Self-regulation in online discussions: Aligning data streams to investigate relationships between speaking, listening, and task conditions

Alyssa Friend Wise, Ying Ting Hsiao

Research output: Contribution to journalArticle


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.

Original languageEnglish (US)
Pages (from-to)273-284
Number of pages12
JournalComputers in Human Behavior
StatePublished - Jul 2019



  • Asynchronous discussion groups
  • Computer mediated communication
  • Multichannel data
  • Multimodal data
  • Quantitative analysis of computer-supported collaborative learning
  • Self-regulated learning

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • Psychology(all)

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