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

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

3 Citations (Scopus)


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 languageEnglish
Title of host publicationICBL 2022 : Blended Learning: Engaging Students in the New Normal Era
Subtitle of host publicationEngaging Students in the New Normal Era - 15th International Conference, ICBL 2022, Proceedings
EditorsRichard Chen LI, Simon K. S. CHEUNG, Peter H. F. NG, Leung-Pun WONG, Fu Lee WANG
PublisherSpringer, Cham
Number of pages12
ISBN (Electronic)9783031089381
ISBN (Print)9783031089398
Publication statusPublished - 18 Jun 2022

Publication series

NameLecture Notes in Computer Science
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.


  • Collaborative problem solving
  • Discourse analysis
  • Deep learning
  • Natural language processing


Dive into the research topics of 'Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models'. Together they form a unique fingerprint.

Cite this