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Towards an integration of text and graph clustering methods as a lens for studying social interaction in MOOCs

TitleTowards an integration of text and graph clustering methods as a lens for studying social interaction in MOOCs
Publication TypeJournal Article
Year of Publication2014
AuthorsYang, D., Wen M., Kumar A., Xing E., & Rose C.
EditorsMcGreal, R., & Conrad D.
PublisherThe International Review of Research in Open and Distributed Learning
Volume15
Start Page214
Issue5
Pagination214-234
Date Published11/2014
Type of WorkSpecial Issue: Research into Massive Open Online Courses
ISSN1492-3831
Keywordslearning analytics, online learning
Abstract

In this paper, we describe a novel methodology, grounded in techniques from the field of machine learning, for modeling emerging social structure as it develops in threaded discussion forums, with an eye towards application in the threaded discussions of massive open online courses (MOOCs). This modeling approach integrates two simpler, well established prior techniques, namely one related to social network structure and another related to thematic structure of text. As an illustrative application of the integrated technique’s use and utility, we use it as a lens for exploring student dropout behavior in three different MOOCs. In particular, we use the model to identify twenty emerging subcommunities within the threaded discussions of each of the three MOOCs. We then use a survival model to measure the impact of participation in identified subcommunities on attrition along the way for students who have participated in the course discussion forums of the three courses. In each of three MOOCs we find evidence that participation in two to four subcommunities out of the twenty is associated with significantly higher or lower dropout rates than average. A qualitative post-hoc analysis illustrates how the learned models can be used as a lens for understanding the values and focus of discussions within the subcommunities, and in the illustrative example to think about the association between those and detected higher or lower dropout rates than average in the three courses. Our qualitative analysis demonstrates that the patterns that emerge make sense: It associates evidence of stronger expressed motivation to actively participate in the course as well as evidence of stronger cognitive engagement with the material in subcommunities associated with lower attrition, and the opposite in subcommunities associated with higher attrition. We conclude with a discussion of ways the modeling approach might be applied, along with caveats from limitations, and directions for future work.

URLhttp://www.irrodl.org/index.php/irrodl/article/view/1853
Rights

Creative Commons Attribution 4.0 International (CC BY 4.0)

Short TitleIRRODL
Original PublicationThe International Review of Research in Open and Distance Learning
Refereed DesignationRefereed
AttachmentSize
1853-15548-1-PB.pdf353.62 KB
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