Abstract
Under certain circumstances, E-learning can prevent students from interrupting their educational process about what happened during the lockdown, which caused education facilities from different levels to close. As a result, traditional learning has been replaced by online learning and the popularity of MOOCs is increasing and rapidly spreading all over the globe. Even after the crisis, the conventional form of learning is being complimented by online education nowadays. However, MOOCs struggle with low completion rates and high dropouts even though a large number of students have joined the courses. A variety of factors may be contributing to the low completion rates, especially the lack of interaction. As a result, predicting MOOC dropouts is an interesting research topic. The problem we intend to address in our research is to develop a system that enables the delivery of customized interventions based on the classification of students who could have motivational barriers.
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Mosbah, H., Boussaha, K., Drissi, S. (2023). Rescue Under-Motivated Learners Who Studied Through MOOCs by Prediction and Intervention. In: Kabassi, K., Mylonas, P., Caro, J. (eds) Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023). NiDS 2023. Lecture Notes in Networks and Systems, vol 783. Springer, Cham. https://doi.org/10.1007/978-3-031-44097-7_12
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