October 2007 Volume 10 Number 4 - Educational Technology ...

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Lu, H., Jia, L., Gong, S.H., & Clark, B. (2007). The Relationship of Kolb Learning Styles, Online Learning Behaviors and Learning Outcomes. Educational Technology & Society, 10 (4), 187-196. The Relationship of Kolb Learning Styles, Online Learning Behaviors and Learning Outcomes Hong Lu Department of educational technology, Shandong Normal University, China // Tel: +86-531-86188575 // luhong_1968@yahoo.com Lei Jia Department of English, Shandong Normal University, China // Tel: +86-531-86181218 // krowbat@gmail.com Shu-hong Gong Department of educational technology, Shandong Normal University, China // Tel: +86-531-86182530 // gongshuhong@21cn.com Bruce Clark Faculty of Education, University of Calgary, Canada // Tel: +1-403-220-7363 // bclark@ucalgary.ca ABSTRACT This study focused on the relationship between Kolb learning styles and the enduring time of online learning behaviors, the relationship between Kolb learning styles and learning outcomes and the relationship between learning outcomes and the enduring time of a variety of different online learning behaviors. Prior to the experiment, 104 students majoring in Educational Technology completed Kolb’s Learning Style Inventory (KLSI). Forty students were chosen to be subjects in an online learning experiment. Results indicated that there was a significant effect of Kolb learning style on the total reading time and total discussion time of the subjects. Although there was no significant effect between Kolb learning styles and learning outcomes, data from the experiment showed that the mean of learning outcomes of Convergers and Assimilators was higher than that of Divergers and Accommodators. There were two models of linear regression between learning outcomes and the enduring time of different online learning behaviors. Both of them were significant at the 0.001 level, and they accounted for 54.9% and 60.8% of the variance of the dependent respectively. The findings of this study were instrumental to instructors and moderators of online courses. First, instructors using online courses should seriously consider the diversity of learning styles when designing and developing online learning modules for different students. Second, they should provide a large number of electronic documents for students and give enough time to let them absorb knowledge by online reading. These could be effective methods to improve the quality of online courses. Keywords Kolb learning styles, Online learning behaviors, Learning outcomes Introduction Although online learning was growing rapidly, the effect of it was not yet satisfactory. For example, some students often complained that they could not find sufficient online learning resources to support their online courses, whereas other students were restricted by what they felt as a lack of opportunities to communicate with their instructors (Huang, 2003). Leigle & Janicki (2006) offered solutions to these problems, arguing that by customizing learning modules for differing student types, the learning outcome would be increased. Based upon this solution, the present study focused on the relationship of Kolb learning styles, online learning behaviors and learning outcomes. It was hoped that this study could help instructors understand the function of learning style in an online learning environment and thus develop corresponding online learning modules for different students. Kolb Learning Style Model The Kolb learning style model was based on Kolb’s experiential learning theory. In this model, Kolb defined learning style on a two-dimensional scale based on how a person perceived and processed information. How a person perceived information was classified as concrete experience or abstract conceptualization, and how a person ISSN 1436-4522 (online) and 1176-3647 (print). © International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. 187
processed information was classified as active experimentation or reflective observation (Simpson & Du, 2004). Accordingly, Kolb (1985) described the process of experiential learning as a four-stage cycle involving four adaptive learning modes: Concrete Experience (CE), Reflective Observation (RO), Abstract Conceptualization (AC), and Active Experimentation (AE). CE tended towards peer orientation and benefited most from discussion with fellow CE learners. AC tended to be oriented more towards symbols and learned best in authority-directed, impersonal learning situations, which emphasized theory and systematic analysis. AE tended to be an active, “doing” orientation to learning that relied heavily on experimentation and learned best while engaging in projects. RO relied heavily on careful observation in making judgments. Kolb (1985) also identified four learning style groups based on the four learning modes: Divergers favored CE and RO, Assimilators favored AC and RO, Convergers favored AC and AE, and Accommodators favored CE and AE. Kolb Learning Style Inventory There were many different learning style models. Many of them were derived from a common ancestry and measured similar dimensions (Brown, 2005). Accompanied by a vast collection of learning models, there was also a wealth of confusing assessment tools, amongst which, Kolb Learning Style Inventory (KLSI) remained one of the most influential and widely distributed instruments used to measure individual learning preference (Kayes, 2005). The original KLSI encountered serious attacks because of its low test-retest reliability and limited construct validity (John et al., 1991). In 1985, Kolb and his associates revised the KLSI to improve and refine its psychometric properties (Smith & Kolb, 1986). Some researchers had examined and found support for the revised KLSI. Veres et al. (1991) examined the revised KLSI and found increased stability. They argued that the revised KLSI might well be useful to researchers, educators and practitioners. Raschick, Maypole & Day’s (1998) research found the revised KLSI a useful tool for optimizing the relationship between supervisors and their students. The tool enabled both groups to view learning as a four-step process that involved experiencing, reflecting, conceptualizing and creatively experimenting. Based on these favorable results, KLSI became a well-accepted instrument for this experiment. Review of Literature Some recent studies analyzed the online learning behaviors between Kolb learning style groups. Simpson & Du (2004) explored the effect of Kolb learning styles on students’ online participation and self-reported enjoyment levels in distributed learning environments. Multiple regression analysis found that learning style had a significant impact on the students’ participation and enjoyment level. Fahy (2005) conducted a study of the relations between Kolb learning style and online communication behavior, and found that Convergers demonstrated their willingness to spend more time and energy on the network itself. Liegle & Janicki (2006) found that learners classified as “Explorers” (Active experimenters) tended to create their own path of learning (learner control), while subjects classified as “Observers” (Reflective observers) tended to follow the suggested path by clicking on the “Next” button (system control) in a web-based training program. However, there were studies that reached opposite conclusions. For example, in the environment of hypermedia learning, Reed et al. (2000) argued that Kolb learning styles had no effect on the number of linear steps, which determined whether the steps were the next logical, sequentially forward movement, and on the number of nonlinear steps, which determined whether the steps were branches or sidetracks. Why did inconsistent results occur in above-mentioned studies? The answer was that the lack of effect of Kolb learning styles could be due to that those studies involved the research variables, such as linear steps vs. nonlinear steps, that Kolb instrument did not seem to measure (Miller, 2005). According to the view, if research variables of the experiment could be measured by the KLSI, the effect of Kolb learning style would appear. To test it, this study chose for the online learning behaviors related with KLSI to be researching variables. Research investigating the learning outcome in an online or a hypermedia environment also reached confusing conclusions. For instance, Melara (1996) examined the effect of Kolb learning styles on learner performance within two different hypertext structures, and showed no significant difference in achievement for learners of different learning style using either hypertext structure. Davidson-shivers et al. (2002) investigated the effect of Kolb learning styles on undergraduate writing performance in a multimedia lesson. No statistically significant difference in writing performance among the learning styles was found. Howard and colleagues (2004) argued that, even though 188
