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.