A Smart MOOC Learning Assistant Semantic Course Hybrid Recommendations Based on Multi-platform Data Integration

Massive Open Online Courses (MOOCs) provide learners with broad access to educational resources; however, learners often face information overload, fragmented course discovery, and difficulty selecting courses that match their goals, skills, and learning preferences. The intelligent learning assistant is capable of providing effective course recommendations to the students. This helps in overcoming the cold start problem for both the students and the courses. Also, the intelligent learning assistant uses deep learning methods to identify complex relationships between the courses and the skills, thereby providing recommendations that match the actual skill development paths. This review proposes a Smart MOOC Learning Assistant based on semantic hybrid course recommendation and multi-platform data integration. The study adopts a review-based methodology supported by thematic analysis and comparative synthesis of recent studies related to semantic web technologies, ontology-based modeling, knowledge graphs, RDF/SPARQL, learner profiling, and hybrid recommendation systems. The proposed framework integrates semantic course representation with hybrid recommendation logic to improve course personalization, interpretability, and learner-centered decision-making. The review highlights the importance of combining content semantics, learner preferences, behavioral signals, and multi-platform data to support more accurate and meaningful recommendations. The feasibility of this system has been further confirmed through the proven strength of multi-platform integration standards, with additional benefits from the use of educational tools, including semantic feedback and dynamic graph visualization, to enhance deep engagement with learning, thereby providing advanced and superior technology in the field of educational technology. The implications of this study suggest that semantic hybrid recommendation systems can reduce information overload, improve course discovery, and support adaptive learning pathways in MOOC environments. Future empirical validation using real MOOC datasets, model evaluation metrics, learner satisfaction measures, and A/B testing is recommended to assess the effectiveness of the proposed framework.