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

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Book cover Blended Learning: Engaging Students in the New Normal Era (ICBL 2022)

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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.

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Notes

  1. 1.

    https://github.com/Embedding/Chinese-Word-Vectors.

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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).

Author information

Authors and Affiliations

  1. School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China

    Zongxi Li

  2. Department of Computing and Decision Sciences, Lingnan University, Hong Kong, China

    Haoran Xie

  3. Faculty of Education, The University of Hong Kong, Hong Kong, China

    Minhong Wang

  4. Department of Educational Information Technology, East China Normal University, Shanghai, China

    Bian Wu & Yiling Hu

Authors
  1. Zongxi Li
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  2. Haoran Xie
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  3. Minhong Wang
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  4. Bian Wu
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  5. Yiling Hu
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Corresponding author

Correspondence to Haoran Xie .

Editor information

Editors and Affiliations

  1. The Hong Kong Polytechnic University, Hong Kong, China

    Richard Chen Li

  2. Hong Kong Metropolitan University, Hong Kong, China

    Simon K. S. Cheung

  3. The Hong Kong Polytechnic University, Hong Kong, China

    Peter H. F. Ng

  4. Tung Wah College, Hong Kong, China

    Leung-Pun Wong

  5. Hong Kong Metropolitan University, Hong Kong, China

    Fu Lee Wang

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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: Li, R.C., Cheung, S.K.S., Ng, P.H.F., Wong, LP., Wang, F.L. (eds) Blended Learning: Engaging Students in the New Normal Era. ICBL 2022. Lecture Notes in Computer Science, vol 13357. Springer, Cham. https://doi.org/10.1007/978-3-031-08939-8_11

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  • DOI: https://doi.org/10.1007/978-3-031-08939-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08938-1

  • Online ISBN: 978-3-031-08939-8

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