Advancements in Machine Learning Techniques for Educational Data Mining: An Overview of Perspectives and Trends

Abstract

In the educational data mining (EDM) field, predicting student at-risk, student retention, dropout and performance have been attractive tasks among researchers. However, it is difficult to develop accurate models without first performing proper feature selection and class balancing. Therefore, the goal of this study is to review the current and future perspective and trends within the field of EDM for the past 10 years. The goal is to understand the state-of-the-art methods and techniques involving feature selection, class balancing and machine learning models. From the analysis, it is understood that there are plenty of research gaps yet to be explored.