Using Soloman-Felder Learning Style Index to Evaluate Pedagogical Resources for Introductory Programming Classes

Imran A. Zualkernan

American University of Sharjah izualkernan@aus.edu

Abstract

Soloman-Felder learning style index has been applied extensively in engineering education to ascertain the learning styles of students. This paper presents an approach showing how learning styles of students can be used to evaluate pedagogical resources. In specific, learning style can be used to help determine an appropriate textbook and an appropriate mixture of additional pedagogical devices such as virtual labs or ‘clickers’. An example from a first undergraduate programming course is used to illustrate the approach. 1. Introduction

Learning styles of students have been used to design

better teaching and learning strategies [1-4]. There is also some evidence that learning styles have an impact on effectiveness of online learning [5-7]. A number of learning style assessment tools and methodologies have been proposed [4]. However, the Soloman-Felder Index of Learning Styles (ILS) [8] has been extensively used in engineering education. ILS characterizes the learning style of a student along four dimensions; active-reflective, sensing-intuitive, visual-verbal and sequential-global. The first dimension (active-reflective) classifies learners based on their preference for learning through first-hand experimentation and social interaction (i.e., action) as opposed to learning by thinking through the process and examining ideas mentally (i.e., reflection). The second dimension of ILS ranges from sensing to intuitive learners; sensing learners learn better from empirical facts and practical procedures while the intuitive learners tend to learn better from conceptual meanings and theories. The third dimension of ILS is concerned with the visual/verbal dichotomy. Visual learners learn through charts, diagrams, rich multimedia and simulations. Verbal learners, on the other hand, prefer lectures. The fourth dimension of ILS is concerned about sequential or global learning; sequential learners prefer learning in a series of steps

leading (in a bottom-up fashion) to a broader understanding. Global learners, on the other hand, prefer to start their learning from larger ill-defined concepts and fill in the details; they learn by starting with the “big picture” and by fitting individual pieces of knowledge in a top-down fashion.

The ILS instrument is available on the internet [8] in the form of a questionnaire. The questionnaire consists of forty-four multiple-choice questions. Based on answers to these questions, ILS grades each dimension on a scale from -12 to +12. For example, a -12 on a visual-verbal scale means that an individual’s learning style is highly visual. In addition to being used with Computer Science and Engineering students [9-11], the validity and reliability of ILS has been established across multiple domains [12]. 2. Learning styles of undergraduate programmers

A study comparing learning styles of undergraduate students at an American University (University of Minnesota, Duluth) and a Middle-Eastern University (American University of Sharjah (AUS), UAE) suggested that despite vast cultural and geographical differences, within the context of an American curriculum, the learning styles of students as measured using ILS, had striking similarities [13]. In addition, there seemed to be a specific pattern to the learning styles of these students. The data from this study will be used to show how learning style can be used to evaluate and select pedagogical resources.

Figure 1-4 summarize the learning style patterns for AUS students (n = 69). A student whose learning style varied between -3 and +3 on a scale of -12 to 12 was considered “neutral” along a dimension. A learning style between 6 and 9 on either side was considered “moderate.” A value of more than 11 on either side was considered “extreme.”

As Fig. 1 shows, most students in AUS are neutral with respect to the active-reflective dimension. Since students are neither predominantly active nor

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reflective, pedagogical support for such students should include both active and reflective learning. This means that students should be allowed to experiment with ideas in group setting and given homework and reading assignments that allows them to reflect.

Count

Percent

ARCount

2.9Cum % 68.1 87.0 97.1 100.0

47 13 7 2Percent 68.1 18.8 10.1

Othermoderate reflectivemoderate activeneutral

70

60

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40

30

20

10

0

100

80

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0

Figure 1- Pareto Chart of Active-Reflective Dimension

Count

Percent

SICount

10.1 7.2 4.3Cum % 46.4 78.3 88.4 95.7 100.0

32 22 7 5 3Percent 46.4 31.9

ext. intuitiveext. sensingmod. intuitivemod. sensingneutral

70

60

50

40

30

20

10

0

100

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Figure 2- Pareto Chart of Sensing-Intuitive Dimension

Count

Percent

VVCount

7.2Cum % 31.9 65.2 92.8 100.0

22 23 19 5Percent 31.9 33.3 27.5

moderate verbalextreme visualneutralmoderate visual

70

60

50

40

30

20

10

0

100

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Figure 3 - Pareto Chart of Visual-Verbal Dimension

Count

Percent

SGCount

4.3Cum % 66.7 91.3 95.7 100.0

46 17 3 3Percent 66.7 24.6 4.3

Othermoderate globalmoderate sequentialneutral

70

60

50

40

30

20

10

0

100

80

60

40

20

0

Figure 4 – Pareto Chart of Sequential-Global Dimension

Fig. 2 shows that most students at AUS tend to be neutral or sensing on the sensing-intuitive dimension. However, since about 40% of students are sensing, the pedagogy should provide strong support for sensing students. In other words, the pedagogy should stress the details of programming language syntax (facts that need to be learned), problem solving through templates (both syntax and process) and repetition where the students are asked to practice programming based on these facts and procedures in a laboratory setting where they can use programming tools in a concrete fashion.

