Optimizing Student Academic Performance Prediction Using Heterogeneous Ensemble Learning

Abstract

This study presents a heterogeneous ensemble learning approach to improve the prediction of student academic performance through educational data mining techniques. The proposed model integrates three diverse classifiers—Random Forest, K-Nearest Neighbor (KNN), and Averaged One-Dependence Estimator (A1DE), integrated through Majority Voting. Data from 300 students enrolled in a postgraduate computer science program has been used for model training and testing. Comprehensive evaluation has been performed using 10-fold cross-validation and metrics such as accuracy, precision, recall, F-measure, and ROC. The ensemble model achieved a prediction accuracy of 96.88%, significantly outperforming individual models. The results highlight the potential of ensemble learning in educational contexts, particularly in accurately identifying at-risk students and informing timely interventions.