Predicting student performance: A comprehensive review of machine learning, deep learning, and explainable AI approaches

Abstract

The application of Machine Learning (ML) and Deep Learning (DL) in Educational Data Mining (EDM) is revolutionizing the educational field. Researchers have been particularly interested in predicting student performance at an early stage. These early predictions can significantly benefit students’ learning experiences, allowing educators and other stakeholders to plan timely interventions. This review examines studies employing these technologies, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure the approach is transparent and replicable. This systematic review begins with preselecting a total of 281 records that go through a rigorous screening process. Then, 72 retained studies are analyzed to uncover the types of databases utilized, examine the most common features and labels in predictive models, and identify the prevalent ML and DL methods and the reasons for their selection. In addition, the review highlights the role of explainability in making complex models more interpretable and pedagogically meaningful. However, only a limited number of studies explicitly connect these predictive approaches to educational innovation. This review therefore emphasizes how predictive and explainable AI (XAI) can bridge that gap by supporting evidence-based teaching, adaptive learning, and more equitable decision-making in education.