An Unsupervised Method for Discovering How Does Learners’ Progress toward Understanding in MOOCs
Ali El mezouary1, Brahim Hmedna2, Omar Baz3
1Ali El mezouary*, IRF-SIC Laboratory, Faculty of Science, Ibn Zohr University, Agadir-Morocco.
2Brahim Hmedna, IRF-SIC Laboratory, Faculty of Science, Ibn Zohr University, Agadir-Morocco.
3Omar Baz, Department, IRF-SIC Laboratory, Faculty of Science, Department, Ibn Zohr University, Agadir-Morocco.
Manuscript received on February 09, 2021. | Revised Manuscript received on March 03, 2021. | Manuscript published on March 30, 2021. | PP: 40-49 | Volume-10 Issue-5, March 2021 | Retrieval Number: 100.1/ijitee.E86730310521| DOI: 10.35940/ijitee.E8673.0310521
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Massive Open Online Course (MOOC) seems to expand access to education and it present too many advantages as: democratization of learning, openness to all and accessibility on a large scale, etc. However, this new phenomenon of open learning suffers from the lack of personalization; it is not easy to identify learners’ characteristics because their heterogeneous masse. Following the increasing adoption of learning styles as personalization criteria, it is possible to make learning process easier for learners. In this paper, we extracted features from learners’ traces when they interact with the MOOC platform in order to identify learning styles in an automatic way. For this purpose, we adopted the Felder-Silverman Learning Style Model (FSLSM) and used an unsupervised clustering method. Finally, this solution was implemented to clustered learners based on their level of preference for the sequential/global dimension of FSLSM. Results indicated that, first: k-means is the best performing algorithm when it comes to the identification of learning styles; second: the majority of learners show strong and moderate sequential learning style preferences.
Keywords: MOOC, learning styles, FSLSM, Sequential global learning styles automatic detection, Clustering.