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Special issue

The role of students' motivation and participation in predicting performance in a MOOC

P.G. de Barba

Corresponding Author

Melbourne Centre for the Study of Higher Education, The University of Melbourne, , Australia

Melbourne School of Psychological Sciences, The University of Melbourne, , Australia

Correspondence: Paula G. de Barba, Centre for the Study of Higher Education, The University of Melbourne, Victoria 3010, Australia. Email:

paula.de@unimelb.edu.au

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G.E. Kennedy

Melbourne Centre for the Study of Higher Education, The University of Melbourne, , Australia

Melbourne School of Psychological Sciences, The University of Melbourne, , Australia

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M.D. Ainley

Melbourne School of Psychological Sciences, The University of Melbourne, , Australia

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First published: 06 March 2016
Cited by: 20

Abstract

Over the last 5 years, massive open online courses (MOOCs) have increasingly provided learning opportunities across the world in a variety of domains. As with many emerging educational technologies, why and how people come to MOOCs needs to be better understood and importantly what factors contribute to learners' MOOC performance. It is known that online learning environments require greater levels of self‐regulation, and that high levels of motivation are crucial to activate these skills. However, motivation is a complex construct and research on how it functions in MOOCs is still in its early stages. Research presented in this article investigated how motivation and participation influence students' performance in a MOOC, more specifically those students who persist to the end of the MOOC. Findings indicated that the strongest predictor of performance was participation, followed by motivation. Motivation influenced and was influenced by students' participation during the course. Moreover, situational interest played a crucial role in mediating the impact of general intrinsic motivation and participation on performance. The results are discussed in relation to how educators and designers of MOOCs can use knowledge emerging from motivational assessments and participation measures gleaned from learning analytics to tailor the design and delivery of courses. © 2016 John Wiley & Sons Ltd

Number of times cited: 20

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