Are Highly Motivated Learners More Likely to Complete a Computer Programming Mooc

Bibliographic Data

ID21729313
AuthorsPiret Luik (0000-0002-9049-4192, University of Tartu), Margret Lepp (0000-0003-3303-5245, University of Tartu), Marina Lepp
Year2021
Volume22
Issue1
Pages41-58
Publication date2021-03-11
Open AccessYes
TypeARTICLE
VenueThe International Review of Research in Open and Distributed Learning (JOURNAL)
Journal identifiersISSN: 1492-3831 • E-ISSN: 1492-3831
PublisherAthabasca University Press (PUBLISHER • CA)
DOI10.19173/irrodl.v22i1.4978
OpenAlexW3138376969
LanguageEN
Citations received3
References cited11

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

Expectancy theory · Machine learning · Mathematics education · Computer Science · Education, Achievement, and Giftedness · Motivation and Self-Concept in Sports · Online Learning and Analytics · Psychology · Social Psychology