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Type: Conference paper
Title: Topic-wise classification of MOOC discussions: a visual analytics approach
Author: Atapattu Mudiyanselage, T.
Falkner, K.
Tarmazdi, H.
Citation: Proceedings of the 9th International Conference on Educational Data Mining, 2016 / Barnes, T., Chi, M., Feng, M. (ed./s), pp.276-281
Publisher: IEDMS
Issue Date: 2016
Conference Name: 9th International Conference on Educational Data Mining (EDM) (29 Jun 2016 - 02 Jul 2016 : Raleigh, NC, USA)
Statement of
Thushari Atapattu, Katrina Falkner, Hamid Tarmazdi
Abstract: With a goal of better understanding the online discourse within the Massive Open Online Course (MOOC) context, this paper presents an open source visualisation dashboard developed to identify and classify emergent discussion topics (or themes). As an extension to the authors’ previous work in identifying key topics from MOOC discussion contents, this work visualises lecture-related discussions as a graph of relationships between topics and threads. We demonstrate the visualisation using three popular MOOCs offered during 2013. This work facilitates the course staff to locate and navigate the most influential topic clusters as well as the discussions that require intervention by connecting the topics with the corresponding weekly lectures. Further, we demonstrate how our interactive visualisation can be used to explore correlation between discussion topics and other variables such as views, posts, votes, and instructor intervention.
Keywords: Visualisation; learning analytics; topic model; MOOC; online discourse; discussion forum
Rights: Copyright 2015 © International Educational Data Mining Society (IEDMS)
RMID: 0030057436
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Appears in Collections:Computer Science publications

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