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

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, respecting 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. • A systematic review of ML/DL techniques for predicting student performance. • PRISMA methodology used to select 72 studies from an initial pool of 281. • OULAD and UCI datasets are the most commonly employed in this domain. • Tree-based models and neural networks dominate predictive approaches. • Explainable AI (XAI) is emerging as a key component to support pedagogical decisions, although its adoption remains limited.