{"ID":2874985,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10516","arxiv_id":"2509.10516","title":"Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Prediction","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.","short_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 nove...","url_abs":"https://arxiv.org/abs/2509.10516","url_pdf":"https://arxiv.org/pdf/2509.10516v3","authors":"[\"Rodrigo Tertulino\",\"Ricardo Almeida\"]","published":"2025-09-03T11:28:57Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CY\",\"cs.LO\"]","methods":"[]","has_code":false}