A Personalized and Collaborative eLearning Materials Recommendation Scenario Using Ontology-Based Data Matching Strategies

We propose a virtual teacher for the evaluation of students' competencies. It aims to improve learning by making personalized suggestions on the learning materials. It is based on three main components: 1) a semantically enriched content management system (CMS), playing the role of knowledge base, 2) a 3D anatomy browser and 3) an ontology-based matching strategy called Controlled Fully Automated Ontology Based Matching Strategy (CFOAM), providing the evaluation methodology. Together with the collaborative knowledge base, which allows knowledge to be represented in natural language and to be further reused, the evaluation methodology becomes the main contribution of the paper. The approach is demonstrated on a learning scenario illustrated around 3D anatomical structures.

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