effects of learning styles and class participation on students' enjoyment level in distributed...

Association for Library and Information Science Education (ALISE)

Effects of Learning Styles and Class Participation on Students' Enjoyment Level inDistributed Learning EnvironmentsAuthor(s): Carol Simpson and Yunfei DuSource: Journal of Education for Library and Information Science, Vol. 45, No. 2 (Spring,2004), pp. 123-136Published by: Association for Library and Information Science Education (ALISE)Stable URL: http://www.jstor.org/stable/40323899 .

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Effects of Learning Styles and Class Participation on Students' Enjoyment

Level in Distributed Learning Environments

Carol Simpson, Ed.D. and Yunfei Du

This study explores the effect of learning styles and online participation on students' self-reported enjoyment levels in distributed learning environments. One hundred and sixty-nine entering students, who were enrolled in their first course totally via Internet, were chosen from masters' students in the School of Library and Information Sciences at the University of North Texas. The students were asked to complete Kolb's Learning-Style Inventory during a face-to-face training session on web-based learning. Subjects also reported their performance and enjoyment level in the course near the end of the term. Web Course Tool (WebCT) courseware automatically recorded ev- ery student's participation in terms of pages accessed, pages read, and total postings made. Multiple regression analysis found learning style significantly impacts students' enjoyment level but class participation does not. Learning styles and class participation together explain students' enjoyment level with an effect size of R2 = .125 (p < .01). Learning styles also statistically impact student participation in terms of "hits" (p = .028) and "posts" (p = .038) in this web-based course.

Introduction The U.S. Department of Education's Office of Educational Research and Improvement defines distance education as "the application of telecommu- nications and electronic devices which enable students and learners to re- ceive instruction that originates from some distant location."1 Since the 1990s, distance learning in library and information science (LIS) education has developed quickly. The 2001 Association for Library and Information Science Education (ALISE) Statistical Report shows 1 ,008 courses were delivered away from their home campus, in comparison to 489 courses in 2000 and 408 courses in 1999.2 US News and World Report reveals that as of December 2002, 14 of 52 ALA-accredited library schools offer masters' programs online.3

Comparison studies found that there seems to be no statistical difference in learning effectiveness between online and onsite learning.4 However,

J. of Education for Library and Information Science, Vol. 45, No. 2- Spring 2004 ISSN: 0748-5786 ©2004 Association for Library and Information Science Education 1 23



1 24 Journal of Education for Library and Information Science

About the Authors

Carol Simpson is Assistant Professor, School of Library and Information Sci- ences, and Fellow of the Texas Center for Digital Knowledge, University of North Texas, Dentón, ([email protected]); YunfeiDu is Assistant Pro- fessor, Library and Information Sciences Program, Wayne State University, De- troit, Michigan, ([email protected]). Ms. received 02/02; accepted 06/02; revised 12/02

distance learners have quite different experiences from onsite learners. Web-based distance learning is not merely web-extended independent studies, but a new challenge for instructors, students, and course- support teams.5 Students should recognize the limitations of an online environment - if they prefer learning via listening, observation, or participation - and should realize how isolation or marginalization "can contribute to their satisfaction with and success in learning online."6 An onsite environment makes it possible for learners to think, watch, observe, and partici- pate while communicating with the class. In web-based distance learning, text-based email and discussion forums are the primary communication channels, in comparison to more vocal and visual communications in onsite classrooms.

Because of the difference in onsite and distance class interactions, it is important to understand how these factors impact student learning and satisfaction. This study looks particularly at library and information science students. If LIS programs can find factors that impact students' cognitive processing, they may be able to design courses to compensate for the short- age of direct contact between instructors and students. In other words, "instructors planning to undertake a Web-based course must recognize the perceptual differences held by students toward the course."7

Learning styles, or cognitive styles, are one of the "perceptual differences." Ford and Chen defined the term cognitive style as "preferred modes of information processing, while learning style is defined as cognitive styles entailing information processing taking place specifically in a learning context."8 Unlike students' personal learning models,9 a learner's preferred learning style tends not to change over a long period. Many researchers use the terms "cognitive style" and "learning style" inter- changeably. The research surrounding learning styles in distance learning is diverse, originating from physical models, cognitive issues, and psychological or emotional aspects of an individual's learning style, etc.10

There is a long tradition of considering individual learning styles in library and information sciences. Stein, Hand, and Totten found that library educators need more training in communication theory, to know students' learning styles, and to realize that not all people can communicate most ef- fectively in the same way.11 Stein and Totten's study also suggested the im-



Effects of Learning Styles and Class Participation 1 25

portance of designing curriculum specifically aimed at identified students' cognitive strengths and weakness in library education.12 Ford stated the need to focus on the distinction between holists (global learners) and serial- ists (sequential learners) in learning, and implications for supporting individual users' navigation through virtual information environments.13

Purpose of This Study This study addresses students' self-reported enjoyment level as an indica- tion of student success in an online course. The WebCT courseware used with the study population automatically records student participation in terms of total web pages accessed (hits), total postings read (reads), and total postings made (posts) by a student during a semester. In this study the following definitions apply:

• A "hit" refers to a student's access to an individual course page, either a course tool or a content page. Duplicate accesses count multiple times.

