An Integrated Approach for Modeling Learning Patterns of Students in Web-Based Instruction

Abstract

Web-based instruction (WBI) programs, which have been increasingly developed in educational settings, are used by diverse learners. Therefore, individual differences are key factors for the development of WBI programs. Among various dimensions of individual differences, the study presented in this article focuses on cognitive styles. More specifically, this study investigates how cognitive styles affect students' learning patterns in a WBI program with an integrated approach, utilizing both traditional statistical and data-mining techniques. The former are applied to determine whether cognitive styles significantly affected students' learning patterns. The latter use clustering and classification methods. In terms of clustering, the K-means algorithm has been employed to produce groups of students that share similar learning patterns, and subsequently the corresponding cognitive style for each group is identified. As far as classification is concerned, the students' learning patterns are analyzed using a decision tree with which eight rules are produced for the automatic identification of students' cognitive styles based on their learning patterns. The results from these techniques appear to be consistent and the overall findings suggest that cognitive styles have important effects on students' learning patterns within WBI. The findings are applied to develop a model that can support the development of WBI programs.