Change discovery of learning performance in dynamic educational environments

In recent years, as information technology has become more prevalent, learning management systems have arisen around e-learning and web-based platforms. As a result, huge quantities of data about students' learning process have been recorded and stored. Teachers can apply data-mining techniques to mine students' learning performance. One such technique is association rule mining, which can find correlations between student characteristics and performance. For instance, a rule (Attendance = Middle) ^ (Gender = Male) → (Semester = Low) indicates that the semester grade of students is at the Low level if their gender is Male and attendance rate is Middle, where Low and Middle are predetermined linguistic terms given by teachers. Teachers can rely on such rules to formulate their teaching strategies. However, these rules may be varied in different semesters because of the change of student characteristics or teaching method of teachers. The above rule is used to describe student behavior during the last semester, yet, within this semester, the rule changes to (Attendance = Low) ^ (Gender = Female) → (Semester = Low). Without updating this knowledge, teachers might adopt inappropriate teaching strategies for students who are learning in different ways across different semesters. In this study, we propose a new change mining model to discover the change in student learning performance and characteristics on the basis of association rules. We conducted experiments with real-life datasets to evaluate the effectiveness of the proposed model.