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. 2025 Jun 5;20(6):e0324731.
doi: 10.1371/journal.pone.0324731. eCollection 2025.

Self-motivated effects of teachers' supportive behaviors on students' intentions of online continuous learning -- based on educational digital transformation

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Self-motivated effects of teachers' supportive behaviors on students' intentions of online continuous learning -- based on educational digital transformation

Zhiqun Ouyang et al. PLoS One. .

Abstract

The study explored the factors influencing students' online continuous learning intentions (CLI) based on educational digital transformation (EDT). The theoretical model was based on the technology acceptance model (TAM), and two dimensions were proposed: flow experience (FE) and teachers' supportive behaviors (TSBs). It studied how these variables directly or indirectly affected students' intentions to CLI within EDT. The constructed model was validated by exploiting a partial least squares structural equation modeling approach (PLS-SEM) based on the valid data from 614 students from five private universities in China. The results suggested that (a) the construct TSBs, including teachers' social support, teachers' intellectual support, and teachers' instrument support, had a positive impact on students' intentions of online continuing learning within EDT; (b) perceived ease of use (PEU) had a positive effect on perceived usefulness (PU), flow experience (FE) and CLI; (c) perceived usefulness (PU) had an effect on FE and CLI; (d) flow experience (FE) had a positive effect on CLI. The findings offered valuable insights for academicians, higher institution administrators, researchers, and higher education policy-makers in enhancing students' learning based on educational digital transformation.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Research model.
Fig 2
Fig 2. Standardized path coefficients and significance.

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References

    1. Goopio J, Cheung C. The MOOC dropout phenomenon and retention strategies. J Teach Travel Tour. 2020;21(2):177–97. doi: 10.1080/15313220.2020.1809050 - DOI
    1. Zhao P, et al.. Precise recognition model for mobile learning procrastination based on backpropagation neural network. Sens Mater. 2023;35.
    1. Al-Emran M, Arpaci I, Salloum SA. An empirical examination of continuous intention to use m-learning: An integrated model. Educ Inf Technol. 2020;25:2899–918.
    1. Prenkaj B, Velardi P, Stilo G, Distante D, Faralli S. A survey of machine learning approaches for student dropout prediction in online courses. ACM Comput Surv. 2020;53(3):1–34. doi: 10.1145/3388792 - DOI
    1. Wang P, Zhao P, Li Y. Design of education information platform on education big data visualization. Wirel Commun Mob Comput. 2022;2022:1–13. doi: 10.1155/2022/6779105 - DOI