TabNet–XGBoost Hybrid Model for Student Performance Prediction and Customized Feedback

Virtual Learning Environments (VLEs) have emerged as a cornerstone of modern education, enabling large-scale delivery of learning materials, assessments, and interactions in fully or partially online formats. The dynamic and self-paced nature of VLEs makes the early prediction of learner scores crucial for timely intervention and support. The existing frameworks either underperform in capturing complex, non-linear relationships in heterogeneous educational data or lack interpretability mechanisms necessary for actionable interventions. This study proposes a TabNet–XGBoost hybrid model with SHAP-based interpretability for score range classification in VLE contexts, using the Open University Learning Analytics Dataset (OULAD). Data preprocessing involved cleaning, encoding, normalization, feature engineering, and score band derivation, producing an enriched feature matrix integrating demographic, assessment, and engagement indicators. TabNet’s sequential attentive feature selection extracted a latent representation of the most informative variables, which was subsequently refined by XGBoost to produce sharper decision boundaries for four distinct score ranges. SHAP values were computed post-prediction to identify domain-specific performance drivers, enabling alignment with a structured feedback module across seven predefined learning domains. Experimental results demonstrated a classification accuracy of 98.8% on the test set, outperforming the baseline frameworks. The SHAP-driven feedback mechanism provided interpretable, domain-targeted insights, enhancing the model’s practical applicability for educators and academic support teams. By integrating high predictive accuracy with transparent reasoning and actionable feedback, the proposed framework addresses both the technical and pedagogical requirements of early performance prediction in online learning environments, offering a scalable solution for real-time academic monitoring and intervention.