Recommender Systems in E-learning: Trends, Challenges, and Future Directions
Year: 2025
Type: article
Abstract: 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.
Author: Journal of Theoretical and Applied Information Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7083177804
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