Paper Info
Title
Self-regulation in online discussions: Aligning data streams to investigate relationships between speaking, listening, and task conditions
Abstract
This study extends previous research studying students' listening and speaking behaviors in online discussions with a particular focus on processes of self-regulated learning and task type as an external facilitator of regulation. 105 undergraduate students participated in a week-long small-group online discussion to address real-world business challenges. Groups were given either two contrasting alternative solutions to debate (negotiative task); or asked to come up with their own possible solutions (generative task). Students' regulation of their listening was assessed based on click-stream data; speaking was assessed by manually coding post content for argumentation. Data streams were aligned at the student-week level. Mixed models showed that the generative task led to positive effects on regulation of listening (percent of real reads and the average time of reviews). In addition, several relationships between listening and speaking were found; notably greater depth of listening to others predicted more positive positions taken in one's own posts and informed breadth of listening predicted more support provided for the positions taken.
Year
DOI
Venue
2019
10.1016/j.chb.2018.01.034
Computers in Human Behavior
Keywords
Field
DocType
Computer mediated communication,Asynchronous discussion groups,Self-regulated learning,Quantitative analysis of computer-supported collaborative learning,Multimodal data,Multichannel data
Social psychology,Self-regulated learning,Argumentation theory,Active listening,Cognitive psychology,Psychology,Coding (social sciences),Computer-mediated communication,Generative grammar,Online discussion,Facilitator
Journal
Volume
ISSN
Citations 
96
0747-5632
1
PageRank 
References 
Authors
0.36
13
2
Name
Order
Citations
PageRank
Alyssa Wise121319.86
Ying Ting Hsiao210.36