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Predicting Chinese University Students’ E-Learning Acceptance and Self-Regulation in Online English Courses: Evidence From Emergency Remote Teaching (ERT) During COVID-19

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  • Sijing Zhou
  • Yu Zhou
  • Huiling Zhu

Abstract

A growing concern for online course learning is to what extent learners are concentrated and self-regulated when they are isolated from their classmates and instructors. To address this issue, this study collected both quantitative and qualitative data from a sample of 580 Chinese university learners from varied majors, who were taking online English courses in Emergency Remote Teaching (ERT) mode during COVID-19. This study identified specific psychological and contextual factors that impact learners’ e-learning acceptance and online self-regulation, based upon Technology Acceptance Model (TAM). Learners’ actual use of three sub-processes of self-regulated strategies, namely, goal setting, task strategies, and self-evaluation was also examined. Partial least squares (PLS)-structural equation modeling (SEM) technique was used to test hypotheses and proposed research model. The quantitative results indicate that media richness, as a contextual factor, and social presence and flow, as two typical psychological factors, are determining antecedents that impact Chinese learners’ e-learning acceptance. Meanwhile, quantitative findings show that learners’ behavioral intention to use e-learning is a main contributor of their use of all three sub-processes of self-regulated learning strategies. Furthermore, thematic analysis was conducted to study the qualitative data, revealing that learners held rather divided and mixed perceptions regarding online learning experience. These findings have important implications for effective online English course design and implementation.

Suggested Citation

  • Sijing Zhou & Yu Zhou & Huiling Zhu, 2021. "Predicting Chinese University Students’ E-Learning Acceptance and Self-Regulation in Online English Courses: Evidence From Emergency Remote Teaching (ERT) During COVID-19," SAGE Open, , vol. 11(4), pages 21582440211, November.
    • Handle: RePEc:sae:sagope:v:11:y:2021:i:4:p:21582440211061379
    DOI: 10.1177/21582440211061379
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    References listed on IDEAS

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    1. Richard L. Daft & Robert H. Lengel, 1986. "Organizational Information Requirements, Media Richness and Structural Design," Management Science, INFORMS, vol. 32(5), pages 554-571, May.
    2. Reinartz, Werner & Haenlein, Michael & Henseler, Jörg, 2009. "An empirical comparison of the efficacy of covariance-based and variance-based SEM," International Journal of Research in Marketing, Elsevier, vol. 26(4), pages 332-344.
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    Cited by:

    1. Haozhe Jiang & A. Y. M. Atiquil Islam & Xiaoqing Gu & Jonathan Michael Spector & Suting Chen, 2022. "Technology-Enabled E-Learning Platforms in Chinese Higher Education During the Pandemic Age of COVID-19," SAGE Open, , vol. 12(2), pages 21582440221, May.
    2. Teng Yu & Jian Dai & Chengliang Wang, 2023. "Adoption of blended learning: Chinese university students’ perspectives," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    3. Sijing Zhou & Huiling Zhu & Yu Zhou, 2022. "Impact of Teenage EFL Learners’ Psychological Needs on Learning Engagement and Behavioral Intention in Synchronous Online English Courses," Sustainability, MDPI, vol. 14(17), pages 1-17, August.

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