Change-Detection Machine Learning Model for Educational Management

Abstract

Adopting learning management systems to support teaching has become the norm in education field. Therefore, huge amount of data about the learning history of students has been accumulated. Educators, therefore, can use data mining techniques to evaluate students’ learning performance. Fuzzy association rule mining is one of the techniques used to investigate the correlation between teaching means or students’ characteristics and their performance. For instance, the rule Gender = Male∧Attendance = Low∧Final Report = Middle→Semester = Middle states that a student’s semester grade is in Middle if his gender is male, attendance rate is Low, and final report score is Middle, where Middle and Low are predetermined linguistic terms given by educators. The information shown in this example reveals general and concise knowledge for educators, allowing them to adjust their teaching strategies and pedagogies. However, no studies, to our knowledge, have yet to assess changes by applying this type of rule in the education field. For example, say that the above rule became available in the last semester; however, is not a trend this semester, and has been substituted by “Gender = Female∧Attendance = High∧Final Report = Middle→Semester = Middle”. Without updates in knowledge and belief, educators might adopt inappropriate teaching strategies and pedagogies for students who are learning in different time-periods. To deal with this problem, this study proposes a change mining model to detect changes in students’ learning performance. We also carry out experiments to evaluate the proposed model. We empirically demonstrate how the model helps educators to understand the changing characteristics of students and to improve their teaching practice.