A Systematic Approach to Predicting Students' Academic Performance: A Review of Recent Literature

The rapid expansion of digital learning has generated large volumes of educational data, creating new opportunities to apply machine learning (ML) and data mining techniques to predict student academic performance. This study synthesizes 58 empirical studies that used Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, and Artificial Neural Networks to identify at-risk students and improve educational outcomes.

The review focuses on predictor variables, validation methods, accuracy rates, and performance metrics. Findings suggest that the most effective predictive models combine four categories of variables: demographic factors, academic indicators, digital behavioral features, and psychosocial attributes. Among the algorithms examined, Random Forest and Artificial Neural Networks demonstrated the strongest predictive performance, achieving accuracy rates of 85%–93% across k-fold cross-validation and train-test split validation.

Performance measures such as precision, recall, F1 score, and AUC further confirm the robustness and generalizability of these models. ML-based academic prediction systems can strengthen early warning systems, support data-driven policymaking, and enable personalized learning interventions. The study concludes that combining multidimensional predictors with explainable AI can improve equity, personalization, operational efficiency, and accountability in educational decision-making.