Optimizing Online Educational Experiences through Semantic Recommender Systems: A Review

Abstract

Semantic Web technologies in providing personalized recommendations to learners in digital educational environments. It involves applying advanced methods like Resource Description Framework (RDF), Web Ontology Language (OWL), and SPARQL query language, and machine learning to understand the meaning and context of learning materials and to align recommendations with individual learner goals, preferences, and knowledge gaps. The rapid expansion of online education platforms has introduced new challenges in delivering personalized and effective learning experiences to diverse learners. Traditional recommender systems, lack the sophistication needed to tailor educational content to the unique goals, cognitive abilities, and knowledge gaps of individual learners. This paper explores the potential of semantic recommender systems in enhancing online educational experiences by employing semantic web technologies such as ontologies, knowledge graphs, and machine learning. We review recent studies and implementations of semantic technologies within Learning Management Systems (LMS), showcasing how they contribute to more adaptive and engaging learning environments.  Future directions for research emphasize the need for scalable, adaptive ontologies, and cross-platform integration to further enhance the personalization and effectiveness of online learning environments.