Editor’s Note: This research compares face-to-face and online learning for students with different learning styles. It finds that learning style is not significant in determining which mode of instruction is chosen nor is it a significant factor in learning effectiveness and performance.

The Relationship between Student Learning Style, Selection of Course Delivery Format, and Academic Performance

Chris Brittan-Powell, Harry Legum, and Elias Taylor

Abstract

The goal of this research was to investigate the role of student learning styles on student selection of, and performance within, academic coursework which was delivered in either a fully online or in a traditionally face to face format. Kolb’s (2005) theory of individual learning styles was used to designate participants’ preferred cognitive strategy for incorporating new knowledge and experiences. Results show that no unique relationship exists between student learning style and their selection of a traditional face to face course compared to a fully online course. Furthermore, student performance both within and across each course delivery type was not influenced by learning style. Implications of these findings are discussed.

Keywords: online, Kolb, learning styles, distance learning, education, instructional technology, personality, academic performance, learning technologies.

Introduction

The educational environment has been altered dramatically by introduction and advances of instructional technologies (IT). These technologies have changed the nature of the educational experience for both students and educators. While the implementation of IT has been significant, evaluation of its effectiveness has not. We are just beginning to understand the various pedagogical factors involved in the effective usage of technologies by educators and students.

The incorporation of IT into the delivery of academic coursework may take many forms. Among other possibilities, course delivery includes two distinct modalities: the fully online modality and the traditional face to face (f2f) modality. While there are variations in the structure in both traditional f2f and fully online modalities, each of these two modalities tends to have unique characteristics. Foremost among the differences is that fully online courses currently tend to be highly dependent upon the use of an internet based course management system (CMS) through which classroom interaction and communication occur. In contrast, interactions and communication between students and instructors in f2f classes generally occurs in person within a traditional classroom setting. Therefore, the context within which student learning occurs for either of these course delivery modalities is quite distinct. Thus, it is important to determine if there are psychological and pedagogical factors that differentially impact student learning in each of these respective course delivery modalities.

Educational specialists have identified students’ learning style as key to their acquisition of new knowledge and skills. Kolb’s (1976) theory of experiential learning styles has been one of the central theories used by educators to understand the influence of the nature of the educational environment (course modality) on student learning (Wierstra & DeJong, 2002). His typological theory was chosen for this study because it focuses on the interaction between the learner and the learning environment (Kolb & Kolb, 2005). Kolb’s theoretical perspective reasonably paralleled our curiosity as to whether students’ learning style influenced their choice of, and performance in, course delivery modalities (f2f and fully online).

David Kolb states that learning occurs by individuals going through a four phase cycle of learning. These phases are: Concrete Experiencing, Reflective Observation, Abstract Conceptualization, and Active Experimentation. While all students are assumed to utilize all four of these phases, his theory presumes that students will typically have a stronger preference a particular combination of them. This preference will then reflect the manner in which they approach acquiring new knowledge and skills (Kolb, 1984).

Students’ preference for the Concrete Experience phase reflects their desire to learn by experiencing. Those with preferences for the Reflective Observation phase enjoy learning by reflecting on the learning material. Students showing a preference for the Abstract Conceptualization phase best learn by logically thinking about a learning situation. Those with preferences for the Active Experimentation phase find that learning by doing works best for them (Kolb, 1976).

The Concrete Experiencing and Abstract Conceptualization phases are deemed to be at opposite ends of the same learning continuum. Additionally, the Active Experimentation and Reflective Observation phases are viewed as being at polar opposites of another distinct learning continuum. Students’ personal learning preferences among these four phases vary. While Kolb posits that effective learning requires students’ use of some degree of all four learning phases, he states that most learners predominantly utilize particular combinations of two of them. These two predominant phases are then combined to identify the respective student’s learning style. Utilizing Kolb’s mapping of students’ preferences results in four possible student learning style types: Diverging, Assimilating, Converging, and Accommodating (Kayes, 2005). A fuller description of each of these learning types is presented below:

·         The Diverging Type is based on the students’ preference for uses of the paired learning phases of Concrete Experience and Reflective Observation. As noted by Kolb, students prefer a more esoteric kind of course where they are allowed to use/combine imaginative observation and brainstorming with others. Students characterized by this learning type like a more personalized educational environment with open minded instructors and classmates. They also enjoy working in a group on course projects and information gathering.

·         The Assimilating Type reflects students’ preferences for the learning phases of Reflective Observation and Abstract Conceptualization. Kolb states that students with this learning type enjoy abstract ideas and concepts and tend to seek out more of the logical soundness, rather than concrete value, of an idea. Additionally, they generally prefer a course structure utilizing lectures, readings, and the exploration of analytical models in order to allow them to fully think through the material.

·         The Converging Type combines the learning phases of Abstract Conceptualization and Active Experimentation. An apt description of this type of student is that they are very practical and are problem solvers. They tend to prefer to focus on particular tasks that need to be resolved and are not highly interested in using social networks toward this end. Individuals interested in technologically oriented careers commonly fit into this type.

