Abstract
Massive Open Online Courses have brought a revolution in the field of e-learning. However, the lack of support and personalization drives learners to lose their motivation and surrender the learning process. One of issues that MOOC should address in personalization of learning according to learners needs to reinforce motivation. The potential of learning activities to motivate learners in enhancing learning cannot be denied. Therefore, we focus on adapting learning activities to learners through a recommender system in order to suit individual learners’ diverse needs. In this paper we outline a set of dimensions that distinguish, describe and categorize learning activities based on existing categorizations. We propose a classification of these recommended learning activities according to Bloom’s taxonomy. These learning activities are integrated into and overall a rule based recommender system with modular architecture.
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Harrathi, M., Touzani, N., Braham, R. (2018). Toward a Personalized Recommender System for Learning Activities in the Context of MOOCs. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_57
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DOI: https://doi.org/10.1007/978-3-319-59480-4_57
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