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Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC

Abstract : This paper uses data mining from a French project management MOOC to study learners' performance (i.e., grades and persistence) based on a series of variables: age, educational background, socioprofessional status, geographical area, gender, self-versus mandatory-enrollment, and learning intentions. Unlike most studies in this area, we focus on learners from the French-speaking world: France and French-speaking European countries, the Caribbean, North Africa, and Central and West Africa. Results show that the largest gaps in MOOC achievements occur between 1) learners from partner institutions versus self-enrolled learners 2) learners from European countries versus low-and middle-income countries, and 3) learners who are professionally active versus inactive learners (i.e., with available time). Finally, we used the CHAID data-mining method to analyze the main characteristics and discriminant factors of MOOC learner performance and dropout.
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https://hal.archives-ouvertes.fr/hal-03035505
Contributor : Rawad Chaker <>
Submitted on : Wednesday, December 2, 2020 - 11:20:04 AM
Last modification on : Tuesday, December 15, 2020 - 3:49:58 AM
Long-term archiving on: : Wednesday, March 3, 2021 - 6:56:16 PM

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Rawad Chaker, Rémi Bachelet. Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC. The International Review of Research in Open and Distributed Learning, 2020, 21 (4), pp.199-221. ⟨10.19173/irrodl.v21i4.4787⟩. ⟨hal-03035505⟩

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