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Interests play an essential role in the process of learning, thereby enriching learners ‘interests will yield to an enhanced experience in MOOCs. Learners interact freely and spontaneously on social media through different forms of user-generated content which contain hidden information that reveals their real interests and preferences. In this paper, we aim to identify and extract the topical interest from the text content shared by learners on social media to enrich their course preferences in MOOCs. We apply NLP pipeline and topic modeling techniques to the textual feature using three well-known topic models: Latent Dirichlet Allocation, Latent Semantic Analysis, and BERTopic. The results of our experimentation have shown that BERTopic performed better on the scrapped dataset.

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Learners are more active and interactive interactive in social media as they freely express themselves and share their thoughts and feeling which generates much useful information about their characteristics. However, the same learners are not usually interactive in MOOCs as much as they do in social media. Therefore, focusing on the Learner’s social profile is a key component towards a successful and enriched experience of learning. Especially that MOOCs offer the possibility to sign up using social media accounts. Thereby, profile modelling is an essential step to capture the knowledge about the learners. In this paper, we aim to explore the information generated by the learner’s participation in social media and combine it with his characteristics in MOOCs. Our approach is based on implementing an ontological learner profile that combines other ontologies (IMS-LIP standard, FOAF, and SIOC ontology) as well as some of the major concepts that describe a user in social media. The result of our solution is a social profile ontology (SPOnt) that describes the main concepts of a learner in each environment (MOOCs and social media). We rely on ontology mapping and merging techniques to maintain the semantic links between the different concepts of the two implemented ontologies.

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