Abstract
In the adaptive learning context, learner data becomes the main resource to identify learners’ individual characteristics to recommend adapted learning materials. Therefore, we need to collect and pre-process learner data to make predictions on the potential usefulness of a course to a given learner. This paper is concerned with Educational Data Mining that aims at exploring learner’s behavior and motivations by considering varying characteristics of their profile and the use of semantic web technologies to model these characteristics. Usually, the learner profile is constructed on a MOOC (Massive Open Online Course) platform; but, could be enriched through external data sources such as professional social networks. Consequently, we test user data extraction from a LinkedIn dataset to enrich the learner profile from a MOOC platform and prepare it for a semantic recommendation approach. Precisely, the obtained learner data will be used to instantiate our Learning Actor ontology that aims at matching semantically the learner needs with the MOOC characteristics. For this purpose, the MOOC profiles retrieved from a MOOC platform dataset will also be adapted to the ontology instantiation, since the developed ontology models the MOOC personalization criteria shared by learners and MOOCs.
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Assami, S., Daoudi, N., Ajhoun, R. (2023). Learner Profile Enrichment and Semantic Modeling of Learning Actors for MOOC Recommendation. In: Hassanien, A.E., Snášel, V., Tang, M., Sung, TW., Chang, KC. (eds) Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. AISI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-031-20601-6_62
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DOI: https://doi.org/10.1007/978-3-031-20601-6_62
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