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

Autor/inn/en	Chaker, Rawad; Bachelet, Rémi
Titel	Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners' Performance and Dropouts in a French MOOC
Quelle	In: International Review of Research in Open and Distributed Learning, 21 (2020) 4, S.199-221 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1492-3831
Schlagwörter	Foreign Countries; Large Group Instruction; Online Courses; Grades (Scholastic); Academic Persistence; Age Differences; Experience; Background; Employment Level; Geographic Regions; Gender Differences; Enrollment; Intention; Socioeconomic Status; Data Collection; Dropouts; Academic Achievement; Achievement Gap; Professional Development; Student Attrition; Learner Engagement; France; Caribbean; Africa + Suchen Sie Ihr Suchwort? Ausland; Online course; Online-Kurs; Notenspiegel; Age; Difference; Age difference; Altersunterschied; Erfahrung; Hintergrundinformation; Beschäftigungsgrad; Geschlechterkonflikt; Einschulung; Socio-economic status; Sozioökonomischer Status; Data capture; Datensammlung; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Schulleistung; Schülerbeurlaubung; Frankreich; Afrika
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, 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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01