A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques

TLDR

Educational data mining offers advanced methods for understanding student learning environments, yet universities face competitive pressures and challenges in performance analysis, requiring intervention plans to support students. This systematic review aims to examine EDM studies from 2009 to 2021 that identify student dropouts and those at risk. The authors conducted a systematic literature review of EDM research published between 2009 and 2021 on dropout and risk prediction. The review found that diverse machine‑learning techniques, applied to university and online learning datasets, effectively predict at‑risk students and dropouts, thereby improving student performance.

Abstract

Educational Data Mining plays a critical role in advancing the learning environment by contributing state-of-the-art methods, techniques, and applications. The recent development provides valuable tools for understanding the student learning environment by exploring and utilizing educational data using machine learning and data mining techniques. Modern academic institutions operate in a highly competitive and complex environment. Analyzing performance, providing high-quality education, strategies for evaluating the students’ performance, and future actions are among the prevailing challenges universities face. Student intervention plans must be implemented in these universities to overcome problems experienced by the students during their studies. In this systematic review, the relevant EDM literature related to identifying student dropouts and students at risk from 2009 to 2021 is reviewed. The review results indicated that various Machine Learning (ML) techniques are used to understand and overcome the underlying challenges; predicting students at risk and students drop out prediction. Moreover, most studies use two types of datasets: data from student colleges/university databases and online learning platforms. ML methods were confirmed to play essential roles in predicting students at risk and dropout rates, thus improving the students’ performance.