AUS students are mostly either neutral or visual learners (see Fig. 3). From a pedagogical perspective, this translates into an approach that uses visual representations of syntax diagrams and templates, call-trees, scoping rules and function-call stacks etc.

Finally, most AUS students are neutral (see Fig. 4) with respect to the sequential-global dimension. However, about one quarter of students at AUS are moderate sequential. This suggests a pedagogical approach that emphasizes learning in small, well-defined chunks that are put together sequentially in a bottom-up fashion.

In summary AUS students should have pedagogical resources that cater for active/reflective, moderately sensing, highly visual and mostly sequential learning styles.

3. Using learning styles to evaluate pedagogical resources

Realizations of invariance in learning styles can be used to evaluate teaching resources such as textbooks, laboratories, virtual laboratories, use of “clickers” and other teaching resources.

The choice of a textbook often reflects a commitment to a pedagogical style. It is difficult to characterize the “pedagogical approach” of a textbook. However, Table 1 shows a quantitative analysis of

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relevant content from three commonly used programming textbooks (labeled T1, T2 and T3). Table 1 - Analysis of commonly used textbooks for teaching introductory programming Metric T1 T2 T3 Number of paragraphs/page 2.641 3.198 1.785 Number of visuals/page 0.121 0.270 0.033 Number of sample programs/page

0.294 0.305 0.258

Number of tables/page 0.026 0.035 0.033 Number of case studies/page 0.035 0.031 0.024 Good programming practice/page

0.255 0.292 0.043

Common programming errors/page

0.433 0.000 0.091

Total number of relevant pages 231 318 209 As Table 1 shows, the relevant number of pages per textbook for the AUS curriculum for introductory programming was 231, 318 and 209 pages respectively.

From an active-reflective perspective, a text book naturally falls into the passive dimension of learning where a learner is engaged in a one-on-one fashion with the textbook in a mostly reflecting mode of learning. All three textbooks follow a passive style of presentation of materials. This means that all three textbooks are similar from this perspective.

From a sensing-intuitive perspective, all three textbooks are biased towards sensing learners because of a tendency to be driven by concrete syntax of the programming language. The numbers of sample programs, another indicator of sensing-intuitive dimension, have approximately the same density in all three textbooks. This implies that any of the three books should have a similar appeal to the sensing-oriented students of AUS.

A picture or a diagram is a visual representation. T2 has the most visuals per page. Therefore, T2 is probably the most suited for AUS students because these students tend to be highly visual. In addition, T2 also breaks text into smaller paragraphs as opposed to T1 and T2 (using paragraphs/page with page size being about the same) and hence, minimizing the verbal dimension.

From a sequential-global dimension, all three textbooks follow a similar highly sequential bottom-up presentation strategy. However, a sequential approach to teaching programming can either be based on syntax or problem solving. T1 and T2 teach students about simpler syntax elements (such as variables, for example) first and then proceed to teach more complex ones (such as loops, for example). T3, on the other hand, underpins its teaching sequence on problem

solving; it teaches students about simple problem solving constructs first, (output only, for example), followed by more complex ones (input, process and output, for example). It is not clear from the learning style data which of these two approaches is preferred by AUS students.

Tables often summarize “big picture” or a global dimension. However, the number of tables/page is almost the same for T2 and T3 and slightly lower for T1. In addition, both T1 and T2 have a higher density of case studies than T3; case studies should appeal to globally oriented learners who learn better from the big picture. Finally, a sprinkling of good programming practices and common programming errors should also appeal to global learners. Both T1 and T2 have about the same density of common programming practices followed by T3. Sprinkled advice on common programming errors should also make T1 more appealing to global learners. Both T2 and T3, on the other hand, have little to no advice on common programming errors. As summarized in Table 2, T2 is perhaps the best suited textbook for AUS students. Table 2. Comparative Evaluation of Textbooks for AUS students T1 T2 T3 Active-Reflective Same Same Same Sensing-Intuitive Same Same Same Visual-Verbal Worse Better Worst Sequential-Global Syntax Problem Syntax

Virtual labs ([14], for example) enable students to

solve a large number of small programming problems over the internet and provide a limited but automated real-time feedback on mistakes. Consequentially, virtual labs provide support for sensing and reflective learners who want to practice on concrete problems by themselves. An introduction of virtual labs at AUS is likely to have little impact because the number of such students in the sample studied is less than 3%.

Automated audience response systems or “clickers” are increasingly being used in higher education [15]. These systems allow a student to provide real-time responses to questions posed in a face-to-face learning situation. An immediate feedback on the correctness of their response is also provided. Use of such systems should provide support for both active and reflective learners; active preferring the real-time nature while reflective learners utilizing the opportunity to reflect.

Pair programming has been used in introductory programming courses where students are required to work in pairs (e.g., [16]). Pair programming is ideal for active learners who learn through social interaction. However, the utility of pair programming for reflective learners is not clear.

