Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis

Linxuan Zhao; Yuanru Tan; Dragan Gašević; David Williamson Shaffer; Lixiang Yan; Riordan Alfredo; Xinyu Li; Roberto Martinez-Maldonado

doi:10.1007/978-3-031-36272-9_20

Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis

Linxuan Zhao, Yuanru Tan, Dragan Gašević, David Williamson Shaffer, Lixiang Yan, Riordan Alfredo, Xinyu Li, Roberto Martinez-Maldonado

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

3 Citations (Scopus)

Abstract

In embodied team learning activities, students are expected to learn to collaborate with others while freely moving in a physical learning space to complete a shared goal. Students can thus interact in various team configurations, resulting in increased complexity in their communication dynamics since unrelated dialogue segments can concurrently happen at different locations of the learning space. This can make it difficult to analyse students’ team dialogue solely using audio data. To address this problem, we present a study in a highly dynamic healthcare simulation setting to illustrate how spatial data can be combined with audio data to model embodied team communication. We used ordered network analysis (ONA) to model the co-occurrence and the order of coded co-located dialogue instances and identify key differences in the communication dynamics of high and low performing teams.

Original language	English
Title of host publication	24th International Conference, AIED 2023 Tokyo, Japan, July 3–7, 2023 Proceedings
Editors	Ning Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, Vania Dimitrova
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	242-254
Number of pages	13
ISBN (Electronic)	9783031362729
ISBN (Print)	9783031362712
DOIs	https://doi.org/10.1007/978-3-031-36272-9_20
Publication status	Published - 2023
Event	International Conference on Artificial Intelligence in Education 2023 - Tokyo, Japan Duration: 3 Jul 2023 → 7 Jul 2023 Conference number: 24th https://link.springer.com/book/10.1007/978-3-031-36336-8 (Proceedings) https://www.aied2023.org/ (Website)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	13916
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Artificial Intelligence in Education 2023
Abbreviated title	AIED 2023
Country/Territory	Japan
City	Tokyo
Period	3/07/23 → 7/07/23
Internet address	https://link.springer.com/book/10.1007/978-3-031-36336-8 (Proceedings) https://www.aied2023.org/ (Website)

Keywords

Collaborative Learning
Communication
Multimodality

Access to Document

10.1007/978-3-031-36272-9_20

Cite this

Zhao, L., Tan, Y., Gašević, D., Shaffer, D. W., Yan, L., Alfredo, R., Li, X., & Martinez-Maldonado, R. (2023). Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis. In N. Wang, G. Rebolledo-Mendez, N. Matsuda, O. C. Santos, & V. Dimitrova (Eds.), 24th International Conference, AIED 2023 Tokyo, Japan, July 3–7, 2023 Proceedings (pp. 242-254). (Lecture Notes in Computer Science; Vol. 13916). Springer. https://doi.org/10.1007/978-3-031-36272-9_20

Zhao, Linxuan ; Tan, Yuanru ; Gašević, Dragan et al. / Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis. 24th International Conference, AIED 2023 Tokyo, Japan, July 3–7, 2023 Proceedings. editor / Ning Wang ; Genaro Rebolledo-Mendez ; Noboru Matsuda ; Olga C. Santos ; Vania Dimitrova. Cham Switzerland : Springer, 2023. pp. 242-254 (Lecture Notes in Computer Science).

@inproceedings{dccad7e430ca4c47a1e0e0c88ce307f3,

title = "Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis",

abstract = "In embodied team learning activities, students are expected to learn to collaborate with others while freely moving in a physical learning space to complete a shared goal. Students can thus interact in various team configurations, resulting in increased complexity in their communication dynamics since unrelated dialogue segments can concurrently happen at different locations of the learning space. This can make it difficult to analyse students{\textquoteright} team dialogue solely using audio data. To address this problem, we present a study in a highly dynamic healthcare simulation setting to illustrate how spatial data can be combined with audio data to model embodied team communication. We used ordered network analysis (ONA) to model the co-occurrence and the order of coded co-located dialogue instances and identify key differences in the communication dynamics of high and low performing teams.",

keywords = "Collaborative Learning, Communication, Multimodality",

author = "Linxuan Zhao and Yuanru Tan and Dragan Ga{\v s}evi{\'c} and Shaffer, {David Williamson} and Lixiang Yan and Riordan Alfredo and Xinyu Li and Roberto Martinez-Maldonado",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Artificial Intelligence in Education 2023, AIED 2023 ; Conference date: 03-07-2023 Through 07-07-2023",

year = "2023",

doi = "10.1007/978-3-031-36272-9_20",

language = "English",

isbn = "9783031362712",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "242--254",

editor = "Ning Wang and Genaro Rebolledo-Mendez and Noboru Matsuda and Santos, {Olga C.} and Vania Dimitrova",

booktitle = "24th International Conference, AIED 2023 Tokyo, Japan, July 3–7, 2023 Proceedings",

url = "https://link.springer.com/book/10.1007/978-3-031-36336-8, https://www.aied2023.org/",

