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Identifying Learners’ Interaction Patterns in an Online Learning Community

Author

Listed:
  • Xuemei Wu

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

  • Zhenzhen He

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

  • Mingxi Li

    (School of Foreign Studies, 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)

  • Changqin Huang

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

Abstract

The interactions among all members of an online learning community significantly impact collaborative reflection (co-reflection). Although the relationship between learners’ roles and co-reflection levels has been explored by previous researchers, it remains unclear when and with whom learners at different co-reflection levels tend to interact. This study adopted multiple methods to examine the interaction patterns of diverse roles among learners with different co-reflection levels based on 11,912 posts. First, the deep learning technique was applied to assess learners’ co-reflection levels. Then, a social network analysis (SNA) was conducted to identify the emergent roles of learners. Furthermore, a lag sequence analysis (LSA) was employed to reveal the interaction patterns of the emergent roles among learners with different co-reflection levels. The results showed that most learners in an online learning community reached an upper-middle co-reflection level while playing an inactive role in the co-reflection process. Moreover, higher-level learners were superior in dialog with various roles and were more involved in self-rethinking during the co-reflection process. In particular, they habitually began communication with peers and then with the teacher. Based on these findings, some implications for facilitating online co-reflection from the perspective of roles is also discussed.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2245-:d:751022
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    References listed on IDEAS

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    1. Jianhui Yu & Changqin Huang & Zhongmei Han & Tao He & Ming Li, 2020. "Investigating the Influence of Interaction on Learning Persistence in Online Settings: Moderation or Mediation of Academic Emotions?," IJERPH, MDPI, vol. 17(7), pages 1-21, March.
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    Cited by:

    1. Siti Fardaniah Abdul Aziz & Norashikin Hussein & Nor Azilah Husin & Muhamad Ariff Ibrahim, 2022. "Trainers’ Characteristics Affecting Online Training Effectiveness: A Pre-Experiment among Students in a Malaysian Secondary School," Sustainability, MDPI, vol. 14(17), pages 1-24, September.
    2. 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.

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