Socio-Semantic Network Motifs Framework for Discourse Analysis

Bodong Chen; Xinran Zhu; Hong Shui

doi:10.1145/3506860.3506893

Socio-Semantic Network Motifs Framework for Discourse Analysis

Bodong Chen, Xinran Zhu, Hong Shui

Curriculum and Instruction

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 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 language	English (US)
Title of host publication	LAK 2022 - Conference Proceedings
Subtitle of host publication	Learning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge
Publisher	Association for Computing Machinery
Pages	500-506
Number of pages	7
ISBN (Electronic)	9781450395731
DOIs	https://doi.org/10.1145/3506860.3506893
State	Published - Mar 21 2022
Event	12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022 - Virtual, Online, United States Duration: Mar 21 2022 → Mar 25 2022

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022
Country/Territory	United States
City	Virtual, Online
Period	3/21/22 → 3/25/22

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Keywords

collaboration
discourse
networks
two-mode networks

Publisher link

10.1145/3506860.3506893

Find It

Find It at U of M Libraries

Cite this

Chen, B., Zhu, X., & Shui, H. (2022). Socio-Semantic Network Motifs Framework for Discourse Analysis. In LAK 2022 - Conference Proceedings: Learning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge (pp. 500-506). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3506860.3506893

Socio-Semantic Network Motifs Framework for Discourse Analysis. / Chen, Bodong; Zhu, Xinran; Shui, Hong.
LAK 2022 - Conference Proceedings: Learning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge. Association for Computing Machinery, 2022. p. 500-506 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, B, Zhu, X & Shui, H 2022, Socio-Semantic Network Motifs Framework for Discourse Analysis. in LAK 2022 - Conference Proceedings: Learning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 500-506, 12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022, Virtual, Online, United States, 3/21/22. https://doi.org/10.1145/3506860.3506893

Chen B, Zhu X, Shui H. Socio-Semantic Network Motifs Framework for Discourse Analysis. In LAK 2022 - Conference Proceedings: Learning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge. Association for Computing Machinery. 2022. p. 500-506. (ACM International Conference Proceeding Series). doi: 10.1145/3506860.3506893

Chen, Bodong ; Zhu, Xinran ; Shui, Hong. / Socio-Semantic Network Motifs Framework for Discourse Analysis. LAK 2022 - Conference Proceedings: Learning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge. Association for Computing Machinery, 2022. pp. 500-506 (ACM International Conference Proceeding Series).

@inproceedings{468044b1017e45c29b28e6b8853debb1,

title = "Socio-Semantic Network Motifs Framework for Discourse Analysis",

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. ",

keywords = "collaboration, discourse, networks, two-mode networks",

author = "Bodong Chen and Xinran Zhu and Hong Shui",

note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022 ; Conference date: 21-03-2022 Through 25-03-2022",

year = "2022",

month = mar,

day = "21",

doi = "10.1145/3506860.3506893",

language = "English (US)",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "500--506",

booktitle = "LAK 2022 - Conference Proceedings",

}

TY - GEN

T1 - Socio-Semantic Network Motifs Framework for Discourse Analysis

AU - Chen, Bodong

AU - Zhu, Xinran

AU - Shui, Hong

PY - 2022/3/21

Y1 - 2022/3/21

N2 - 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.

AB - 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.

KW - collaboration

KW - discourse

KW - networks

KW - two-mode networks

UR - http://www.scopus.com/inward/record.url?scp=85126231137&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85126231137&partnerID=8YFLogxK

U2 - 10.1145/3506860.3506893

DO - 10.1145/3506860.3506893

M3 - Conference contribution

AN - SCOPUS:85126231137

T3 - ACM International Conference Proceeding Series

SP - 500

EP - 506

BT - LAK 2022 - Conference Proceedings

PB - Association for Computing Machinery

T2 - 12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022

Y2 - 21 March 2022 through 25 March 2022

ER -

Socio-Semantic Network Motifs Framework for Discourse Analysis

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this