·         The Accommodating Type pairs the learning phases of Active Experimentation and Concrete Experience. Students with this learning type like action oriented learning settings that require hands on experience. They tend to prefer the use feelings rather than a logically oriented approach to solve problems. Kolb notes that this style of learner would prefer to do field work rather than focus on abstract lecture material
(Kolb & Kolb, 2005).

The distinctiveness of the above learning types highlights the variability in students’ experiences of formal learning situations. Educators seek to understand what instructional strategies might best serve students. In this vein, we desire to know not only if IT is effective, but if it is differentially effective with different types of student learners. Such knowledge could potentially help us determine how our pedagogy could strategically tailor our IT in order to increase the likelihood of students’ academic success. Furthermore, given that students tend to intuitively know their own learning preferences, we evaluated if they tended to choose one course delivery modality over another contingent upon Kolb’s theorized model. Given this reasoning, the specific research questions guiding this study were:

a)      Does students’ Kolb learning style influence their choice of course delivery format?

b)      Does Kolb learning style differentially influence students’ academic performance contingent upon the respective course delivery format?

Method

Participants

This study was undertaken at a Historically Black College/University (HBCU) where the majority of students (approximately 81%) are women. A total of 108 students participated in this study. All of these students were enrolled in the same advanced undergraduate class (Psychological Assessment) and in all cases had the same instructor. Students were enrolled in one of two distinct sections/formats of the course. Each of these two sections had unique course delivery formats – fully online or traditional face to face (f2f). At the time of registration, all students were able to self-select which one of these two course delivery formats they desired. Data for each of these respective formats were collected during two consecutive semesters. Seventy-two participants were enrolled in a fully online section of the course which used a CMS (Blackboard) and a lecture capture system (Tegrity). The lecture capture system (LCS) provided students with video-recorded lectures via the internet. This could be watched by students either by video-streaming to their computer or via podcasts. All the lectures covered the identical set of PowerPoint lecture material. At the beginning of each semester, online students received comprehensive training on use of all IT, and IT assistance was easily available either through the instructor or established University IT services for the duration of the course. Students who took the course in this manner were labeled the Online Students. The age of the Online Student group ranged from 19 to 61 years (M = 25. 55 years, SD = 3.87 years). The majority of participants were women (91%). Furthermore, most participants identified themselves as Black (93%), with the remaining (7%) identifying as White.

In addition, 36 students received instruction and evaluation using a traditional f2f classroom meeting format. While Blackboard was used to post relevant course information to students in the f2f group, student usage of it was not required. This group was labeled the f2f Students. The age of the f2f Student group ranged from 20 to 54 years (M = 26.14 years, SD = 2.77 years). The majority of participants were women (82%). Furthermore, most participants identified themselves as Black (95%), with the remaining (5%) identifying as White. An earlier study comparing only student grades across both delivery formats (Fully Online and f2f) of this course found no statistically significant difference due to course delivery format (Brittan-Powell, 2008).

Measures

Academic Performance. Student end of the semester numerical grade served as the measure of academic performance. This value could range between 0 and 100. All students, across both conditions, received identical midterm and final exams with both of these exams given in a face to face setting. In addition, students in both course delivery formats received an identical set of 10 quizzes during the semester all of which were taken through the CMS under identical conditions. Test security was maintained throughout the study.

Kolb Learning Style Inventory, Version 3.1 (KLSI 3.1) (Kolb & Kolb, 2005)

The KLSI 3.1 was used to measure individuals’ learning styles in both educational settings and everyday life settings. Based on participants’ rank ordered responses to twelve items, they are categorized as having one predominant learning style out of the four that are possible in the Kolb model. The four possible learning styles are respectively: Accommodating (AC), Assimilating (AS), Converging (CO), and Diverging (DI) and are fully described above. Across four distinct studies, internal consistency reliabilities for the respective KLSI 3.1 subscales have been shown to vary between .70 to .84 (Kayes, 2005; Rubie & Stout, 1991; Veres, Sims, & Locklear, 1991; Wierstra & DeJong, 2002). Furthermore, Veres et al. (1991) found test-retest reliabilities for all four subscales of the KLSI 3.1 to be above .9.

Design and Procedures

This study utilized a Causal-Comparative / Ex-Post Facto research design. At the time of registration, all students were able to self-select the course delivery format they desired (f2f or fully online). Both course delivery formats used identical sets of PowerPoint lecture notes to deliver the course content and all additional materials were available to all students. All students, in both conditions, took an in-person final exam, at the end of which they were provided an optional extra credit opportunity in which a brief survey containing a brief demographics page and the KLSI 3.1 was administered.

Results

The first research question sought to evaluate whether students’ preference for course delivery modality (Fully Online vs. f2f) was contingent upon their Kolb learning style. Given the nature of the research question looking across two nominal treatment conditions within which students could be determined as one of four nominal learning styles, a 2 by 4 Chi-Square test was used. Results of this inferential test were non-significant, χ² (3, N = 108) = 3.22, p = .21, suggesting that students’ learning style did not influence their selection of taking a course in either a f2f or fully online format.

