in trade and industrial. More trade and industrial students were dominant in concrete random than concrete sequential. Of the 45 trade and industrial students dominant in a single style, 17 identified concrete random as their dominant learning style.
Of the 137 students indicating bimodal learning scores, 44 were in business education, 54 were in health occupations, and 39 were in trade and industrial. In business education and trade and industrial the most common bimodal pairing was concrete sequential-abstract sequential. The most common pairing in health occupations was concrete sequential-abstract random (see Table 3).
|Bimodal pairings by program areas and gender|
|Program Area and Gender||CS-AS||CS-AR||CS-CR||AS-AR||AS-CR||AR-CR||Total|
|Trade and Industrial||14||3||11||5||2||4||39|
In order to find significant differences among the program areas, the chi-square test was performed. For this test, the number of bimodal students was added to each category to reflect the total numbers in that particular category (see Table 4).
|Frequency and percentage of learning styles of single area dominant and bimodal students by program areas|
|Learning Style||Business Education
|Trade and Industrial
|Totals and percentages exceed 100% because of the inclusion of two learning styles for bimodal students.|
The results of the test indicated that the program areas did not differ significantly in the proportion of the concrete sequential category (x2 (2) = 2.11; p = .35). The program areas differed significantly in the proportion of the abstract sequential category (x2 (2) = 6.28; p = .043). Follow-up analysis revealed that the health occupations students were significantly lower than the other two programs (x2 (1) = 5.44; p = .02) in the abstract sequential category. In the abstract random category, the three program areas differed significantly (x2 (2) = 11.38; p = .003). Further analysis showed that the trade and industrial students were significantly lower than the other two program areas (x2 (1) = 10.12; p = .002) and the health occupations students were significantly higher (x2 (1) = 6.51; p = .0107) in the abstract random category. For the category of concrete random, the program areas also differed significantly (x2 (2) = 8.81; p = .01) with the trade and industrial students being higher than the other two program areas (x2 (1) = 8.80; p = .003).
Within the concrete sequential area the mean score was greater for students with more work experience. For students with no work experience the mean concrete sequential score was 25.5. For those with 1-10 years of work experience, the mean score was 26.6; with 11-20 years the mean increased to 28.1; and for those with more than 21 years of work experience the mean was 28.4 (see Table 5).
The results of the ANOVA performed on years of work experience and learning style indicated significant differences between students with years of work experience and the concrete sequential learning style (see Table 6). The data did not reveal significant differences at the .05 level with the other three learning styles.
As noted in Table 7, t-tests revealed significant differences between genders in the abstract sequential, abstract random, and concrete random learning styles categories. In the category of abstract random, the mean score for females was higher than males. In the categories of abstract sequential and concrete random, the mean score for males was higher than females.
|Mean and standard deviation for learning style and work experience|
|Note: CS=concrete sequential; AS=abstract sequential; AR=abstract random;
|Analysis of variance for learning style and work experience|
|* p = < .05|
|Mean and standard deviation for learning style and gender|
|Style and Gender||x||SD||P Value||T|
|Note: Males n = 94; females n = 228
* p < .05
Discussion and Recommendations
This study was undertaken to determine the predominant learning style of students enrolled in postsecondary institutes in Arkansas as measured by the Gregorc Style Delineator (1985a), as well as, to determine if there were significant differences in learning styles among students with different characteristics: (a) program area, (b) years of work experience, and (c) gender. The survey instrument focused upon the four major learning styles: (a) concrete sequential, (b) concrete random, (c) abstract sequential, and (d) abstract random. Students with two dominant areas were designated as bimodal.
The findings and review of literature of this study indicated that concrete sequential is the most predominant learning style. However, there is enough variety in the learning styles of postsecondary students that teachers should recognize the importance of accommodating and encouraging students with different learning styles.
While caution is suggested in generalizing these findings, a dominant concrete sequential individual may be more likely to return to school and retrain for a different career after years of work experience. This notion is supported by the finding that there is a significant relationship between years of work experience and the concrete sequential learning style.
