Understanding Participant’s Behaviour in Massively Open Online Courses
As the offer of Massive Open Online Courses (MOOCs) continues to grow around the world, a great deal of MOOC research has focused on their low success rates and used indicators that might be more appropriate for traditional degree-seeking students than for MOOC learners, who, because of the openness...
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Athabasca University Press
2019
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oai:doaj.org-article:7b9ee1069a424abaa125aca563d34f482021-12-02T18:02:59ZUnderstanding Participant’s Behaviour in Massively Open Online Courses10.19173/irrodl.v20i1.37091492-3831https://doaj.org/article/7b9ee1069a424abaa125aca563d34f482019-02-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/3709https://doaj.org/toc/1492-3831As the offer of Massive Open Online Courses (MOOCs) continues to grow around the world, a great deal of MOOC research has focused on their low success rates and used indicators that might be more appropriate for traditional degree-seeking students than for MOOC learners, who, because of the openness of MOOCs, represent a more diverse clientele who exhibit different characteristics and behaviours. In this study, conducted in a French MOOC that is part of the EDUlib initiative, we systematically classified MOOC user profiles based on their behaviour in the open-source learning management system (LMS) — in this case, Sakai — and studied their survival in the MOOC. After formatting the logs in ordinal variables in order to reflect a continuum of participation central to the behavioural engagement concept (Fredricks, Blumenfeld, & Paris, 2004), we incrementally executed a two-step cluster analysis procedure that led us to identify five different user profiles, after having manually excluded Ghots : Browser, Self-Assessor, Serious Reader, Active-Independent, and Active-Social. These five profiles differed both qualitatively and quantitatively on the continuum of engagement, and a significant proportion of the less active profiles did not drop out of the MOOC. Our results confirm the importance of social behaviours, as in recent typologies, but also point out a new Self-Assessor category. The implications of these profiles for MOOC design are discussed. Bruno PoellhuberNormand RoyIbtihel BouchouchaAthabasca University PressarticleDistance EducationMOOCsOpen Learningparticipant profilessurvival analysisbehavioural engagementSpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 20, Iss 1 (2019) |
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DOAJ |
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DOAJ |
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EN |
| topic |
Distance Education MOOCs Open Learning participant profiles survival analysis behavioural engagement Special aspects of education LC8-6691 |
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Distance Education MOOCs Open Learning participant profiles survival analysis behavioural engagement Special aspects of education LC8-6691 Bruno Poellhuber Normand Roy Ibtihel Bouchoucha Understanding Participant’s Behaviour in Massively Open Online Courses |
| description |
As the offer of Massive Open Online Courses (MOOCs) continues to grow around the world, a great deal of MOOC research has focused on their low success rates and used indicators that might be more appropriate for traditional degree-seeking students than for MOOC learners, who, because of the openness of MOOCs, represent a more diverse clientele who exhibit different characteristics and behaviours. In this study, conducted in a French MOOC that is part of the EDUlib initiative, we systematically classified MOOC user profiles based on their behaviour in the open-source learning management system (LMS) — in this case, Sakai — and studied their survival in the MOOC. After formatting the logs in ordinal variables in order to reflect a continuum of participation central to the behavioural engagement concept (Fredricks, Blumenfeld, & Paris, 2004), we incrementally executed a two-step cluster analysis procedure that led us to identify five different user profiles, after having manually excluded Ghots : Browser, Self-Assessor, Serious Reader, Active-Independent, and Active-Social. These five profiles differed both qualitatively and quantitatively on the continuum of engagement, and a significant proportion of the less active profiles did not drop out of the MOOC. Our results confirm the importance of social behaviours, as in recent typologies, but also point out a new Self-Assessor category. The implications of these profiles for MOOC design are discussed.
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| format |
article |
| author |
Bruno Poellhuber Normand Roy Ibtihel Bouchoucha |
| author_facet |
Bruno Poellhuber Normand Roy Ibtihel Bouchoucha |
| author_sort |
Bruno Poellhuber |
| title |
Understanding Participant’s Behaviour in Massively Open Online Courses |
| title_short |
Understanding Participant’s Behaviour in Massively Open Online Courses |
| title_full |
Understanding Participant’s Behaviour in Massively Open Online Courses |
| title_fullStr |
Understanding Participant’s Behaviour in Massively Open Online Courses |
| title_full_unstemmed |
Understanding Participant’s Behaviour in Massively Open Online Courses |
| title_sort |
understanding participant’s behaviour in massively open online courses |
| publisher |
Athabasca University Press |
| publishDate |
2019 |
| url |
https://doaj.org/article/7b9ee1069a424abaa125aca563d34f48 |
| work_keys_str_mv |
AT brunopoellhuber understandingparticipantsbehaviourinmassivelyopenonlinecourses AT normandroy understandingparticipantsbehaviourinmassivelyopenonlinecourses AT ibtihelbouchoucha understandingparticipantsbehaviourinmassivelyopenonlinecourses |
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