Adaptive course recommendation using federated learning and graph convolutional networks in IoT-enhanced e-learning

The increase in e-learning platforms, especially Massive Open Online Courses (MOOCs), highlights the necessity for sophisticated, privacy-conscious recommendation algorithms that adjust to evolving learner interactions in IoT-integrated settings. This study introduces an innovative architecture that utilizes Federated Learning (FL) to safeguard user privacy during distributed training on educational platforms. This approach utilizes Graph Convolutional Networks (GCN) to depict intricate user-course interactions as a graph, adeptly capturing higher-order relational dependencies. Furthermore, DistilBERT-based feature extraction generates concise, semantically dense representations from course descriptions, hence improving content relevancy. Real-time IoT data, including user engagement metrics from smart devices, dynamically influences graph connections, facilitating context-aware recommendations.The suggested solution emphasizes scalability and privacy, tackling essential issues in contemporary e-learning environments. Thorough assessments indicate that our methodology substantially surpasses baseline methodologies across various performance indicators, providing exceptionally tailored course recommendations. This research promotes the advancement of adaptive, safe, and efficient recommendation systems for IoT-integrated e-learning, enhancing engaging and personalized learning experiences for users globally.