Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC
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 intention...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
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Athabasca University Press
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/1963818f142a4cdeab4a0d4583917504 |
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| Sumario: | 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.
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