Connectivity for Knowledge Building: A Framework of Socio-Semantic Network Motif Analysis

Bodong Chen

Connectivity for Knowledge Building: A Framework of Socio-Semantic Network Motif Analysis

Bodong Chen

Curriculum and Instruction

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

Abstract

Productive knowledge-building discourse requires students to work as a collective to solve shared problems. Students need to engage with each other socially while continually producing ideas, debugging understanding, and rising above multiple ideas to build high-level knowledge structures. To investigate complex socio-cognitive dynamics in knowledge-building discourse, this paper proposes to model discourse as a socio-semantic network (SSN) and then use network motifs-defined as recurring, significant subgraphs-to characterize the network and the discourse. A framework of SSN motif analysis is proposed according to knowledge-building discourse processes. To demonstrate the utility of the framework, we applied it to discourse data from two classes. Results revealed distinct motif profiles of two classes across two discourse phases. The framework of SSN motif analysis shows promise as a novel, theoretically informed approach to discourse analysis.

Original language	English (US)
Title of host publication	International Collaboration toward Educational Innovation for All
Subtitle of host publication	Overarching Research, Development, and Practices - 15th International Conference on Computer-Supported Collaborative Learning, CSCL 2022
Editors	Armin Weinberger, Wenli Chen, Davinia Hernandez-Leo, Bodong Chen
Publisher	International Society of the Learning Sciences (ISLS)
Pages	19-26
Number of pages	8
ISBN (Electronic)	9781737330646
State	Published - 2022
Event	15th International Conference on Computer-Supported Collaborative Learning, CSCL 2022 - Virtual, Online, Japan Duration: Jun 6 2022 → Jun 10 2022

Publication series

Name	Proceedings of International Conference of the Learning Sciences, ICLS
ISSN (Print)	1814-9316

Conference

Conference	15th International Conference on Computer-Supported Collaborative Learning, CSCL 2022
Country/Territory	Japan
City	Virtual, Online
Period	6/6/22 → 6/10/22

Bibliographical note

Publisher Copyright:
© ISLS.

Keywords

discourse analysis
knowledge building
learning analytics
network analysis

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Chen, B. (2022). Connectivity for Knowledge Building: A Framework of Socio-Semantic Network Motif Analysis. In A. Weinberger, W. Chen, D. Hernandez-Leo, & B. Chen (Eds.), International Collaboration toward Educational Innovation for All: Overarching Research, Development, and Practices - 15th International Conference on Computer-Supported Collaborative Learning, CSCL 2022 (pp. 19-26). (Proceedings of International Conference of the Learning Sciences, ICLS). International Society of the Learning Sciences (ISLS).

Connectivity for Knowledge Building: A Framework of Socio-Semantic Network Motif Analysis. / Chen, Bodong.
International Collaboration toward Educational Innovation for All: Overarching Research, Development, and Practices - 15th International Conference on Computer-Supported Collaborative Learning, CSCL 2022. ed. / Armin Weinberger; Wenli Chen; Davinia Hernandez-Leo; Bodong Chen. International Society of the Learning Sciences (ISLS), 2022. p. 19-26 (Proceedings of International Conference of the Learning Sciences, ICLS).

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

Chen, B 2022, Connectivity for Knowledge Building: A Framework of Socio-Semantic Network Motif Analysis. in A Weinberger, W Chen, D Hernandez-Leo & B Chen (eds), International Collaboration toward Educational Innovation for All: Overarching Research, Development, and Practices - 15th International Conference on Computer-Supported Collaborative Learning, CSCL 2022. Proceedings of International Conference of the Learning Sciences, ICLS, International Society of the Learning Sciences (ISLS), pp. 19-26, 15th International Conference on Computer-Supported Collaborative Learning, CSCL 2022, Virtual, Online, Japan, 6/6/22.

Chen B. Connectivity for Knowledge Building: A Framework of Socio-Semantic Network Motif Analysis. In Weinberger A, Chen W, Hernandez-Leo D, Chen B, editors, International Collaboration toward Educational Innovation for All: Overarching Research, Development, and Practices - 15th International Conference on Computer-Supported Collaborative Learning, CSCL 2022. International Society of the Learning Sciences (ISLS). 2022. p. 19-26. (Proceedings of International Conference of the Learning Sciences, ICLS).

Chen, Bodong. / Connectivity for Knowledge Building : A Framework of Socio-Semantic Network Motif Analysis. International Collaboration toward Educational Innovation for All: Overarching Research, Development, and Practices - 15th International Conference on Computer-Supported Collaborative Learning, CSCL 2022. editor / Armin Weinberger ; Wenli Chen ; Davinia Hernandez-Leo ; Bodong Chen. International Society of the Learning Sciences (ISLS), 2022. pp. 19-26 (Proceedings of International Conference of the Learning Sciences, ICLS).

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abstract = "Productive knowledge-building discourse requires students to work as a collective to solve shared problems. Students need to engage with each other socially while continually producing ideas, debugging understanding, and rising above multiple ideas to build high-level knowledge structures. To investigate complex socio-cognitive dynamics in knowledge-building discourse, this paper proposes to model discourse as a socio-semantic network (SSN) and then use network motifs-defined as recurring, significant subgraphs-to characterize the network and the discourse. A framework of SSN motif analysis is proposed according to knowledge-building discourse processes. To demonstrate the utility of the framework, we applied it to discourse data from two classes. Results revealed distinct motif profiles of two classes across two discourse phases. The framework of SSN motif analysis shows promise as a novel, theoretically informed approach to discourse analysis.",

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language = "English (US)",

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AB - Productive knowledge-building discourse requires students to work as a collective to solve shared problems. Students need to engage with each other socially while continually producing ideas, debugging understanding, and rising above multiple ideas to build high-level knowledge structures. To investigate complex socio-cognitive dynamics in knowledge-building discourse, this paper proposes to model discourse as a socio-semantic network (SSN) and then use network motifs-defined as recurring, significant subgraphs-to characterize the network and the discourse. A framework of SSN motif analysis is proposed according to knowledge-building discourse processes. To demonstrate the utility of the framework, we applied it to discourse data from two classes. Results revealed distinct motif profiles of two classes across two discourse phases. The framework of SSN motif analysis shows promise as a novel, theoretically informed approach to discourse analysis.

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Connectivity for Knowledge Building: A Framework of Socio-Semantic Network Motif Analysis

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