Towards a Quality-Driven and Ontology-Based Recommender Framework for Open Educational Resources

Open Educational Resources (OERs) have emerged as indispensable components of educational innovation, encompassing freely accessible teaching, learning and research materials. The increasing variety and quantity of OERs present a challenge in terms of assessing their quality. A structured recommendation framework is needed to organize and categorize information and knowledge related to OERs and then systematically evaluate the quality of these resources and most importantly enable informed decision-making in educational settings. This research paper focuses on the proposal of a recommender system supporting open educational practices, with a particular emphasis on the quality of OERs. In this context, this research work proposes the development of an ontology called Open Learning Object Metadata (OLOM) specifically designed for OERs. Indeed, OLOM was built upon an existing Learning Object Metadata (LOM) ontology, incorporating additional classes and attributes as necessary. By improving resource discovery and organization, OLOM assists educators in enhancing the efficiency and effectiveness of OERs. Our ontology-based proposed framework will contribute to the assessment and organization of OERs, facilitating informed decision-making and optimizing their reuse and interoperability in educational settings.