International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 18, No. 5, 8 Oct. 2026
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A Hybrid Explainable AI Model for Accurate and Transparent Prediction of Student Academic Performance
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Author(s)
Index Terms
Explainable AI, Hybrid Machine Learning, SHAP, LIME, Student Performance Prediction, Educational Data Mining, Ensemble Learning.
Abstract
Predicting student academic performance accurately is essential and allows for timely interventions and data-supported educational decision-making. However, the black-box nature of state-of-the-art machine learning models hinders their use in policy-sensitive educational settings, where interpretability and accountability are paramount. We propose a structured hybrid explainable artificial intelligence (XAI) framework that combines three tree-based ensemble models–namely, random forest, XGBoost, and CatBoost–using a prediction-level soft-voting strategy and couples them with complementary post-hoc interpretation methods at the explanation level (SHAP and LIME). Instead of proposing a new base algorithm, this study systematically unifies heterogeneous boosting and bagging approaches with strict validation as well as dual-layer explanation consistency evaluation. Experiments on the UCI Student Performance dataset show that the proposed framework can achieve a competitive predictive performance (91.8% accuracy, ROC–AUC = 0.953) together with transparent and actionable interpretability. The robustness of the interpretability layer is further supported by a quantitative assessment of explanation stability, fidelity, and agreement across various methods. The presented framework harmonizes the trade-off between accuracy and interpretability to provide a deployable, policy-aware decision-support solution that is aligned with responsible AI principles for adoption within educational contexts.
Cite This Paper
Mohammad Nasar, Mohammad Abu Kausar, "A Hybrid Explainable AI Model for Accurate and Transparent Prediction of Student Academic Performance", International Journal of Modern Education and Computer Science(IJMECS), Vol.18, No.5, pp. 149-167, 2026. DOI:10.5815/ijmecs.2026.05.09
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