• A "read" means an individual's access of a posting in the mail or discussion areas of a course.

• A "post" reflects a message composed, either public or private, in the mail or discussion areas of the course.

The following questions are addressed:

1 . What is the relationship between learning style, class participation, and student enjoyment level in distance learning?

2. If there are relationships between these variables, what is the magnitude of the relationships?

3. What predictors are most important in explaining enjoyment level variance?

4. Among all three indicators of class participation ("hits," "reads," and "posts"), do some variables explain enjoyment level better than others?

The hypotheses of this study are:

Ho (1): There is no statistically significant difference between individual learning styles and student self-report of class enjoyment;

Ho (2): There is no statistically significant difference between class participation and student self-report of class enjoyment; and

Ho (3): There is no statistically significant difference between individual learning styles and online class participation.

Learning Style Models

For the purpose of this study, the Kolb Learning-Style Inventory (LSI)14 best fit the specific scenario. Other learning style models, such as Dunn &



1 26 Journal of Education for Library and information Science

Dunn's, consider environmental, emotional, sociological, and physiologi- cal factors besides cognitive ones.15 Because the subjects were online, only course design could be manipulated to match the subjects' preference of cognitive processing. Other variables such as environmental variables (noise, temperature, and even lifestyle) cannot be controlled.

Kolb's LSI measures student learning style in two bipolar dimensions: concrete experiences when learning, and conceptual analyses when accru- ing skills and knowledge. In general, Kolb's LSI is considered to be a cognitive learning style model, which includes storage and retrieval of information in the brain and represents the learner's way of perceiving, thinking, problem solving, and remembering. Kolb's learning styles (assimilating, accommodating, diverging, and converging) originate from Jean Piaget's cognitive structure of assimilation, accommodation, equili- bration, and interiorization.16

Kolb defined learning style on a two-dimensional scale based on how a person perceives and internalizes information. How a person absorbs information is classified as concrete experience or abstract conceptualization; how a person internalizes (processes) information is classified as active experimentation or reflective observation. Kolb paired the two sets of options into four types of learning styles.17 According to Kolb, an individual is either concrete or abstract, and either reflective or active.

Type I: Diverging (concrete, reflective) - experience is gathered through tangible, felt qualities of immediate experience and is turned to thought by internal reflection on the external world. People with this style perform better in situations that call for generation of ideas, and they prefer to learn in groups.

Type II: Assimilating (abstract, reflective) - can understand information best when it is presented symbolically and conceptually and time is given for internal reflection. The assimilating style is important for library and information careers. People with this style generally find it more important that a theory have logical soundness than practical value. They prefer read- ing and having time to think things through.

Type HI: Converging (abstract/active) - understand and perceive information best through concepts and symbols but need the opportunity to work actively (external manipulation) in order to internalize the information. People with this style prefer lab assignments, practical applications, and to experiment with new ideas.

Type IV: Accommodating (concrete/active) - gather information best by tangible, felt qualities of immediate experience and need to process information through active action. This type applies new course material to new situations to solve real problems. They prefer to work with others to get assignments done and to test different approaches to complete projects.18

Other learning style theories include the Dunn and Dunn Learning Styles Model, McCarthy's 4 MAT System, and Gregoric's Mediation Abilities




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Model.19 These approaches have been developed independently of each other - and each with little recognition of the others' work20 (see Figure 1).

