Simulating collaborative discourse data

Zachari Swiecki, Cody Marquart, Brendan Eagan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


We present a method for simulating collaborative discourse in terms of the patterns of codes present in the data. We argue that simulation methods have a successful history in other areas of social science, and that these methods can-and should-be adopted to study collaborative processes. We describe a novel simulation method that we have developed and provide evidence of the validity of this method by statistically comparing our observed data to simulated data. Furthermore, we show that using simulated data yields insights about collaborative processes that we would have missed if we had only examined the observed data.

Original languageEnglish
Title of host publicationCSCL Proceedings - 15th International Conference on Computer-Supported Collaborative Learning (CSCL) 2022
EditorsArmin Weinberger, Wenli Chen, Davinia Hernandez-Leo, Bodong Chen
Place of PublicationBloomington IN USA
PublisherInternational Society of the Learning Sciences
Number of pages8
ISBN (Electronic)9781737330646
Publication statusPublished - 2022
EventComputer-Supported Collaborative Learning 2022 - Online, Japan
Duration: 6 Jun 202210 Jun 2022
Conference number: 15th (Website) (Proceedings)

Publication series

NameComputer-Supported Collaborative Learning Conference, CSCL
PublisherInternational Society of the Learning Sciences (ISLS)
ISSN (Print)1573-4552


ConferenceComputer-Supported Collaborative Learning 2022
Abbreviated titleCSCL 2022
Internet address

Cite this