Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Prediction

• URL: http://arxiv.org/abs/2509.10516v3
• Date: Mon, 10 Nov 2025 14:44:47 GMT
• Title: Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Prediction
• Authors: Rodrigo Tertulino, Ricardo Almeida,
• Abstract summary: Digitalization of education presents opportunities for data-driven personalization, but it also introduces challenges to student data privacy.<n>A novel privacy-preserving recommender system is proposed and evaluated to address this critical issue using Federated Learning (FL)
• Score: 0.07161783472741748
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing digitalization of education presents unprecedented opportunities for data-driven personalization, but it also introduces significant challenges to student data privacy. Conventional recommender systems rely on centralized data, a paradigm often incompatible with modern data protection regulations. A novel privacy-preserving recommender system is proposed and evaluated to address this critical issue using Federated Learning (FL). The approach utilizes a Deep Neural Network (DNN) with rich, engineered features from the large-scale ASSISTments educational dataset. A rigorous comparative analysis of federated aggregation strategies was conducted, identifying FedProx as a significantly more stable and effective method for handling heterogeneous student data than the standard FedAvg baseline. The optimized federated model achieves a high-performance F1-Score of 76.28%, corresponding to 92% of the performance of a powerful, centralized XGBoost model. These findings validate that a federated approach can provide highly effective content recommendations without centralizing sensitive student data. Consequently, our work presents a viable and robust solution to the personalization-privacy dilemma in modern educational platforms.

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