Student Performance Prediction Using Machine Learning: A Review Of Educational Data Mining Methods For Early Intervention

Predicting student performance is a central application of educational data mining and learning analytics, aimed at forecasting academic outcomes and identifying at-risk students early enough for teachers to intervene. This paper reviews machine learning approaches to the problem. It examines the data sources used, including prior grades, attendance, assignment submissions, learning-management-system activity and demographic factors, and the models applied, from decision trees, random forests and support vector machines to knowledge tracing and deep sequence models. Public datasets such as the Portuguese secondary-school data and the Open University Learning Analytics Dataset are discussed, together with class imbalance, feature engineering, evaluation metrics and fairness concerns. Real-world early-warning systems that combine prediction with student feedback are reviewed. A five-layer architecture comprising data integration, preprocessing, predictive modelling, explanation and intervention is proposed, with research gaps, objectives and a comparison of algorithms. The review concludes that predictive models are most useful when their outputs are explained, checked for bias and paired with supportive interventions that improve retention and personalized learning.