Literaturnachweis - Detailanzeige
Autor/inn/en | Chaker, Rawad; Bachelet, Rémi |
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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 |
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 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). |
Anmerkungen | Athabasca University Press. 1200, 10011-109 Street, Edmonton, AB T5J 3S8, Canada. Tel: 780-497-3412; Fax: 780-421-3298; e-mail: irrodl@athabascau.ca; Web site: http://www.irrodl.org |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2024/1/01 |