Application of Association Rule Mining with Concept-Effect Relationship Model for Learning Diagnosis

Abstract

The traditional concept-effect relationship model (CER model) aims at finding the student’s suggestion to improve personalized learning outcomes. To provide more benefits to the instructor, we apply association rule mining to searching for interesting relationships among all students’ in- class testing scores. This approach enhances instructors to better understand student learning performance and improve the instructor’s course design. The experimental results on a computer data mining course have demonstrated feasibility of the approach and the mining results provide feedback for supporting instructors in the form of strong association rules, which is found to be very useful in practical applications.