RECOMMENDER SYSTEMS IN E-LEARNING: TRENDS, CHALLENGES, AND FUTURE DIRECTIONS

Recommender systems enable personalized learning experiences in e-learning, which was previously unheard of. This survey describes the history and evolution of recommender systems and the methodologies and problems faced in contemporary e-learning. We consider classical approaches like collaborative filtering, content-based filtering, and hybrid models, as well as new approaches using deep learning, knowledge graphs, and XAI (explainable artificial intelligence). We also analyze problems like data sparsity, cold start challenges, system interpretability, and ethics. Emerging trends like adaptive learning and proactive context-aware recommendations are also discussed. Every aspect of the field is explained in this article, and it forms the basis for further insights and the impact these systems will have on education in the future.