Related Studies Using Kolb's Learning-Style Model Although Newstead reports problems of reliability with previous versions of Kolb's LSI in 1992, Willcoxson and Prosser in 1996 find scores from Kolb's LSI highly stable and reliable.21 Using Kolb's Learning-Style Inven- tory, Terrell and Dringus studied the correlation between learning styles and student drop-out rates. They found that at the graduate student level the majority of students can succeed in online learning environments regard- less of their learning style; however, they found one particular learning style (accommodating) dropped from programs at a rate substantially higher than students with other preferred learning styles.22 Diaz and Cartnal suggest that students with less need for concrete experience in learning may be expected to be better suited to the distance format.23

Simons found learning style is a factor in training and education and it is a significant factor in the design of training programs and courses. Simons used only two of Kolb's two-dimensional scales (concrete vs. abstract, reflective vs. active) to group subjects instead of four learning styles. Simons found the reflective observation (RO) learners performed best in the instruction treatment, while active experimentation ( AE) learners excelled in the exploration technique. Simons' didn't report concrete experience (CE) learners and abstract conceptualization (AC) learners because they were not significant in Simon's path models.24

Method of the Current Study This correlational study was initiated to find relationships between students' self-report of enjoyment level, their learning styles, and class participation in a distance-learning environment. The population included masters' level students in the School of Library and Information Sciences at the University of North Texas.

One hundred sixty-nine graduate students specializing in school librarianship participated in the study during the first and second summer terms, 2001. The subjects were 100 percent online students, and they came to campus for software training because they had never taken a WebCT course. The course, Literature for Youth, was their first online course and was taught 100 percent online. The subjects were homogenous for age, gender, and knowledge level. More than 90 percent of them were school librarians or schoolteachers working toward their school library certifica- tion. They enrolled as regular masters' students. Ninety-five percent of all enrolled students participated in the study. Students' final grades were based on their performance in online readings, discussion postings, class projects, and online participation. The students were asked to complete the LSI in a face-to-face training session on web-based learning. The authors




were not permitted to use students' final grades in this study. To compensate, the authors developed a survey to measure students' learning effectiveness, particularly enjoyment level (see Appendix). Subjects reported their performance and enjoyment level during the course near the end of the term, using a five-category Likert scale. WebCT courseware automatically records all students' participation in terms of total web pages accessed (hits), total postings read (reads), and total postings made (posts) by a student during a semester. All students were required to read course pages, make assigned postings, and read all other student postings. Students were notified through the class syllabus that their online participation activities were tracked.

Analysis of the data was accomplished using the SPSS function of multiple regression analysis.25 Independent variables are learning style, total number of hits during the semester, total number of reads during the semester, and total number of class postings during the semester. The dependent variable is students' self-reported enjoyment level. In addition to statistical significance, effect sizes are also reported and interpreted in this study. Ef- fect size reports the magnitude of the correlation and is helpful in meta-analysis.26

Findings Of the 169 participants, there were 163 females and six males. Interviews with the students found that the sample of students matched the regular per- ception of distance learners specializing in youth librarianship: female, working full-time, involved with family and community work. Most of them are schoolteachers with library experience.27 Data screening found missing data were lost at random and these did not affect the distribution of the sample, thus all incomplete cases were removed from the data. The re- maining 169 cases from students in two courses in Summer I and Summer II, 2001, represent over 80 percent of the population. Table 1 also shows that most of the students in this sample are converging (abstract/active) or assimilating (abstract, reflective). Converging students liked the courses the most (1.69 out of 5.0) and assimilating students liked the courses the least (2.34 out of 5.0).

Learning styles Mean ata. Deviation n

Diverging 1.85 0.69 13 Accommodating 1 .87 1 .03 37 Converging 1.69 0.87 67 Assimilating Z34 1_25 52 Note: Enjoyment Level (1 = very enjoyable, 5 = very frustrated)

Table 1 Enjoyment Level by Learning Styles




Enjoyment Learning Source Level Style Hits Readings Posts

Mean 1.94 1.94 2402.36 1540.19 1.6441 Median 2.00 1.87 2370.00 1463.00 1.6435 Skewness 1.00 0.63 -0.21 0.22 0.36 Kurtosis 0.372 0.19 0.43 -0.55 0.10

Table 2 Descriptive Statistics of Regression Variables

Note: Learning style is a categorical variable, so it is represented with the mean of the dependent variable of each group.

The normally distributed class posting scores were achieved by trans- forming raw scores into logarithms (see Table 2, "Posts"). Descriptive statistics and histograms showed all variables were normally distributed and fitted linearity. The standardized predicted value and standardized residual scatterplot also indicated homoscedasticity, which means the sample meets the requirements of using this statistical method. Table 2 indicates the "nor- mal" distribution of the data because neither skewness nor kurtosis is greater than ± 1 .

Table 3 demonstrates a significant relationship between learning style (combined with class participation) and enjoyment level. The effect size (R2 = .125, adjusted R2 = .104) demonstrates the magnitude of correlation, which indicates a medium relationship between the independent variables and dependent variable in this study.28

Learning style (Table 4) was most significant in explaining enjoyment level. Learning style demonstrated a moderate positive relationship (beta weight = 0.287, p < .01) with the dependent variable, enjoyment level. Beta weight explains regression magnitudes between learning styles, class participation, and enjoyment level. Thus, the study rejects null hypothesis (1) and supports the alternative hypothesis.

