Fairfield
UniversityThis study examines learning styles of freshmen entering college and first year academic achievement to help incoming students negotiate the high school-to-college transition. Preliminary results indicate a great diversity in the learning styles at the university. Females perform better academically than males. Divergers and assimilators have a greater difficulty academically than the convergers and accommodators. Data also suggests a trend where more divergers and accommodators are on academic probation. Implications for future research and intervention strategies are discussed.
Late adolescence has been characterized as a period of change, not only in terms of individual physical and cognitive development, but also in terms of the changes that occur in the adolescents1 social contexts (Zigler and Finn-Stevenson, 1993). This period offers special challenges and opportunities for developmental researchers to examine the relationship between individual development and contextual changes. The most dramatic "normative age-graded" change (Coelho, Hamburg, and Murphey, 1968) during the late adolescent years is the transition from high school to college.
The transition from high school to college is one of the predictable, scheduled, or planned changes in an adolescent1s life. Because of this predictability, prevention researchers (Elias, Gara, Ubriaco, Rothbaum, Clabby, and Schuyler, 1986) hold that such a transition represents an important developmental crossroad with opportunities for growth and advancement. However, the high school-to-college transition can be a potentially stressful or threatening disruption (Bronfenbrenner, 1979; Felner, Farber, and Primavera, 1983). Furthermore, this predictability provides a distinctive opportunity for planned preventative interventions aimed at promoting successful adaptation (Felner, Primavera, and Cauce, 1981).
The majority of the school transition literature has focused on younger individuals, particularly those experiencing the transition from middle school to high school, with few examining the high school-to-college transition (e.g. Coelho, Hamburg, and Murphey, 1968; Barone, Trickett, Schmid, and Leone, 1993). In light of limited research in the area of college transition, it is necessary to apply the findings of transition in early adolescence. Prevention researchers have found that decreases in grades and overall class rank (Seidman, Aber, Allen, and French, 1996), and increases in course failures, absenteeism, and class cutting are usually present when students are trying to negotiate a school transition (Reyes and Jason, 1991). Students are also very stressed and anxious when they are experiencing new educational settings (Crockett, Peterson, Graber, Schulenberg, and Ebata, 1989; Felner et al., 1983; Jason, Danner, and Kurasaki, 1993). In college, many students are experiencing changes in grading standards, course load, teaching practices, peer groups, and parental contact (Coelho, Hamburg, and Murphey, 1968). The many changes in social context has sparked prevention researchers to intervene and promote a healthy transition.
Prevention researchers use two basic intervention approaches to promote healthy adjustment (Cowen, 1982). One approach is to provide social support (Zea, Jarama, and Bianchi, 1995). Social support involves providing avenues such as counseling, tutorial services, support groups, and individualized attention to individuals facing life transitions. Fairfield University has tried to use social support as a means of helping to reduce the number of students who do not adjust adequately to college life through its First Year Experience Program.
The other basic intervention technique is to build competence. Building personal competence or competence training involves teaching specific skills that are thought to mediate positive adjustment. Such competencies could involve goal setting and study skills (Cowen, 1982).
An important area of personal competence for college students is learning styles, i.e. how individuals approach learning something new. Learning styles (Kolb, Rubin, and McIntyre, 1979; Kolb, 1993) may be a means of identifying incoming students who are at risk for academic difficulty during their studies at Fairfield. The learning styles are based on the theories of Dewey, Lewin, Piaget, and the experiential learning theory (Kolb,1979). The Learning Styles Inventory (LSI) developed by Kolb (1993) provides a framework for examining an individual1s strengths and weaknesses in learning. The inventory measures an individual1s relative emphasis on four basic learning modes: Concrete Experience (CE), Reflective Observation (RO), Abstract Conceptualization (AC), and Active Experimentation (AE). Concrete Experience is learning by "feeling." Reflective Observation is learning by "watching." Abstract Conceptualization involves learning by "thinking." Active Experimentation is learning by "doing." The LSI also categorizes each learner into one of four preferred learning style types e.g. accommodator, diverger, converger, and assimilator. Accommodators combine the learning steps of CE and AE, learn primarily from "hands on experience" and adapt well to immediate circumstances. Divergers combine the learning steps of CE and RO and are best at viewing situations from many different points of view. Divergers would rather observe than take action. Divergers are creative, good at generating alternatives, recognizing problems and understanding people. Convergers combine the experiential learning modes of AC and AE. Convergers are best at finding practical uses for ideas and theories and usually do well on conventional tests (Stice, 1987). Convergers are also good at defining and solving problems. Finally, assimilators are best at understanding a wide range of information and putting it into concise, logical form. Assimilators are generally more interested in the logical soundness of an idea than its practical value. This style combines the learning steps of AC and RO.
