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.

翻译：教育日益数字化为数据驱动的个性化带来了前所未有的机遇，但也对学生数据隐私提出了重大挑战。传统的推荐系统依赖于集中式数据，这种范式通常与现代数据保护法规不相容。为解决这一关键问题，本文提出并评估了一种基于联邦学习（FL）的新型隐私保护推荐系统。该方法利用深度神经网络（DNN），并结合了来自大规模ASSISTments教育数据集的丰富工程特征。通过对联邦聚合策略进行严格的比较分析，发现FedProx在处理异构学生数据时，相比标准FedAvg基线方法，是一种显著更稳定且更有效的方法。优化后的联邦模型实现了高达76.28%的F1分数，相当于强大的集中式XGBoost模型性能的92%。这些结果验证了联邦学习方法能够在无需集中敏感学生数据的情况下，提供高效的个性化内容推荐。因此，我们的研究为现代教育平台中的个性化与隐私困境提供了一个可行且稳健的解决方案。