A Review of Classification Approaches in Educational Data Mining for Predicting Student Performance

With the rapid increase in student data, and the growing interest in finding insights into student learning patterns,Educational Data Mining (EDM) methods are increasingly being used by educational institutes. Classification, a popular EDM method, enables the in-depth, efficient, and thorough analysis of student data while providing insights that directly assist in understanding student learning patterns and identifying elements that influence academic success. This review seeks to identify common trends and assess the effectiveness of four popularly explored classification approaches for predicting student performance. To assure the selection of research that specifically addresses the use of classification approaches for predicting student academic achievement, this review follows a systematic approach. A quality evaluation step was also included to help ensure that only reliable and credible studies were included in the review. According to the review findings of thirty two studies, most researchers used assessment results, academic performance index, and demographics to predict student performance. Decision Trees and Probabilistic classifiers were found to be the most popular and commonly used classification approaches for predicting student performance. The review also focuses on the challenges often faced while undertaking classification tasks in EDM and outlines future research directions in the context of analyzing student data.