Open AccessInternationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC
Author(s) -
Rawad Chaker,
Rémi Bachelet
Publication year - 2020
Publication title -
international review of research in open and distance learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.436
H-Index - 68
ISSN - 1492-3831
DOI - 10.19173/irrodl.v21i4.4787
Subject(s) - chaid , dropout (neural networks) , psychology , mathematics education , political science , computer science , artificial intelligence , decision tree , machine learning
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.