The study fails to reject null hypothesis (2) because none of the participa-

Sum of Mean Source Squares df Square F p

Regression 23.41 6 3.90 3.87 0.001 Residual 162.25 162 1.01 Total 186.66 168

Table 3 ANOVA Report

Note: Predictors: (Constant), Learning Style, Hits, Readings, and Posts; Dependent Variable: Enjoy- ment Level; Degree of freedom is 6 because learning style has 4 categories.



Effects of Learning Styles and Class Participation 131

Unstandardized Standardized Model coefficient B Std. Error coefficient Beta t p

(Constant) 0.57 0.92 0.62 0.54 Learning Style 1.10 0.30 0.287 3.70 0.00 Hits -2.00E-04 0.00 -0.12 -1.19 0.24 Reads -3.17E-04 0.00 -0.16 -1.40 0.16 Posts 0J3 O42 O03 0.30 0.76 Note: Dependent Variable: Enjoyment Level.

Table 4 Regression Coefficients

tion scores ("hits," "reads," and "posts") is statistically significant. Class participation, however, shows a negative relationship by beta weight coefficients of "hits" and "reads." By looking at their beta weights, the variable of "reads" and the variable of "hits" are found to be much better in explaining the dependent variable since the variable of "posts" do not contribute much to enjoyment level. The data imply that when there are more required "hits" and "reads," students are slightly less likely to enjoy this course, but the relationships are not significant.

Learning style is significant in explaining student participation. Di- verging students made the most "hits" and "reads," while assimilating students made the least "posts" (Table 5).

Table 6 shows one-way analysis of variance (ANOVA) results from com- paring learning style and student participation ("hits," "reads," and "posts"). Learning style is statistically significant in terms of "hits" (p = .028) and "posts" (p = .038), but not in "reads" (p = .307) (alpha = .05). Null hypothesis (3) is not rejected.

Mean (Std. Deviation)

Learning Style Hits Reads Posts

Diverging 2637.62 (724.39) 1 643.77 (381 .92) 48.08 (21 .59) Accommodating 2137.92 (683.51) 1427.43 (469.89) 46.38 (21.81) Converging 2439.24 (610.41) 1520.87 (547.66) 59.82 (38.85) Assimilating 2484.1 7 (604.25) 1619.42 (526.27) 44.23 (28.20) Total 2402.36 (645.84) 1540.19 (515.13) 31.94 (2.46)

Table 5 Statistics of Hits, Reads, and Posts, by Different Learning Style




Source Sum of Squares df Mean Square F p

Hits Regression 3746041.24 3 1248680.41 3.11 0.028 Residual 66328047.46 165 401988.17 Total 70074088.70 168

Reads Regression 961370.07 3 320456.69 1.21 0.307 Residual 43619675.87 165 264361.67 Total 44581045.94 168

Posts Regression 8491.97 3 2830.66 2.87 0.038 Residual 162900.71 165 987.28 Total 171392.68 168

Table 6 ANOVA Report of Student Participation by Learning Style

Discussion

In this particular case, learning style and class participation significantly impact students' enjoyment level. Class participation, however, has a weak negative impact on enjoyment level. It is risky, if not wrong, to evaluate students by counting simply their "hits" or "posts" made in a class. It may be more effective to evaluate online participation by looking at the quality of students' posts contributed to the class, not "I agree/good point" replies. It seems appropriate to use an online portfolio for student assessments in online courses.

The sample used in this study shows that converging students (abstract/active) liked the courses the most (1.69 out of 5.0) and assimilating students (abstract, reflective) liked the courses the least (2.34 out of 5.0). Diverging (concrete, reflective, 1 .85 out of 5.0) and accommodating (concrete/active, 1.87 out of 5.0) students are not significantly different from the rest of the group. It might be conjectured that learners need many online activities (online readings, email, group work, etc.) in this course. Those who like to "do" things will enjoy the course the most, however those who prefer to "observe" in onsite environments will find themselves lost in online activities.

Learning style also affects student participation. Diverging students made the most "hits" and "reads," and assimilating students had the least "posts." It is possible that assimilating students also don't like this particular web-based course in terms of content or other factors. However, student participation was counted toward their final evaluation. Based on this study, course designers need to provide more support in their course modules to students who learn best by abstract conceptualization and reflective observation. Online courses lack the opportunity of direct demonstration




while an onsite class might have more of that type of activity, hence finding a way to provide these opportunities would enhance the learning options for students with these tendencies.

