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


  • Kerrie Anna Douglas Purdue University
  • Peter Bermel Purdue University
  • Md Monzurul Alam Purdue University
  • Krishna Madhavan Purdue University



MOOCs, learning analytics, assessment


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

Author Biographies

Kerrie Anna Douglas, Purdue University

Visiting Assistant Professor

Engineering Education


Peter Bermel, Purdue University

Assistant Professor

Electrical and Computer Engineering


Md Monzurul Alam, Purdue University

Graduate Research Assistant

Electrical and Computer Engineering


Krishna Madhavan, Purdue University

Associate Professor

Engineering Education




How to Cite

Douglas, K. A., Bermel, P., Alam, M. M., & Madhavan, K. (2016). Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course. Journal of Learning Analytics, 3(3), 170-192.