The second research question investigated whether students’ Kolb learning style influenced their academic performance (course grade) differentially contingent upon whether they took the course in either an f2f versus a fully online delivery format. A 2 x 4 ANOVA revealed no significant differences for either the main effect of Course Delivery Type, F(1, 100) = 1.32, p = .85, or the main effect of Kolb learning style, F(3, 100) = 1.44, p = .47, or the interaction term F(3, 100) = 1.76 , p = .69. These results suggest that there is no relationship between students academic performance and their Kolb learning style.

Discussion

Given the growing use of IT in academia, it is important that we determine the relevant human factors that may influence student course performance. This study attempted to utilize the Kolb theory of experiential learning (Kolb, 1984) to determine if the typology utilized in it might be of assistance in determining the effectiveness of two distinct course delivery formats (f2f and fully online) on student learning. Kolb’s posits that people are predisposed toward predominant learning styles through which they incorporate new knowledge and skills. As described above, Kolb has shown that in traditional learning contexts, students exhibit preferences for how they best process experiential material and thus learn most effectively. However, the findings of this study showed that students did not have a preference for either course delivery format based upon their learning style. Furthermore, it shows that their academic performance in either course delivery format was not contingent upon their learning style. Given these findings, no substantial inferences may be made with respect to how Kolb’s learning theory might be utilized in providing a traditional predominantly lecture style course to students. Rather, these findings suggest that, for at least this type of course, given proper IT support, students find either course delivery format similarly effective regardless of their learning style.

While these findings are of a nature that no suggestions should be made to how courses are designed or delivered, it can be suggested that the relatively new nature of IT in education requires us to inquire anew into the human factors that may be salient to creating effective educational opportunities. Kolb’s theory was developed over two decades ago, well before the introduction of IT such as course management systems and lecture capture systems into college level coursework. Teachers had comparatively less need to consider the potentially reciprocal influence of IT on their teaching pedagogy. Furthermore, it has been suggested by some that a ‘neo-millennial’ perspective (Dede, 2006) should be taken toward understanding students’ learning styles. The non-significant nature of the results found in this current study are perhaps best understood as informing the research in this area in two distinct manners. First, this study suggests that online education is as efficacious as traditional f2f education when viewed from the perspective of student learning styles. Secondly, these results show that we need to reevaluate how we comprehend student learning styles given the reality that IT has altered the nature of our experiential educational environments. Both of these possibilities support the need for further research into the area of IT and student learning styles.

References

Brittan-Powell, C. (2008). The impact of a lecture capture system upon student learning. Manuscript submitted for publication.

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Kayes, D. C. (2005). Internal validity and reliability of Kolb’s learning style inventory.         Journal of Business and Psychology. 20 (2), 249 – 257.

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Kolb, D. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall.

Kolb, D. A., & Kolb, A. (2005). The Kolb learning style inventory – Version 3.1: 2005 Technical specifications. Boston, MA: The Hay Group.

Rubie, T.L., & Stout, D. E. (1991). Reliability and classification stability and response set bias of alternate forms of the learning style inventory. Educational and Psychological Measurement, 51(2), 481-489.

Veres, J.G., Sims, R.R., & Locklear, T.S. (1991). Improving the reliability of Kolb’s revised learning style inventory. Educational and Psychological Measurement, 51, 143-150.

Wierstra, R. F. A., & DeJong, J.A. (2002). A scaling theoretical evaluation of Kolb’s learning style inventory. In Valcke, M., and Gombeir, D., (Eds.). Learning styles: Reliability and validity, 431-440. Proceedings of the seventh annual European learning styles information network, 26-28 June. Ghent, Belgium: University of Ghent.

About the Authors

Dr. Chris Brittan-Powell is an assistant professor in the Department of Applied Psychology and Rehabilitation Counseling at Coppin State University. He received his Ph.D. in Counseling Psychology from the University of Maryland at College Park and conducted his undergraduate studies at Boston College. Along with multicultural psychology, he focuses much of his research in the area of instructional technology. E-mail: cbrittan-powell@coppin.edu

Dr. Harry Legum is an associate professor in the Department of Applied Psychology and Rehabilitation Counseling at Coppin State University. He received his Ph.D. in Counseling from the George Washington University and B.S. from the University of Maryland at College Park. A prime area of his research is in the areas of education and school counseling. E-mail: hlegum@coppin.edu

Dr. Elias L. Taylor is a full professor of Sociology and former chairperson in the Department of Social Sciences at Coppin State University. He specializes in sociology, anthropology, social psychology, research methods, and statistics. He is a graduate of Rollins College, Winter Park, Florida and holds the M.A. and Ph.D. degrees from the New School University, Graduate Faculty for Political and Social Research. E-mail: etaylor@coppin.edu

For more information regarding this article, please communicate with the principal author and contact person, Chris Brittan-Powell.