The data support Stein and Totten's statement that library education may benefit from the design of curriculum specifically aimed at identified students' cognitive styles.29 Therefore, in distance learning environments, it is good practice for online instructors to incorporate students' learning styles into the pedagogical design of their courses to maximize their students' success.

Distance educators are encouraged to find more pedagogical approaches that contribute to effective learning.30 From this study, the authors identify the need to design courses according to students' learning styles to enhance student success. For example, course modules might be designed to meet observing, participation, thinking, and summarizing learning circles to ac- commodate different learning styles. Accommodating different learning styles may also be helped through resource sharing in web-based course design. It is thus possible to design common courseware components for more than one library school, such as through The Library and Information Sci- ence Distance Education Consortium.31

Future studies are needed to explore other pedagogical issues such as the impact of students' technological background and instructors' teaching styles. In addition, because these research participants were chosen from summer school students, further study is needed to determine whether summer school students have different motivations from long semester students, which might affect their performance. It would be valuable to study different student groups in the future to validate these findings.

Acknowledgments The authors wish to express our sincere appreciation to the Texas Center for Digital Knowledge for their generous support of this study. Thanks to Pro- fessor Brian O'Connor for his warm personality and willingness to offer help whenever needed. The authors also wish to express thanks to Professor Barbara Stein for her thoughtful suggestions.

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6. Ibid, 357. 7. Elizabeth Buchanan, Hong Xie, Malore Brown, and Dietmar Wolfram, "A Systematic

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10. Waynne Blue James and Daniel L. Gardner, "Learning Styles: Implications for Distance Education," in Facilitating Distance Education, Mark. H. Rossman and Maxine E. Rossman, eds.(San Francisco, CA: Jossey-Bass, 1995), 19-31; Marcia M. Linn, "Cogni- tion and Distance Learning," Journal of the American Society for Information Science 47, no. 1 (1996): 826-42; Ritta Dunn and Shirley A. Griggs, eds., Practical Approaches to Using Learning Styles in Higher Education (Westport, CT: Greenwood, 2000); and Tammy Schellens and Martin Valcke. "Re-engineering Conventional University Educa- tion: Implication for Students' Learning Styles," Distance Education 21 no. 2 (2000): 361-84.

1 1 . Barbara L. Stein, James D. Hand, and Herman L. Totten, "Understanding Preferred Cogni- tive Styles - a Tool for Facilitating Better Communication," Journal of Education for Li- brary and Information Science 27, no. 1 (1986): 38-49.

1 2. Barbara L. Stein and Herman L. Totten, "Cognitive Styles: Similarities Among Students," Journal of Education for Librarianship 24, no. 1 (1983): 38-43.

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14. David A. Kolb, Learning-Style Inventory, version 3 (Boston: TRG Hay/McBer, Training Group, 1993).

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18. Ibid., 230. 1 9. Dunn and Dunn, The Complete Guide to the Learning Styles', Bernice McCarthy, "A Tale of

Four Librarians: 4 MAT's Learning Styles," Educational Leadership 54, no. 6 (1997): 46-51; and Antonio F. Gregoric, "Learning/Teaching Styles" Potent Forces Behind Them," Educational Leadership 36, no. 4 (1979): 234-36.

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27. Gayle Douglas, "Speaking Out: Analysis of the Experiences and Options Reported by Re- cent Graduates of the University of South Carolina's MLS Program," Journal of Education for Library and Information Science 43, no. 1 (2002): 43-46.

28 . Jacob Cohen, Statistical Power Analysis for the Behavior Science, 2nd ed. (Hillsdale, N. J. : L. Erlbaum Associates, 1988), 80.

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32. This paper was presented at the ALISE 2002 Annual Conference at New Orleans, LA, Jan- uary 15-18, 2002.




Appendix: Student Satisfaction Survey Please circle the appropriate response that most closely reflects your

point of view (on a scale from 1 to 5) for the following questions.

Note: All questions need to be answered.

1. In general, how would you describe your experience in completing the course?

Very frustrating 12 3 4 5 Very enjoyable

2. How satisfied were you with your performance of the course tasks/projects?

Mostly unsatisfied 12 3 4 5 Mostly satisfied

3. Generally, you think this course is

Very hard 12 3 4 5 Very easy

4. Your overall expectation of final score of this course is

Mostly uncompetitive 12 3 4 5 Mostly competitive

5. Your chance to take a similar course over the Web is

Very unlikely 12 3 4 5 Very likely



effects of learning styles and class participation on students' enjoyment level in distributed...

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