Sustainable Educational Data Mining Studies: Identifying Key Factors and Techniques for Predicting Student Academic Performance - Murnawan, Murnawan; Lestari, Sri; Samihardjo, Rosalim; Dewi, Deshinta Arrova (Journal of Applied Data Sciences, 2024)
This research paper presents a systematic literature review of sustainable educational data mining (EDM) studies published between 2017 and 2022 with the objective of identifying the primary factors that affect student academic performance. The purpose of this study is to provide a comprehensive analysis of sustainable EDM research and identify the most important factors that influence student performance while highlighting commonly used data mining techniques in the EDM field. The results suggest that student demographics, previous grades and class performance, social factors, and online learning activities are the most common and widely used factors for predicting student performance in educational institutions. Furthermore, Decision Trees, Naive Bayes, and Random Forests are the most frequently used categories of data mining algorithms in the studies included in the dataset. The methodology used in this study is a systematic literature review, which is a widely used technique for literature review that provides a reliable and unbiased process for reviewing data from diverse sources. The findings of this study provide valuable insights into the factors influencing student performance in educational institutions and can be used by researchers to inform future research and identify relevant factors to consider when predicting student performance.