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Just because a majority of students follow a particular learning styles does not mean that others should be excluded. For example, in order to engage the global and intuitive learners (a small proportion at AUS), the homework problems can be changed. Rather than using typical toy problems stressing syntax building capabilities, problems in numerical analysis and graphics can be suggested. Similarly, since some textbooks do not support a visual style of learning, the lecture presentation can be slanted towards a more visual presentation to balance out the visual dimension.

Finally, traditional pedagogical resources like laboratories emphasize group problem solving and hence support the active style of learning. The verbal dimension of learning is obviously emphasized by classroom lectures. The presentation order of materials in the textbooks typically forces the same presentation order in lectures and labs and hence provides an overall support for the sequential style of learning employed in most programming textbooks. 4. Conclusion

This paper has shown how Soloman-Felder learning style Index can be used to evaluate not only alternative pedagogical resources but to arrive at a balanced mix of resources to cater to a particular population of students in a classroom.

Each individual class of students will have its own particular learning profile. Since the Soloman-Felder learning style index is readily available on the internet, this index can be easily and effectively used to fine-tune pedagogical resources for each individual class. 5. References [1] Dunn, R. Bruno, J., Skalar, R. I. and Beaudry, J., “Effects of matching and mismatching minority developmental college education student’s hemispheric preferences on mathematical scores,“ Journal of Education Research, vol. 83, no. 5, pp. 283-288, 1990. [2] Larkin-Hein, T. and Buddy, D., “Research on Learning Style: Applications in the Physics and Engineering Classrooms,” IEEE Transactions on Education, vol. 44, no. 3, pp. 276-281, August, 2001. [3] Nelson, B., Dunn, R., Griggs, S., Primavera, L., Fitzpatrick, M., Bacilious, Z., and Miller, R., “Effects of learning style intervention on college students’ retention and achievement,” Journal of College Student Development, vol. 34, no. 5, pp. 364-369, Sep. 1993. [4] Coffield, F., Moseley, D., Hall, E., and Ecclestone, K., “Learning styles and pedagogy in post-16 learning: A systematic and critical review,” Learning and Skills Research Center Report, 2004. [Online] Available: www.LSRC.ac.uk [Accessed November 22, 2005]

[5] Allert, J., “A Companion Technology Approach to CS1: Handheld Computers with Concept Visualization Software,” Proceedings of the 8th International Conference on Innovative Technology in Computer Science Education (ITiCSE-03), Thessaloniki, Greece, pp. 134-138, June 2003. [6] Allert, J., “Learning Style and Factors Contributing to Success in an Introductory Computer Science Course,” Proceedings of the 4th IEEE International Conference on Advanced Learning Technologies (ICALT2004), Joensuu, Finland, pp. 385-389, Aug. 2004. [7] Carver, C., Howard, R. A., and Lane, W. D., “Enhancing Student Learning Through Hypermedia Courseware and Incorporation of Student Learning Styles,” IEEE Transactions on Education, vol. 42. no. 2, pp. 33-38, February, 1999. [8] Soloman, B. and Felder, R. M., Index of Learning Styles (ILS), [Online] Available: http://www.ncsu.edu/felder-public/ILSpage.html. [Accessed November 22, 2005]. [9] Chamillard, A.T. and Karolick, D., “Using Learning Style Data in an Introductory Computer Science Course”, Proceedings of the thirtieth ACM Technical Symposium on Computer Science Education (SIGCSE’99), New Orleans, LA, pp. 291-295, February 1999. [10] Howard, R. A., Carver, C.A., and Lane, W. D., “Felder’s Learning Styles, Bloom’s Taxonomy, and the Kolb Learning Cycle: Tying it all Together in the CS2 Course” , Proceedings of the twenty-seventh ACM Technical Symposium on Computer Science Education (SIGCSE 96), Philadelphia, PA, pp. 227-231, February 1996. [11] Thomas, L., Ratcliffe, M., Woodbury, J., and Jarman, E., “Learning Styles and Performance in the Introductory Programming Sequence,” Proceedings of the 33rd ACM Technical Symposium on Computer Science Education (SIGCSE 2002), Covington, Kentucky, pp. 33-37, February 2002. [12] Felder, R., and Spurlin, J., “Application, reliability and validity of Index of Learning Styles,” International Journal of Engineering Education, vol. 21, no. 1, pp. 103-112, 2005. [13] Zualkernan, I. A., Allert, J. and Qadah, G.,”Learning Styles of Computer Programming Students: A Middle-Eastern and American Comparison,” IEEE Transactions on Education, vol. 49, no. 4, November, 2006 (to appear). [14] Rongas, T., Kaarna, A., Kälviäinen, H., "Classification of Computerized Learning Tools For Introductory Programming Courses: Learning Approach", Proceedings of ICALT 2004, Joensuu, Finland, pp. 678-680, 2004,. [15] Duncan, D. Clickers in the Classroom, Benjamin Cumming, 2005. [16] Nagappan, N., Williams, L., Ferzli, M., Wiebe, E., Yang, K., Miller, C., and Bali, S., “Improving the CS1 Experience with Pair Programming”, in Proceedings of ACM Technical Symposium on Computer Science Education (SIGCSE’03), Reno, Nevada, USA, , pp. 359-362, February 2003.

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