Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course

Kerrie Anna Douglas
Peter Bermel
Md Monzurul Alam
Krishna Madhavan

Abstract


MOOCs attract a large number of users with unknown diversity in terms of motivation, ability, and goals. To understand more about learners in a MOOC, the authors explored clusters of user clickstream patterns in a highly technical MOOC, Nanophotonic Modelling through the algorithm k-means++.  Five clusters of user behaviour emerged: Fully Engaged, Consistent Viewers, One-Week Engaged, Two-Week Engaged, and Sporadic users. Assessment behaviours and scores are then examined within each cluster, and found different between clusters. Nonparametric statistical test, Kruskal-Wallis yielded a significant difference between user behaviour in each cluster. To make accurate inferences about what occurs in a MOOC, a first step is to understand the patterns of user behaviour. The latent characteristics that contribute to user behaviour must be explored in future research. 

Keywords: MOOCs, Learning Analytics, Assessment


Keywords


MOOCs; learning analytics; assessment

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DOI: http://dx.doi.org/10.18608/jla.2016.33.9

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