Comparative analysis of hybrid and single classification algorithms for student academic performance forecasting

Abstract

Educational data mining has become an important area of research for predicting students’ performance and enabling early intervention at higher education levels. In this work, a comparison of hybrid and single machine learning classifiers is undertaken to predict student academic performance datasets (hybrid dataset) from Al al-Bayt University, Jordan that consists of 19,700 students’ records, while a synthetic dataset that consists of 10,000 students’ datasheets is used for model validation. Ten single models, i.e., Logistic Regression, Naïve Bayes, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, XGBoost, CatBoost, and AdaBoost, were tested via 10-fold cross-validation. Furthermore, a hybrid soft-voting ensemble model combining Logistic Regression, Random Forest, and XGBoost was constructed. The best-performing single model was XGBoost, with an accuracy of 80%, while the combined hybrid model achieved the highest accuracy (92.06%). This study shows that hybrid ensemble models improve predictive performance and generalization compared to single classifiers, providing insights for educational  institutions to detect at-risk students and facilitate early academic intervention.