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Investigating Sequence Patterns of Collaborative Problem-Solving Behavior in Online Collaborative Discussion Activity

Author

Listed:
  • Yafeng Zheng

    (School of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou 450046, China)

  • Haogang Bao

    (Faculty of Education, Beijing Normal University, Beijing 100875, China)

  • Jun Shen

    (School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia)

  • Xuesong Zhai

    (College of Education, Zhejiang University, Hangzhou 310058, China)

Abstract

Collaborative problem solving (CPS) is an influential human behavior affecting working performance and well-being. Previous studies examined CPS behavior from the perspective of either social or cognitive dimensions, which leave a research gap from the interactive perspective. In addition, the traditional sequence analysis method failed to combine time sequences and sub-problem sequences together while analyzing behavioral patterns in CPS. This study proposes a developed schema for the multidimensional analysis of CPS. A combination sequential analysis approach that comprises time sequences and sub-problem sequences is also employed to explore CPS patterns. A total of 191 students were recruited and randomly grouped into 38 teams (four to six students per team) in the online collaborative discussion activity. Their discussion transcripts were coded while they conducted CPS, followed by the assessment of high- and low- performance groups according to the developed schema and sequential analysis. With the help of the new analysis method, the findings indicate that a deep exploratory discussion is generated from conflicting viewpoints, which promotes improved problem-solving outcomes and perceptions. In addition, evidence-based rationalization can motivate collaborative behavior effectively. The results demonstrated the potential power of automatic sequential analysis with multidimensional behavior and its ability to provide quantitative descriptions of group interactions in the investigated threaded discussions.

Suggested Citation

  • Yafeng Zheng & Haogang Bao & Jun Shen & Xuesong Zhai, 2020. "Investigating Sequence Patterns of Collaborative Problem-Solving Behavior in Online Collaborative Discussion Activity," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8522-:d:428552
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    References listed on IDEAS

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    1. Jeffrey Mo, 2017. "Collaborative problem solving," PISA in Focus 78, OECD Publishing.
    2. Anette Oxenswärdh & Ulrika Persson-Fischier, 2020. "Mapping Master Students’ Processes of Problem Solving and Learning in Groups in Sustainability Education," Sustainability, MDPI, vol. 12(13), pages 1-14, June.
    3. Filippina Risopoulos-Pichler & Fedor Daghofer & Gerald Steiner, 2020. "Competences for Solving Complex Problems: A Cross-Sectional Survey on Higher Education for Sustainability Learning and Transdisciplinarity," Sustainability, MDPI, vol. 12(15), pages 1-15, July.
    4. Neubert, Jonas C. & Mainert, Jakob & Kretzschmar, André & Greiff, Samuel, 2015. "The Assessment of 21st Century Skills in Industrial and Organizational Psychology: Complex and Collaborative Problem Solving," Industrial and Organizational Psychology, Cambridge University Press, vol. 8(2), pages 238-268, June.
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    Cited by:

    1. Peijie Jiang & Xiaomeng Ruan & Zirong Feng & Yanyun Jiang & Bin Xiong, 2023. "Research on Online Collaborative Problem-Solving in the Last 10 Years: Current Status, Hotspots, and Outlook—A Knowledge Graph Analysis Based on CiteSpace," Mathematics, MDPI, vol. 11(10), pages 1-20, May.

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