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Discussion Analytics: Identifying Conversations and Social Learners in FutureLearn MOOCs

Chua, Shi-Min; Tagg, Caroline; Sharples, Mike and Rienties, Bart (2017). Discussion Analytics: Identifying Conversations and Social Learners in FutureLearn MOOCs. In: MOOC analytics: live dashboards, post-hoc analytics and the long-term effects (Vigentini, Lorenzo; Wang, Yuan; Paquette, Luc and Urrutia, Manuel León eds.), CEUR-WS.org pp. 36–62.

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Abstract

Discussion among learners in MOOCs has been hailed as beneficial for social constructive learning. To understand the pedagogical value of MOOC discussion forums, several researchers have utilized content analysis techniques to associate individual postings with differing levels of cognitive activity. However, this analysis typically ignores the turn taking among discussion postings, such as learners responding to others’ replies to their posts, learners receiving no reply for their posts, or learners just posting without conversing with others. This information is particularly important in understanding patterns of conversations that occur in MOOCs, and learners’ commenting behaviors. Therefore, in this paper we categorize comments in a FutureLearn MOOC based on their nature (post vs. reply to others’ post), classify learners based on their contributions for each type of post-ing, and identify conversations based on the types of comments composing them. This categorization quantifies the dynamics of conversations in the discussion activities, allowing monitoring of on-going discussion activities in FutureLearn and further analysis of identified conversations, social learners, and course steps with an unusually high number of a particular type of comment.

Item Type: Conference or Workshop Item
Copyright Holders: 2018 The Authors
ISSN: 1613-0073
Extra Information: Held at 7th International Learning Analytics and Knowledge Conference
Vancouver, BC, Canada — March 13 - 17, 2017
Keywords: MOOCs; computer-mediated collaborative learning; learning analytics; FutureLearn
Academic Unit/School: Learning and Teaching Innovation (LTI) > Institute of Educational Technology (IET)
Learning and Teaching Innovation (LTI)
Faculty of Wellbeing, Education and Language Studies (WELS) > Languages and Applied Linguistics > English Language & Applied Linguistics
Faculty of Wellbeing, Education and Language Studies (WELS) > Languages and Applied Linguistics
Faculty of Wellbeing, Education and Language Studies (WELS)
Related URLs:
Item ID: 57071
Depositing User: Shi-Min Chua
Date Deposited: 22 Oct 2018 08:41
Last Modified: 23 Oct 2018 04:38
URI: http://oro.open.ac.uk/id/eprint/57071
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