The dominant learning style of students by gender seemed to mirror the dominant learning style of the program areas of health occupations and trades and industrial. This raises the question "Are people attracted to a specific profession because of their gender or because of their learning style?" Additionally, this study suggests that many students have more than one dominant learning style. One may conclude that these students have a broader acceptance of varied teacher instruction. Each teacher should identify his or her own learning style as well as the learning styles of the students. This awareness will help educators and students understand the diversity of people and encourage them to accept others as they are. This understanding may help students learn to work cooperatively with others in many different situations.
Educators can plan class activities to reflect a variety of learning styles. Observing teachers with different learning styles will help educators evaluate their own style and see the effect of different styles of teaching. Team teaching with others with a different learning style will expose students to a variety of styles and help both learn to vary their own way of teaching.
In the classroom and lab, encouraging students to work with others who exhibit different learning styles creates synergy. Students learn to be more tolerant of others' learning styles when doing group work with those that think and react differently to problem-solving situations.
This study reinforces research findings that show there are a variety of learning styles. However, it raises two additional questions: What is the relationship of career choices to an individual's learning style? Is the learning style of technical students representative of students in other educational settings?
Orr is an Assistant Professor; Park is an Associate Professor; D. Thompson is an Assistant Professor; C. Thompson is a Professor. They are all in the Department of Vocational and Adult Education at the University of Arkansas, Fayetteville.
Butler, K. A. (1986). Learning and teaching style in theory and practice. Columbia, CT: The Learner's Dimension.
Clark, L. H., & Starr, I. S. (1996). Secondary and middle school teaching methods. Englewood Cliffs, NJ: Prentice-Hall.
Cromwell, R. R. (1996). Who are we as instructional leaders: A statistical analysis. (ERIC Document Reproduction Service ED 393 830)
Gregorc, A. F. (1982a). An adult's guide to style. Columbia, CT: GregorcAssociates.
Gregorc, A. F. (1982b). Gregorc style delineator: Development, technical and administration manual. Columbia, CT: Gregorc Associates.
Gregorc, A. F. (1985a). Gregorc style delineator: A self-assessment instrument for adults. Columbia, CT: Gregorc Associates.
Gregorc, A. F. (1985b). Inside styles: Beyond the basics. Columbia, CT: Gregorc Associates.
Gregorc, A. F. (1997). Gregorc mind styles learner characteristics extenda-chart. Columbia, CT: Gregorc Associates.
Gregorc, A. F., & Butler, K. A. (1984). Learning is a matter of style. Vocational Education Journal, 59 (3), 27-29.
Guild, P. B., & Garger, S. (1985). Marching to difference drummers. Alexandria, VA: Association for Supervision and Curriculum Development.
Hand, K. L. (1992). Teaching to learning styles: Leaders guide. Alexandria, VA: Association for Supervision and Curriculum Development.
Harasym, P. H., Leong, E. J., Juschka, B. B., Lucier, G. E., & Lorscheider, F. L. (1996). Relationship between Myers-Briggs type indicator and Gregorc style delineator. Perceptual and Motor Skills, 82, 1203-1210.
Hitch, E. J., & Youatt, J. P. (1993). Communicating home economics content: A guidebook for professionals. South Holland, IL: Goodheart-Willcox.
Nelson, B., Dunn, R., Griggs, S. A., Primavera, L., Fitzpatrick, M., Bacilious, Z., & Miller, R. (1993). Effects of learning style intervention oncollege students' retention and achievement. Journal of College Student Development, 34 (5), 364-369.
Perry, C. (1994). Students' learning styles: Implications for teacher education. (ERIC Document Reproduction Service No. ED 375 136)
Spoon, J. C., & Schell, J. W. (1998). Aligning student learning styles with instructor teaching styles. Journal of Industrial Teacher Education, 35 (2), 41-56.
Stewart, K. L., & Felicetti, L. A. (1992). Learning styles of marketing majors. Educational Research Quarterly, 15 (2), 15-23. [Online]. Available: http://ericae.net/db/riecije/ej480418.htm [This is no longer a valid link]
Watson, S. (1997). Learning style preferences: A comparison of traditional and nontraditional interior design students. Unpublished Dissertation, University of Arkansas, Fayetteville.