Three studies (Nunn, 1995; Kevin and Liberty, 1975; Reifle and Edwards, 1975) have focused on using the LSI as a predictor performance in educational settings. Nunn (1995) examined the effects upon achievement and locus of control of at-risk middle school students who were enrolled in a year long learning styles/strategies intervention course. Results indicated significant improvement within the "at-risk" group in terms of grade point average and locus of control. The analysis provides tentative support for the effectiveness of a learning styles/strategies intervention and Nunn (1995) stresses further research in the area to examine the learning styles predictability in various educational settings.
Kevin and Liberty (1975) employed the LSI to predict a student's performance in an organic chemistry course using computer based instruction. The findings were at odds with the predicted correlation between choosing a major and one's learning style. Students' AC score for abstractness varied positively with grade point average. In a similar study, Reifle and Edwards (1975) failed to find any significant predictive relationship between LSI score and performance in a variety of computer based instruction.
There is a more abundant research literature focused on a comparison of teaching styles and learning styles using the LSI (Kaplan and Kies, 1995; Kruzick, Frieson, and Van Soest, 1986; Wallace, 1995). Although not empirical in nature, these descriptive studies suggest that students learn best when their learning style matches the teaching style of the professor.
The purpose of the present study is to explore relationship between students' learning style and first year college academic achievement. Should the learning style be an adequate predictor, then individuals found to be "at risk" for academic difficulties could be offered competence building services during freshman year and helped to more successfully negotiate the high school-to-college transition at Fairfield University.
Seven hundred and thirty-nine Fairfield University freshmen from the Class of 2000 (395 women and 344 men; mean age = 17.7 years) volunteered to participate as part of their orientation program. This sample represents 82.9% of the incoming freshmen body (N=891).
Learning Style Inventory (LSI).Kolb's (1993) LSI was used to evaluate an individual1s learning style type. The LSI was self-administered and consisted of twelve sentence clauses (e.g. "when I learn..." and "I learn best when...") followed by four sentence endings (e.g. "...I like to think about ideas" and "...I rely on logical thinking"). Participants were asked to rank the endings for each sentence "according to how well you think each one fits with how you would go about learning something." Subjects were asked to recall a recent situation where they had learned something new. Recalling a concrete situation was used to require the subject to think of one specific example and not waiver in their decision making. Subjects ranked the sentence endings from "4" for the sentence ending that describes how they learned "best" to "1" for the sentence ending that seems "least" like the way they learned. Subjects were also instructed not to make ties.
The inventory yielded two experiential learning subscores for "abstractness" (i.e. concrete - abstract) and "activeness" (i.e. active - reflective) in learning. Using these scores, a learning style type was determined. The four learning style types are accommodator, diverger, converger, and assimilator. Accommodators are concrete-active (i.e. "hands on") learners. Divergers are concrete-reflective (i.e. observing and feeling) learners. Convergers are abstract-active (i.e. doing and thinking) learners and assimilators are abstract-reflective (i.e. logical and observant) learners.
ProcedureThe Learning Style Inventory IIa (Kolb, 1993) was administered in a small group setting during the summer freshmen orientation program. Instructions were printed on the inventory as well as read by the group leader. Those students who did not attend the small group meeting received the LSI via campus mail and were asked to complete and return the form to the experimenter. Out of the 891 freshmen in the Class of 2000, 739 students (82.9%) returned the LSI. Out of those 739 students, 93.8% (N=693) returned a completed storable inventory and 6.2% (N=46) were completed incorrectly and were discarded.
