Privacy-Preserving and Interpretable Grade Prediction: A Differential Privacy Integrated TabNet Framework

Abstract

The increasing digitization of educational data poses critical challenges in balancing predictive accuracy with privacy protection for sensitive student information. This study introduces DP-TabNet, a pioneering framework that integrates the interpretable deep learning architecture of TabNet with differential privacy (DP) techniques to enable secure and effective student grade prediction. By incorporating the Laplace Mechanism with a carefully calibrated privacy budget (ϵ = 0.7) and sensitivity (Δf = 0.1), DP-TabNet ensures robust protection of individual data while maintaining analytical utility. Experimental results on real-world educational datasets demonstrate that DP-TabNet achieves an accuracy of 80%, only 4% lower than the non-private TabNet model (84%), and outperforms privacy-preserving baselines such as DP-Random Forest (78%), DP-XGBoost (78%), DP-MLP (69%), and DP-SGD (69%). Furthermore, its interpretable feature importance analysis highlights key predictors like resource engagement and attendance metrics, offering actionable insights for educators under strict privacy constraints. This work advances privacy-preserving educational technology by demonstrating that high predictive performance and strong privacy guarantees can coexist, providing a practical and responsible framework for educational data analytics.