Ontology for Educational Recommendation System in an Immersive Environment

Educational Recommendation Systems (ERS) face challenges in evaluating student behavior and performance, aiming to generate personalized learning suggestions. This paper proposes an ontology designed to support ERS in immersive learning environments. The ontology models key elements such as student interactions, learning materials, and competencies. As part of the validation process, an automatic script was developed to generate ontology instances, and SPARQL queries were executed to simulate interaction scenarios. The results include the defined ontology taxonomy and a demonstration of how its structure can represent adaptive learning contexts.