}

Zhao, L, Tan, Y, Gašević, D, Shaffer, DW, Yan, L, Alfredo, R, Li, X & Martinez-Maldonado, R 2023, Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis. in N Wang, G Rebolledo-Mendez, N Matsuda, OC Santos & V Dimitrova (eds), 24th International Conference, AIED 2023 Tokyo, Japan, July 3–7, 2023 Proceedings. Lecture Notes in Computer Science, vol. 13916, Springer, Cham Switzerland, pp. 242-254, International Conference on Artificial Intelligence in Education 2023, Tokyo, Japan, 3/07/23. https://doi.org/10.1007/978-3-031-36272-9_20

Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis. / Zhao, Linxuan; Tan, Yuanru; Gašević, Dragan et al.
24th International Conference, AIED 2023 Tokyo, Japan, July 3–7, 2023 Proceedings. ed. / Ning Wang; Genaro Rebolledo-Mendez; Noboru Matsuda; Olga C. Santos; Vania Dimitrova. Cham Switzerland: Springer, 2023. p. 242-254 (Lecture Notes in Computer Science; Vol. 13916).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis

AU - Zhao, Linxuan

AU - Tan, Yuanru

AU - Gašević, Dragan

AU - Shaffer, David Williamson

AU - Yan, Lixiang

AU - Alfredo, Riordan

AU - Li, Xinyu

AU - Martinez-Maldonado, Roberto

N1 - Conference code: 24th

PY - 2023

Y1 - 2023

N2 - In embodied team learning activities, students are expected to learn to collaborate with others while freely moving in a physical learning space to complete a shared goal. Students can thus interact in various team configurations, resulting in increased complexity in their communication dynamics since unrelated dialogue segments can concurrently happen at different locations of the learning space. This can make it difficult to analyse students’ team dialogue solely using audio data. To address this problem, we present a study in a highly dynamic healthcare simulation setting to illustrate how spatial data can be combined with audio data to model embodied team communication. We used ordered network analysis (ONA) to model the co-occurrence and the order of coded co-located dialogue instances and identify key differences in the communication dynamics of high and low performing teams.

AB - In embodied team learning activities, students are expected to learn to collaborate with others while freely moving in a physical learning space to complete a shared goal. Students can thus interact in various team configurations, resulting in increased complexity in their communication dynamics since unrelated dialogue segments can concurrently happen at different locations of the learning space. This can make it difficult to analyse students’ team dialogue solely using audio data. To address this problem, we present a study in a highly dynamic healthcare simulation setting to illustrate how spatial data can be combined with audio data to model embodied team communication. We used ordered network analysis (ONA) to model the co-occurrence and the order of coded co-located dialogue instances and identify key differences in the communication dynamics of high and low performing teams.

KW - Collaborative Learning

KW - Communication

KW - Multimodality

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U2 - 10.1007/978-3-031-36272-9_20

DO - 10.1007/978-3-031-36272-9_20

M3 - Conference Paper

AN - SCOPUS:85164954230

SN - 9783031362712

T3 - Lecture Notes in Computer Science

SP - 242

EP - 254

BT - 24th International Conference, AIED 2023 Tokyo, Japan, July 3–7, 2023 Proceedings

A2 - Wang, Ning

A2 - Rebolledo-Mendez, Genaro

A2 - Matsuda, Noboru

A2 - Santos, Olga C.

A2 - Dimitrova, Vania

PB - Springer

CY - Cham Switzerland

T2 - International Conference on Artificial Intelligence in Education 2023

Y2 - 3 July 2023 through 7 July 2023

ER -

Zhao L, Tan Y, Gašević D, Shaffer DW, Yan L, Alfredo R et al. Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis. In Wang N, Rebolledo-Mendez G, Matsuda N, Santos OC, Dimitrova V, editors, 24th International Conference, AIED 2023 Tokyo, Japan, July 3–7, 2023 Proceedings. Cham Switzerland: Springer. 2023. p. 242-254. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-36272-9_20

Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Human-centred Teamwork Analytics

The Jacobs Foundation Research Fellowship Program

Cite this

Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Projects

Human-centred Teamwork Analytics

The Jacobs Foundation Research Fellowship Program

Cite this