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.
|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|
|Number of pages||12|
|Publication status||Published - 18 Jun 2022|
|Name||Lecture Notes in Computer Science|
Bibliographical noteFunding 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).
© 2022, Springer Nature Switzerland AG.
- Collaborative problem solving
- Discourse analysis
- Deep learning
- Natural language processing