Participants granted permission to review their academic transcripts at the end of each semester. Grade point average (GPA) and academic status (i.e. probation or good standing) were used as measures of academic achievement.
All participants who filled out the inventory correctly received a feedback form (See Appendices A-D) identifying their learning style type, learning strengths and weaknesses, and possible career matches with their learning style based on Wolfe and Kolb (1979) and Kolb's (1993) research.
The results can be broken down into two major sections. The former is a descriptive analysis of the learning styles of the Class of '00 while the latter section explores the relationship between GPA, academic status, and learning style type.
Descriptive Analysis.The data suggests that there was considerable diversity in the learning styles of the incoming freshmen. Seventeen percent were characterized as convergers (N=120). Twenty-six percent were classified as divergers (N=180). Twenty-seven percent were characterized as assimilators (N=189) and 29% were accommodators (N=196). As shown in Table 1, the gender distribution across the four learning styles were quite similar.
The learning style types were also examined in terms of academic major. The results are shown in Table 2. Based on the research of Kolb and associates (Biberman and Buchanan, 1986; Kolb, 1993; Wolfe and Kolb, 1979), natural science majors' preferred learning style should be characterized as convergers (21%) or assimilators (29%) since these are the preferred learning styles of professionals in the field. Social science majors comprising 8% of the participants were primarily categorized as assimilators with 37%. Business majors in the work force are categorized as divergers or accommodators and were represented by 28% and 30%, respectively. In the career fields of the humanities (N=299), the preferred learning style based on Kolb's (1993) research is the diverger (29%). In the nursing profession the preferred style is diverger with 23% being represented in this sample.
Participants' GPA were used as a measure of academic success at the university after the first semester marking period. Two 2 x 2 ANOVA's were performed to examine relationship between the two experiential learning axes (i.e. concrete-abstractness and active-reflective) and GPA. There was no significant difference between the concrete learners (M = 2.62, SD = .826) and the abstract learners (M = 2.63, SD = .785) in terms of GPA, F (1, 682) = .0030, p = .951. A significant difference between the active and reflective learners in terms learning style type, F (1, 680) = 10.263, p = .0014, was found. Active learners (M = 2.53, SD = .799) had a significantly higher GPA than reflective learners (M = 2.53, SD = .821 ).
A 2 x 4 (gender x learning style type) ANOVA was performed to further examine the relationship and the two experiential axes were broken down into the four learning style types. There was a significant main effect for both learning style type, F (3, 677) = 3.683, p = .012, and gender, F (1, 677), p = .047. The data suggests that divergers (M = 2.51, SD = .904) and assimilators (M = 2.57, SD = .733) are having the most academic difficulty at Fairfield during their first semester of college. Convergers (M = 2.72, SD = .853) and accommodators (M = 2.74, SD = .732) are doing better academically. The results also show that female students (M = 2.68, SD = .804) are doing better academically than males (M = 2.57, SD = .807). There was no significant interaction between learning style type and gender, F (3, 677) = 1.349, p = .257. Results are summarized in Table 3.
A chi square analysis was utilized to access any differences in academic status across the four learning styles. Twelve percent of the freshmen class (N = 84) were on academic probation and had a GPA less than 1.80. Although the chi square analysis was not significant at the .05 alpha level, X2 = 4.076, p = .240, it did suggest a trend that divergers (15.7%) were more likely to be on academic probation than convergers (11.4%). Assimilators (13.7%) were on probation more than their accommodator counterparts (8.8%), as shown in Figure 1.
In this exploratory study, the learning styles of the members of the Fairfield University Class of 2000 were quite diverse. Students at Fairfield prefer to learn in a variety of different ways with each style of learning having its own unique strengths and weaknesses. Such diversity in learning styles at the University suggests that it is important for administrators and professors to consider this diversity to more fully harness each students' learning potential.
