Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?

Computer programming MOOCs attract people who have different motivations. Previous studies have hypothesized that the motivation declared before starting the course can be an important predictor of distinctive dropout rates. The aim of this study was to outline the main motivation clusters of partic...

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Autores principales:	Piret Luik, Marina Lepp
Formato:	article
Lenguaje:	EN
Publicado:	Athabasca University Press 2021
Materias:	MOOC motivation programming clusters completion Special aspects of education LC8-6691
Acceso en línea:	https://doaj.org/article/1d015067c6924305ae38027e10700a7b
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spelling	oai:doaj.org-article:1d015067c6924305ae38027e10700a7b2021-12-02T17:15:37ZAre Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?10.19173/irrodl.v22i1.49781492-3831https://doaj.org/article/1d015067c6924305ae38027e10700a7b2021-03-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/4978https://doaj.org/toc/1492-3831Computer programming MOOCs attract people who have different motivations. Previous studies have hypothesized that the motivation declared before starting the course can be an important predictor of distinctive dropout rates. The aim of this study was to outline the main motivation clusters of participants in a computer programming MOOC, and to compare how these clusters differed in terms of intention to complete and actual completion rate. The sample consisted of 1,181 respondents to the pre-course questionnaire in the Introduction to Programming MOOC. A validated motivation scale, based on expectancy-value theory and k-means cluster analysis, was used to form the groups. The four identified clusters were named as Opportunity motivated (27.7%), Over-motivated (28.6%), Success motivated (19.6%) and Interest motivated (24.0%). Comparison tests and chi-square test were used to describe the differences among the clusters. There were statistically significant differences among clusters in self-evaluated probability of completion. Also, significant differences emerged among three clusters in terms of percentages of respondents who completed the MOOC. Interestingly, the completion rate was the lowest in the Over-motivated cluster. A statistically significant higher ratio of completers to non-completers was found in the Opportunity motivated, Success motivated, and Interest motivated clusters. Our findings can be useful for MOOC instructors, as a better vision of participants’ motivational profiles at the beginning of the MOOC might help to inform the MOOC design to better support different needs, potentially resulting in lower dropout rates. Piret LuikMarina LeppAthabasca University PressarticleMOOCmotivationprogrammingclusterscompletionSpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 22, Iss 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MOOC motivation programming clusters completion Special aspects of education LC8-6691
spellingShingle	MOOC motivation programming clusters completion Special aspects of education LC8-6691 Piret Luik Marina Lepp Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?
description	Computer programming MOOCs attract people who have different motivations. Previous studies have hypothesized that the motivation declared before starting the course can be an important predictor of distinctive dropout rates. The aim of this study was to outline the main motivation clusters of participants in a computer programming MOOC, and to compare how these clusters differed in terms of intention to complete and actual completion rate. The sample consisted of 1,181 respondents to the pre-course questionnaire in the Introduction to Programming MOOC. A validated motivation scale, based on expectancy-value theory and k-means cluster analysis, was used to form the groups. The four identified clusters were named as Opportunity motivated (27.7%), Over-motivated (28.6%), Success motivated (19.6%) and Interest motivated (24.0%). Comparison tests and chi-square test were used to describe the differences among the clusters. There were statistically significant differences among clusters in self-evaluated probability of completion. Also, significant differences emerged among three clusters in terms of percentages of respondents who completed the MOOC. Interestingly, the completion rate was the lowest in the Over-motivated cluster. A statistically significant higher ratio of completers to non-completers was found in the Opportunity motivated, Success motivated, and Interest motivated clusters. Our findings can be useful for MOOC instructors, as a better vision of participants’ motivational profiles at the beginning of the MOOC might help to inform the MOOC design to better support different needs, potentially resulting in lower dropout rates.
format	article
author	Piret Luik Marina Lepp
author_facet	Piret Luik Marina Lepp
author_sort	Piret Luik
title	Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?
title_short	Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?
title_full	Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?
title_fullStr	Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?
title_full_unstemmed	Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?
title_sort	are highly motivated learners more likely to complete a computer programming mooc?
publisher	Athabasca University Press
publishDate	2021
url	https://doaj.org/article/1d015067c6924305ae38027e10700a7b
work_keys_str_mv	AT piretluik arehighlymotivatedlearnersmorelikelytocompleteacomputerprogrammingmooc AT marinalepp arehighlymotivatedlearnersmorelikelytocompleteacomputerprogrammingmooc
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Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?

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