IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2023i1p181-d1306724.html
   My bibliography  Save this article

Relationship between the Latent Profile of Online Socially Regulated Learning and Collaborative Learning Motivation

Author

Listed:
  • Xiaodan Wang

    (School of Computer Engineering, Guilin University of Electronic Technology, Beihai 536000, China)

  • Xin Wang

    (School of Computer Engineering, Guilin University of Electronic Technology, Beihai 536000, China)

  • Tinghui Huang

    (School of Computer Engineering, Guilin University of Electronic Technology, Beihai 536000, China)

  • Limin Liu

    (School of Computer Engineering, Guilin University of Electronic Technology, Beihai 536000, China)

  • Xiaohui Chen

    (School of Information Science and Technology, Northeast Normal University, Changchun 130117, China)

  • Xin Yang

    (Faculty of Education, Northeast Normal University, Changchun 130024, China)

  • Jia Lu

    (School of Computer Engineering, Guilin University of Electronic Technology, Beihai 536000, China)

  • Hanxi Wang

    (School of Geographical Sciences, Harbin Normal University, Harbin 150025, China)

Abstract

Socially regulated learning (SoRL) is an important way to maintain the sustainable development of collaborative learning (CL). Usually, learners can achieve sustainable and high-quality SoRL with the intervention of teachers. To improve the appropriateness of the intervention, teachers need to clarify the profiles of SoRL to which learners belong, as well as the influence of collaborative learning motivation (CLM) and the relevant background variables. This study used three non-duplicate samples to provide evidence for the psychometric properties of the SoRL and CLM scales through item analysis, exploratory factor analysis (sample 1, n = 531), and confirmatory factor analysis (sample 2, n = 1278). The profiles of SoRL among university students were determined through latent profile analysis (sample 3, n = 909). This study identified three profiles of regulation (strong SoRL, progressive SoRL, and weak SoRL). The analysis of multivariate variance and multiple logistic regression methods further explored the differences in the dimensions of SoRL structures across different profiles and the extent to which CLM and background variables predicted profiles. The results showed that collaborative motivation (CM) and learning motivation (LM) were the predictors of learners’ transformation from a low regulation level to a medium regulation level. CM, LM, altruistic motivation, and major background were the predictors of learners’ transition from the medium regulation level to the high regulation level. Accordingly, teachers can provide learners with an appropriate external intervention to promote the improvement of SoRL. This study contributes to improving learners’ SoRL levels and promoting the sustainable development of education. In the future, the changing characteristics of learners’ SoRL profiles over time will be explored, and the application of learning process data will be strengthened.

Suggested Citation

  • Xiaodan Wang & Xin Wang & Tinghui Huang & Limin Liu & Xiaohui Chen & Xin Yang & Jia Lu & Hanxi Wang, 2023. "Relationship between the Latent Profile of Online Socially Regulated Learning and Collaborative Learning Motivation," Sustainability, MDPI, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:181-:d:1306724
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/1/181/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/1/181/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guangmin Zhang & Yang Zhang & Wei Tian & Huimin Li & Ping Guo & Fangfang Ye, 2021. "Bridging the Intention–Behavior Gap: Effect of Altruistic Motives on Developers’ Action towards Green Redevelopment of Industrial Brownfields," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    2. Iseul Choi & Donald Moynihan, 2019. "How to foster collaborative performance management? Key factors in the US federal agencies," Public Management Review, Taylor & Francis Journals, vol. 21(10), pages 1538-1559, October.
    Full references (including those not matched with items on IDEAS)

    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. Yang Zhang & Xiaomeng Luo & Shuqing Wang & Chang Liu, 2023. "Green Redevelopment of Industrial Brownfields: Driving Mechanism and Model," Sustainability, MDPI, vol. 15(23), pages 1-15, November.
    2. Milena Vainieri & Francesca Ferrè & Stefania Manetti, 2021. "An Integrated Framework to Measure the Performance of Inter-Organizational Programme on Health Technology Assessment," Sustainability, MDPI, vol. 13(7), pages 1-17, March.
    3. Maria Bostenaru Dan & Magdalena Maria Bostenaru-Dan, 2021. "Greening the Brownfields of Thermal Power Plants in Rural Areas, an Example from Romania, Set in the Context of Developments in the Industrialized Country of Germany," Sustainability, MDPI, vol. 13(7), pages 1-18, March.
    4. Prescott C. Ensign & Shawn Roy & Tom Brzustowski, 2021. "Decisions by Key Office Building Stakeholders to Build or Retrofit Green in Toronto’s Urban Core," Sustainability, MDPI, vol. 13(12), pages 1-31, June.
    5. Carmine Bianchi & Robinson Salazar Rua, 2022. "A feedback view of behavioural distortions from perceived public service gaps at ‘street‐level’ policy implementation: The case of unintended outcomes in public schools," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(1), pages 63-84, January.
    6. Yang Zhang & Fangfang Ye & Guangmin Zhang, 2021. "Determinants of Green Regeneration of Industrial Brownfield under Ecological Security Constraints: Model and Empirical Analysis Based on Grounded Theory," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    7. Xintian Wang & Zhangchi Wang & Yan Li, 2022. "Internet Use on Closing Intention–Behavior Gap in Green Consumption—A Mediation and Moderation Theoretical Model," IJERPH, MDPI, vol. 20(1), pages 1-20, December.

    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:jsusta:v:16:y:2023:i:1:p:181-:d:1306724. 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.