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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Rawad Chaker, Rémi Bachelet
Formato:	article
Lenguaje:	EN
Publicado:	Athabasca University Press 2020
Materias:	MOOCs learner grades learner dropout learner performance academic cohorts educational data mining Special aspects of education LC8-6691
Acceso en línea:	https://doaj.org/article/1963818f142a4cdeab4a0d4583917504
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1963818f142a4cdeab4a0d4583917504
record_format	dspace
spelling	oai:doaj.org-article:1963818f142a4cdeab4a0d45839175042021-12-02T18:02:58ZInternationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC10.19173/irrodl.v21i4.47871492-3831https://doaj.org/article/1963818f142a4cdeab4a0d45839175042020-07-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/4787https://doaj.org/toc/1492-3831This 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. Rawad ChakerRémi BacheletAthabasca University PressarticleMOOCslearner gradeslearner dropoutlearner performanceacademic cohortseducational data miningSpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 21, Iss 4 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	MOOCs learner grades learner dropout learner performance academic cohorts educational data mining Special aspects of education LC8-6691
spellingShingle	MOOCs learner grades learner dropout learner performance academic cohorts educational data mining Special aspects of education LC8-6691 Rawad Chaker Rémi Bachelet Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC
description	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.
format	article
author	Rawad Chaker Rémi Bachelet
author_facet	Rawad Chaker Rémi Bachelet
author_sort	Rawad Chaker
title	Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC
title_short	Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC
title_full	Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC
title_fullStr	Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC
title_full_unstemmed	Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC
title_sort	internationalizing professional development: using educational data mining to analyze learners’ performance and dropouts in a french mooc
publisher	Athabasca University Press
publishDate	2020
url	https://doaj.org/article/1963818f142a4cdeab4a0d4583917504
work_keys_str_mv	AT rawadchaker internationalizingprofessionaldevelopmentusingeducationaldataminingtoanalyzelearnersperformanceanddropoutsinafrenchmooc AT remibachelet internationalizingprofessionaldevelopmentusingeducationaldataminingtoanalyzelearnersperformanceanddropoutsinafrenchmooc
_version_	1718378794759225344

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

Ejemplares similares