Predicting Student Performance Using Machine Learning Techniques: A Systematic Literature Review

Predicting students' academic achievement is a difficult and challenging task, especially with the advancement of machine learning technology. However, despite the belief that learning outcomes can improve learning and teaching, the achievement of predictive academic achievement has not been fully explored. This study aims to comprehensively and deeply analyze the performance of machine learning and deep learning techniques in predicting student academic achievement. Using a systematic literature review approach, we surveyed a decade of research work, focusing on two aspects: (1) what are the key factors that influence the prediction of academic performance, and (2) what predictive models have been developed to predict student academic performance. The study found that various data mining techniques have been used to predict students' future achievement in advance and improve learning outcomes by exploring multi-source heterogeneous data from educational environments.