The data regarding major and learning style are particularly interesting. Various disciplines and fields have preferred learning styles (Kolb, 1993) that match what is expected in the particular profession. For instance, research suggests that natural science majors in the fields of medicine, biology, physical science, and mathematics should be either a converger or an assimilator (Kolb, 1993; Stice, 1987; Wolfe and Kolb, 1979), yet half of students in these majors are pursuing professions where their learning style is not preferred. Perhaps the large proportion of students who drop out of the biology and premed/dental program are mismatched with the desired learning styles of the profession that they are pursuing.
A similar mismatch was found for declared business majors. Fifty-eight percent of business majors were the preferred divergers and accommodators while the other 42% of their learning styles (assimilators and convergers) contradict the preferred business norms (Biberman and Buchanan, 1986). The preferred learning style in the nursing field is that of the diverger (Wolfe and Kolb, 1979), yet 77% of the nursing students at Fairfield are either a converger, assimilator, or accommodator.
In all three of these types of majors, the university's curriculum requires freshmen to immediately begin taking important foundation courses in their declared major early in their academic careers at Fairfield. The large number of majors over represented in the wrong style may be linked to these freshmen doing poorly in their majors and subsequently changing majors a semester or two later. The university may want to reconsider having freshmen begin their major course work without exploring the core curriculum. Exploring the core curriculum may help many students choose a major that matches their learning style. Future research might follow these participants longitudinally to see if these students who have their career fields1 mismatched learning style do poorly or switch majors. Also, it would be interesting to look at the undeclared students longitudinally and see if they choose majors that match their preferred learning style.
At Fairfield, there is a definite trend in terms of what learners do best academically in their first semester. Students who are active learners and excel at conventional tests, i.e. accommodators and convergers, significantly do better than the reflective divergers and assimilators. Since more than half (53.9%) of the freshmen class were divergers and assimilators, the University should review and evaluate its means of testing and assigning grades. A curriculum that utilizes various audio-visual, extra-sensory, and reflective techniques in evaluating students performance in courses, would allow those students who do not do well on conventional educational measures to excel academically in their preferred style. The university should offer a course or at least more practice in conventional test taking strategies.
Similarly, the First Year Experience Program provides an excellent forum for building competency in an individual1s specific learning style. Since assimilators, like divergers, are doing worse academically and do not adjust immediately to their academic surroundings like accommodators, then maybe a greater emphasis should be placed on the college lifestyle changes in environment in terms of being away from home, meeting new people, learning in new ways, managing time, etc.
The data also suggests that female students were performing significantly better than their male peers. The causes behind this trend are still quite unclear but, the data suggest differences not linked to learning styles. Since learning styles do not appear to relate to the gender difference, then possibly social activities are related to the females performing better academically. Males maybe putting more time into social activities than into their studies and vice versa for females. Males may also be experiencing substance abuse related issues (Street and Kromrey, 1994). Future research in the social area is necessary to examine why females do better than males and more emphasis should be placed on time management skills during their college careers.
This study is preliminary in nature. One semester of academic work is clearly not an adequate indicator of success and failure at the Fairfield. To truly analyze if learning styles can predict academic success at the Fairfield, it is necessary to follow the students through at least the end of their freshmen year and beyond into their sophomore year.
In conclusion, this study raises a great deal of questions that require further investigation into the learning styles and their predictive value. Are Fairfield students whose learning styles mismatched with their major doing poorly academically? Will the students learning style change over time as they learn a specific subject matter? What happens if professors begin to teach from a more eclectic method and allow the conventionally bad test takers to excel in their own preferred learning style? What can the programs that are already in place do to help build student1s learning style competence? How can the university better address the needs of those students who have been shown to take more time to make a complete adjustment? Also, since SAT scores where not used in any of the preliminary analyzes, do scholastic aptitude tests fit in any way into the the learning style equation? There is a definite need to follow through and examine further the predictive nature of Kolb1s learning styles.
A copy of the paper with figures and graphs is available for download in Adobe Acrobat PDF format at http://www.cmmtypsych.net/cook/learnstyle.pdf. The Adobe Acrobat Reader web browser plug-in is required to open the PDF file.
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