Open AccessArticle
An Ensemble Learning Approach for Student Performance Analysis of a Higher Educational Institute using a SHAP-Based Feature Selection and Optuna Optimization
1 Electrical and Computer Engineering Department, Rafik Hariri University, Meshref, Lebanon
2 Department of Financial Studies, Rafik Hariri University, Meshref, Lebanon
3 Department of Human Resources, Saint Joseph University of Beirut, Beirut, Lebanon
4 Department of Management and Marketing Studies, Rafik Hariri University, Meshref, Lebanon
*whom correspondence should be addressed. E-mail: nassreddinega@rhu.edu.lb
Adv. Sci. Technol. Eng. Syst. J. 11(2), 1–11 (2026);
DOI: 10.25046/aj110201
Received: 21 January 2026, Revised: 7 March 2026, Accepted: 10 March 2026, Published Online: 19 March 2026
(This article belongs to the SP20 (Special Issue on Multidisciplinary Frontiers in Engineering, Computing and Applied Sciences 2026) & Section Artificial Intelligence in Computer Science (CAI))
Forecasting and assessing student performance are crucial for allowing educators to pinpoint deficiencies and promote grade improvement. A thorough comprehension of feature contributions is crucial for improving model interpretability and facilitating informed decision-making in academic institutions. Explainable artificial intelligence encompasses methodologies and strategies designed to deliver transparent and accessible rationales for the decisions rendered by artificial intelligence and machine learning algorithms. In this research paper, an interpretable gradient boosting approach for predicting student performance is introduced, including both feature selection using SHapley Additive exPlanations (SHAP)-based features and cost-sensitive decision thresholds. The proposed methodology includes hybrid resampling with SMOTE-Tomek, Optuna hyperparameter optimization with stratified cross-validation, and SHAP-guided feature selection strategy. The proposed approach is tested using a dataset of a higher educational institute in the Middle East, including student information, learning management, and a video interaction system, to make an analysis and evaluate the performance of the students. The results show both improvement of the macro F1-score and the fail-class recall by achieving an accuracy of 94%, a weighted/macro F1-score of 0.9399, and a fail-class recall of 0.9619. The suggested method facilitates trade-offs among prediction accuracy, interpretability, and fairness, bridging the divide between high-performing machine learning models and practical educational applications, hence aiding in the formulation of data-driven policies and the customization of learning experiences.
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