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A Data-Driven Optimized Mechanism for Improving Online Collaborative Learning: Taking Cognitive Load into Account

Author

Listed:
  • Linjie Zhang

    (Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China)

  • Xizhe Wang

    (Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China)

  • Tao He

    (School of Information Technology in Education, South China Normal University, Guangzhou 510631, China)

  • Zhongmei Han

    (Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China)

Abstract

Research on online collaborative learning has explored various methods of collaborative improvement. Recently, learning analytics have been increasingly adopted for ascertaining learners’ states and promoting collaborative performance. However, little effort has been made to investigate the transformation of collaborative states or to consider cognitive load as an essential factor for collaborative intervention. By bridging collaborative cognitive load theory and system dynamics modeling methods, this paper revealed the transformation of online learners’ collaborative states through data analysis, and then proposed an optimized mechanism to ameliorate online collaboration. A quasi-experiment was conducted with 91 college students to examine the potential of the optimized mechanism in collaborative state transformation, awareness of collaboration, learning achievement, and cognitive load. The promising results demonstrated that students learning with the optimized mechanism performed significantly differently in collaboration and knowledge acquisition, and no additional burden in cognitive load was noted.

Suggested Citation

  • Linjie Zhang & Xizhe Wang & Tao He & Zhongmei Han, 2022. "A Data-Driven Optimized Mechanism for Improving Online Collaborative Learning: Taking Cognitive Load into Account," IJERPH, MDPI, vol. 19(12), pages 1-18, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:12:p:6984-:d:833362
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    References listed on IDEAS

    as
    1. Xuemei Wu & Zhenzhen He & Mingxi Li & Zhongmei Han & Changqin Huang, 2022. "Identifying Learners’ Interaction Patterns in an Online Learning Community," IJERPH, MDPI, vol. 19(4), pages 1-20, February.
    2. Mattia Bongini & Massimo Fornasier & Francesco Rossi & Francesco Solombrino, 2017. "Mean-Field Pontryagin Maximum Principle," Journal of Optimization Theory and Applications, Springer, vol. 175(1), pages 1-38, October.
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