Socio-Semantic Network Motifs Framework for Discourse Analysis

Bodong Chen, Xinran Zhu, Hong Shui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Effective collaborative discourse requires both cognitive and social engagement of students. To investigate complex socio-cognitive dynamics in collaborative discourse, this paper proposes to model collaborative discourse as a socio-semantic network (SSN) and then use network motifs - defined as recurring, significant subgraphs - to characterize the network and hence the discourse. To demonstrate the utility of our SSN motifs framework, we applied it to a sample dataset. While more work needs to be done, the SSN motifs framework shows promise as a novel, theoretically informed approach to discourse analysis.

Original languageEnglish (US)
Title of host publicationLAK 2022 - Conference Proceedings
Subtitle of host publicationLearning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages500-506
Number of pages7
ISBN (Electronic)9781450395731
DOIs
StatePublished - Mar 21 2022
Event12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022 - Virtual, Online, United States
Duration: Mar 21 2022Mar 25 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022
Country/TerritoryUnited States
CityVirtual, Online
Period3/21/223/25/22

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Keywords

  • collaboration
  • discourse
  • networks
  • two-mode networks

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