Mining interesting contrast rules for a web-based educational system

Web-based educational technologies allow educators to study how students learn (descriptive studies) and which learning strategies are most effective (causal/predictive studies). Since web-based educational systems collect vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of students, assessments, and the solution strategies adopted by students. This paper focuses on the discovery of interesting contrast rules, which are sets of conjunctive rules describing interesting characteristics of different segments of a population. In the context of webbased educational sy stems, contrast rules help to identifY attributes characterizing patterns of performance disparity between various groups of students. We propose a general formulation of contrast rules as well as a framework for finding such patterns. We apply this technique to an online educational sy stem developed at Michigan State University called LON-CAP A.