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

Xiaodan Wang, Xin Wang, Tinghui Huang, Limin Liu, Xiaohui Chen, Xin Yang, Jia Lu and Hanxi Wang ()
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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

Sustainability, 2023, vol. 16, issue 1, 1-16

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.

Keywords: collaborative learning (CL); socially regulated learning (SoRL); latent profile analysis (LPA); collaborative learning motivation (CLM) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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