An Intelligent Adaptive cMOOC “IACM” for Improving Learner’s Engagement

Soumaya El Emrani, Ali El Merzouqi, Mohamed Khaldi


Despite the massive number of enrollments in MOOC (Massive Open Online Course) platforms, dropout rates are very high. This problem can be due to several factors: Social, pedagogical, prior knowledge as well as a demotivation. To deal with this type of problems, we have designed an adaptive cMOOC (Connectivist MOOC) platform for each registered learner’s profile.
From the first human-machine interaction, the process adapts the learner's need according to a pre-established model. It is based on the processing of statistical data collected by correspondence analysis and regression algorithms. Each generated learner’s profile will provide an adaptive navigation and pedagogical activities. The intelligent system presented in this work will be able to classify learners according to their preferences and learning styles.


MOOC; cMOOC; adaptive learning; intelligent system; machine learning; correspondence analysis

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Copyright (c) 2021 Soumaya El Emrani, Ali El Merzouqi, Mohamed Khaldi

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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