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




MOOCs, learner grades, learner dropout, learner performance, academic cohorts, educational data mining, CHAID, low- and middle-income countries, developing countries


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, socio-professional 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.

Author Biographies

Rawad Chaker, Lyon 2 University

Rawad Chaker holds a PhD in Educational Sciences. He's specialized in educational technology, with a sociological and psychological approach. He's an Assistant Professor at Lyon 2 University, France.

Rémi Bachelet, Centrale Lille

Rémi Bachelet holds a PhD in Management sciences and is an Assistant Professor at Ecole Centrale de Lille. He's the creator of the "Project Management" MOOC, the first certifying MOOC in France.


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How to Cite

Chaker, R., & Bachelet, R. (2020). 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, 21(4), 199–221.



Research Articles