Computing Reviews

Motivation as a lens to understand online learners:toward data-driven design with the OLEI scale
Kizilcec R., Schneider E. ACM Transactions on Computer-Human Interaction22(2):1-24,2015.Type:Article
Date Reviewed: 06/29/15

Massive open online courses (MOOCs) pose fresh challenges in e-learning. They report very large and global enrollments--across cultures, time zones, and goals. This necessitates new studies on learner behavior and effective teaching/learning practices. In this context, this paper makes a good attempt to understand the variety in such learners and links this variety to learner behavior online.

The first part of this paper discusses evolving an online learning enrollment intention (OLEI) scale to capture the various possible motivations for MOOC learners. The process of evolution is discussed to demonstrate its usefulness and adequacy. From open-ended responses, generalized items are created manually. These were cross-validated for adequacy and ambiguity and then refined over a series of iterations. The final scale includes entries such as “to improve English,” “general interest,” and “relevant to job.” These motivations were analyzed for correlations and variations across gender and age using a large dataset of MOOC learners. The rest of the paper postulates a series of research questions mapping learner behavior, including forum posts, fraction of videos seen, and assignments done to different motivations. For example, those who are motivated by the need to strengthen academic records would tend to submit all of the required assignments.

All studies are presented well and follow sound analysis methodology appropriate to the specific type of relation considered. The results of the studies will be of interest to all those involved in e-learning, specifically the buzz of MOOCs. Overall, this is a good paper.

Reviewer:  M Sasikumar Review #: CR143558 (1509-0829)

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