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

Authors

  • Soumaya El Emrani LIROSA Laboratory, Faculty of Sciences, Abdelmalek Essaâdi University
  • Ali El Merzouqi LIROSA Laboratory, Faculty of Sciences, Abdelmalek Essaâdi University
  • Mohamed Khaldi LIROSA Laboratory, Normal Superior School, Abdelmalek Essaâdi University

DOI:

https://doi.org/10.3991/ijet.v16i13.22261

Keywords:

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

Abstract


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.

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Published

2021-07-13

How to Cite

El Emrani, S., El Merzouqi, A., & Khaldi, M. (2021). An Intelligent Adaptive cMOOC “IACM” for Improving Learner’s Engagement. International Journal of Emerging Technologies in Learning (iJET), 16(13), pp. 82–94. https://doi.org/10.3991/ijet.v16i13.22261

Issue

Vol. 16 No. 13 (2021)

Section

Papers

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