Towards Accurate Student Performance Prediction: An Assessment of Machine Learning Models and Metrics

Predicting students’ academic outcomes has become a central focus within Educational Data Mining (EDM) to support early academic interventions, minimize dropout risks, and promote personalized learning strategies. The availability of diverse educational data—ranging from demographic details and previous grades to behavioral engagement and digital learning activity—has encouraged the adoption of machine learning (ML) approaches for this purpose. This analysis synthesizes major ML techniques applied to student performance prediction, including regression models, decision trees, random forests, support vector machines, k-nearest neighbors, naïve Bayes classifiers, artificial neural networks, gradient boosting methods such as XGBoost and LightGBM, and deep learning models like CNNs and RNNs. Common evaluation measures—such as accuracy, precision, recall, F1-score, ROC-AUC, MAE, MSE, RMSE, and R²—are also examined. Despite their potential, challenges persist, including inconsistent data quality, complex feature selection, privacy and ethical concerns, limited interpretability, poor generalization across institutions, and the need for frequent model updates. Future research should emphasize explainable AI (XAI), temporal modeling techniques, integration of behavioral and psychological indicators, and transfer learning to enhance scalability and adaptability. Overall, this study highlights the promise of ML-driven systems in improving educational outcomes when combined with ethical, interpretable, and context-aware design principles.