Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models

Zongxi LI; Haoran XIE; Minhong WANG; Bian WU; Yiling HU

doi:10.1007/978-3-031-08939-8_11

Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models

Zongxi LI, Haoran XIE^*, Minhong WANG, Bian WU, Yiling HU

^*Corresponding author for this work

Department of Computing and Decision Sciences

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

Abstract

Creativity and collaboration are considered core competencies of contemporary students in different education levels and disciplines. Existing research mainly focuses on the theoretical framework for computer-supported collaborative learning, and the dialogic content analysis is mainly based on expert annotating. Consequently, there is a vacuum in the direction of AI-based discourse analysis, which prevents researchers from progressing further towards automatic monitoring and assessing collective creativity in problem-solving activities. Hence, this paper aims to fill such a gap by setting a preliminary benchmark for deep learning models in dialogue coding. More concretely, we target identifying metacognition and cognition indicators in a collaborative problem-solving process based on a collective creativity coding framework. Moreover, our work goes beyond the conventional computer-mediated and dyad (one-on-one) settings and focuses on an interactive problem-oriented activity involving multiple participants. We employ deep learning models on the full transcripts collected during the activity to validate the affordance of AI-based coding models in a real teaching and learning scenario. To the best of our knowledge, it is the first attempt to introduce AI techniques into dialogue analysis in collaborative learning.

Original language	English
Title of host publication	ICBL 2022 : Blended Learning: Engaging Students in the New Normal Era
Subtitle of host publication	Engaging Students in the New Normal Era - 15th International Conference, ICBL 2022, Proceedings
Editors	Richard Chen LI, Simon K. S. CHEUNG, Peter H. F. NG, Leung-Pun WONG, Fu Lee WANG
Publisher	Springer, Cham
Chapter	11
Pages	123-134
Number of pages	12
ISBN (Electronic)	9783031089381
ISBN (Print)	9783031089398
DOIs	https://doi.org/10.1007/978-3-031-08939-8_11
Publication status	Published - 18 Jun 2022

Publication series

Name	Lecture Notes in Computer Science
Volume	13357
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Bibliographical note

Funding Information:
Acknowledgement. The research described in this paper has been supported by Eastern Scholar Chair Professorship Fund from Shanghai Municipal Education Commission of China (No. JZ2017005) and National Natural Science Foundation of China (No. 61977023).

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

Collaborative problem solving
Discourse analysis
Deep learning
Natural language processing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-08939-8_11

https://link.springer.com/10.1007/978-3-031-08939-8_11

Cite this

LI, Z., XIE, H., WANG, M., WU, B., & HU, Y. (2022). Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models. In R. C. LI, S. K. S. CHEUNG, P. H. F. NG, L-P. WONG, & F. L. WANG (Eds.), ICBL 2022 : Blended Learning: Engaging Students in the New Normal Era: Engaging Students in the New Normal Era - 15th International Conference, ICBL 2022, Proceedings (pp. 123-134). (Lecture Notes in Computer Science; Vol. 13357). Springer, Cham. https://doi.org/10.1007/978-3-031-08939-8_11

LI, Zongxi ; XIE, Haoran ; WANG, Minhong et al. / Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models. ICBL 2022 : Blended Learning: Engaging Students in the New Normal Era: Engaging Students in the New Normal Era - 15th International Conference, ICBL 2022, Proceedings. editor / Richard Chen LI ; Simon K. S. CHEUNG ; Peter H. F. NG ; Leung-Pun WONG ; Fu Lee WANG. Springer, Cham, 2022. pp. 123-134 (Lecture Notes in Computer Science).

@inproceedings{3f64ef5bf81647d69e150c0b3a54a588,

title = "Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models",

abstract = "Creativity and collaboration are considered core competencies of contemporary students in different education levels and disciplines. Existing research mainly focuses on the theoretical framework for computer-supported collaborative learning, and the dialogic content analysis is mainly based on expert annotating. Consequently, there is a vacuum in the direction of AI-based discourse analysis, which prevents researchers from progressing further towards automatic monitoring and assessing collective creativity in problem-solving activities. Hence, this paper aims to fill such a gap by setting a preliminary benchmark for deep learning models in dialogue coding. More concretely, we target identifying metacognition and cognition indicators in a collaborative problem-solving process based on a collective creativity coding framework. Moreover, our work goes beyond the conventional computer-mediated and dyad (one-on-one) settings and focuses on an interactive problem-oriented activity involving multiple participants. We employ deep learning models on the full transcripts collected during the activity to validate the affordance of AI-based coding models in a real teaching and learning scenario. To the best of our knowledge, it is the first attempt to introduce AI techniques into dialogue analysis in collaborative learning.",

keywords = "Collaborative problem solving, Discourse analysis, Deep learning, Natural language processing",

author = "Zongxi LI and Haoran XIE and Minhong WANG and Bian WU and Yiling HU",

note = "Funding Information: Acknowledgement. The research described in this paper has been supported by Eastern Scholar Chair Professorship Fund from Shanghai Municipal Education Commission of China (No. JZ2017005) and National Natural Science Foundation of China (No. 61977023). Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.",

year = "2022",

month = jun,

day = "18",

doi = "10.1007/978-3-031-08939-8_11",

language = "English",

isbn = "9783031089398",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Cham",

pages = "123--134",

editor = "LI, {Richard Chen} and CHEUNG, {Simon K. S.} and NG, {Peter H. F.} and Leung-Pun WONG and WANG, {Fu Lee}",

booktitle = "ICBL 2022 : Blended Learning: Engaging Students in the New Normal Era",