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Abstracting and Indexing Educationa
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The Relationship of Kolb Learning S
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a question about learning together
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affordances of networked learning s
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included a unit on collaborative le
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on at different times and work indi
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• the numerous evaluation studies
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wanted to work. The same inquiry pr
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usability studies have a place in t
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Milrad, M., & Jackskon, M. (2007).
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information- and communication tech
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Interactive Examination, the studen
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Table 1. Criteria for grading OD st
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content. Differences in the attitud
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Even though further research on the
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Olofsson, A. D. (2007). Participati
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Etzioni (1993) points out that an i
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programme. They are rather construc
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to be that each individual trainee
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Bernmark-Ottosson, A. (2005). Demok
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The Knowledge Foundation (2005). IT
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Figure 1. Design Theories in contex
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attention as we frequently discuss
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Blog Reflection This design concept
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Narrative Structure (Form) Content
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Löwgren, J., & Stolterman, E. (200
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critique, neither do the single ind
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suggested by Ljungberg (1999b) we c
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the del.icio.us site (see figure 3)
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Device cultures It is not only the
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uilt using a camera-equipped PDA ru
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Jones, C., Dirckinck-Holmfeld, L.,
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Milrad, M., & Spikol, D. (2007). An
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components to be able to deal with
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compulsory throughout the project.
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only with their existing day-today
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As our work continues, we will try
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cognition as well as self-regulated
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Mobile mind map tool for stimulatin
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the pictorial knowledge representat
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Ericsson, K. A., & Simon, H. A. (19
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Al-A'ali, M. (2007). Implementation
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complex cognitive skills involve em
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Table 1. CAT and a linear mathemati
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It is agreed that the difficulty le
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Figure 7. Our newly added factors i
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question factors according to IRT;
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Instructure 1 Login User-ID Passwor
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Shute, V., & Towle, B. (2003). Adap
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distinguish between partial knowled
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esponse is identified, then the sco
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are stored in the answer record and
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2 (7) MNSQ = ∑WniZ ni ∑ Wni =
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in the course. A randomized block d
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To test whether ET can effectively
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course, more comparison tests could
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Fleischmann, K. R. (2007). Standard
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educational standards in practice t
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laboratory activities, which are bu
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process is currently a largely top-
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Hsu, Y.-S., Wu, H.-K., & Hwang, F.-
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evolution with computing technology
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Resources 0.55 0.72 e17 In my schoo
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teachers’ instructional evolution
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Regression models indicating relati
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of beliefs about integrating comput
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Sinko, M., & Lehtinen, E. (1999). T
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concept of social support. Lastly,
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Procedure Content analysis procedur
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Q.2 carries 25 marks, Q.3 carries 2
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the proposed methods can evaluate s
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Gogoulou, A., Gouli, E., Grigoriado
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enabling learner (subject) to work
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• Self-, Peer- and Collaborative-
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(iii) Regulates the communication:
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Figure 3. A screen shot of the SCAL
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2 nd study: Thirty-five students pa
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them characterized it as time and e
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Soller, A. (2001). Supporting Socia
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making reference to the others wher
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Solution 2.2: Deliberately select h
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just near a deadline, when it may b
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assessed on an individual basis. Wi
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- and even appreciated - by student
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Panitz, T., & Panitz, P. (1998). En
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Concept Mapping Concept mapping by
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Research Questions In order to dete
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with their teacher but rather than
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Tukey’s HSD post hoc test reveale
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collaboratively during study time?
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Jegede, O.J., Alaiyemola, F.F., & O
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esources. The use of synchronous te
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The use of digital communication mo
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contributed more information and en
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3. Use the second screen for the le
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• Use drawing to develop writing
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student and teacher on the synchron
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Vogel, J. J., Vogel, D. S., Cannon-
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Cases The first set of ten case stu
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Kılıçkaya, F. (2007). Website re
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