IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i4p2245-d751022.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/4/2245/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/4/2245/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Youlai Zeng & Jiaxin Wei & Wenting Zhang & Nan Sun, 2024. "Online class-related boredom and perceived academic achievement among college students: the roles of gender and school motivation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    2. Jiankang He & Xue Yang & Mingxuan Du & Chengjia Zhao & Xin Wang & Guohua Zhang & Honglei Peng, 2022. "Prospective Association between Smartphone Addiction and Perceived Stress and Moderation of Boredom during COVID-19 in China," IJERPH, MDPI, vol. 19(22), pages 1-10, November.
    3. Mohammed Rafiqul Islam & Rimon Sarker & Rebaka Sultana & Md. Faisal-E-Alam & Rui Alexandre Castanho & Daniel Meyer, 2023. "Understanding the COVID-19 Pandemic’s Impact on E-Learner Satisfaction at the Tertiary Level," Sustainability, MDPI, vol. 15(8), pages 1-14, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2245-:d:751022. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.