}

LI, Z, XIE, H, WANG, M, WU, B & HU, Y 2022, Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models. in RC LI, SKS CHEUNG, PHF NG, L-P WONG & FL WANG (eds), ICBL 2022 : Blended Learning: Engaging Students in the New Normal Era: Engaging Students in the New Normal Era - 15th International Conference, ICBL 2022, Proceedings. Lecture Notes in Computer Science, vol. 13357, Springer, Cham, pp. 123-134. https://doi.org/10.1007/978-3-031-08939-8_11

Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models. / LI, Zongxi; XIE, Haoran; WANG, Minhong et al.

ICBL 2022 : Blended Learning: Engaging Students in the New Normal Era: Engaging Students in the New Normal Era - 15th International Conference, ICBL 2022, Proceedings. ed. / Richard Chen LI; Simon K. S. CHEUNG; Peter H. F. NG; Leung-Pun WONG; Fu Lee WANG. Springer, Cham, 2022. p. 123-134 (Lecture Notes in Computer Science; Vol. 13357).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

TY - GEN

T1 - Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models

AU - LI, Zongxi

AU - XIE, Haoran

AU - WANG, Minhong

AU - WU, Bian

AU - HU, Yiling

N1 - Funding Information: Acknowledgement. The research described in this paper has been supported by Eastern Scholar Chair Professorship Fund from Shanghai Municipal Education Commission of China (No. JZ2017005) and National Natural Science Foundation of China (No. 61977023). Publisher Copyright: © 2022, Springer Nature Switzerland AG.

PY - 2022/6/18

Y1 - 2022/6/18

N2 - Creativity and collaboration are considered core competencies of contemporary students in different education levels and disciplines. Existing research mainly focuses on the theoretical framework for computer-supported collaborative learning, and the dialogic content analysis is mainly based on expert annotating. Consequently, there is a vacuum in the direction of AI-based discourse analysis, which prevents researchers from progressing further towards automatic monitoring and assessing collective creativity in problem-solving activities. Hence, this paper aims to fill such a gap by setting a preliminary benchmark for deep learning models in dialogue coding. More concretely, we target identifying metacognition and cognition indicators in a collaborative problem-solving process based on a collective creativity coding framework. Moreover, our work goes beyond the conventional computer-mediated and dyad (one-on-one) settings and focuses on an interactive problem-oriented activity involving multiple participants. We employ deep learning models on the full transcripts collected during the activity to validate the affordance of AI-based coding models in a real teaching and learning scenario. To the best of our knowledge, it is the first attempt to introduce AI techniques into dialogue analysis in collaborative learning.

AB - Creativity and collaboration are considered core competencies of contemporary students in different education levels and disciplines. Existing research mainly focuses on the theoretical framework for computer-supported collaborative learning, and the dialogic content analysis is mainly based on expert annotating. Consequently, there is a vacuum in the direction of AI-based discourse analysis, which prevents researchers from progressing further towards automatic monitoring and assessing collective creativity in problem-solving activities. Hence, this paper aims to fill such a gap by setting a preliminary benchmark for deep learning models in dialogue coding. More concretely, we target identifying metacognition and cognition indicators in a collaborative problem-solving process based on a collective creativity coding framework. Moreover, our work goes beyond the conventional computer-mediated and dyad (one-on-one) settings and focuses on an interactive problem-oriented activity involving multiple participants. We employ deep learning models on the full transcripts collected during the activity to validate the affordance of AI-based coding models in a real teaching and learning scenario. To the best of our knowledge, it is the first attempt to introduce AI techniques into dialogue analysis in collaborative learning.

KW - Collaborative problem solving

KW - Discourse analysis

KW - Deep learning

KW - Natural language processing

UR - http://www.scopus.com/inward/record.url?scp=85133286143&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-08939-8_11

DO - 10.1007/978-3-031-08939-8_11

M3 - Conference paper (refereed)

SN - 9783031089398

T3 - Lecture Notes in Computer Science

SP - 123

EP - 134

BT - ICBL 2022 : Blended Learning: Engaging Students in the New Normal Era

A2 - LI, Richard Chen

A2 - CHEUNG, Simon K. S.

A2 - NG, Peter H. F.

A2 - WONG, Leung-Pun

A2 - WANG, Fu Lee

PB - Springer, Cham

ER -

LI Z, XIE H, WANG M, WU B, HU Y. Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models. In LI RC, CHEUNG SKS, NG PHF, WONG L-P, WANG FL, editors, ICBL 2022 : Blended Learning: Engaging Students in the New Normal Era: Engaging Students in the New Normal Era - 15th International Conference, ICBL 2022, Proceedings. Springer, Cham. 2022. p. 123-134. (Lecture Notes in Computer Science). Epub 2022 Jun 18. doi: 10.1007/978-3-031-08939-8_11