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Determinants of Active Online Learning in the Smart Learning Environment: An Empirical Study with PLS-SEM

Author

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  • Shaofeng Wang

    (Smart Learning Institute, Beijing Normal University, Beijing 100875, China
    School of Logistics and e-Commerce, Zhejiang Wanli University, Ningbo 315000, China)

  • Gaojun Shi

    (School of Education, Hangzhou Normal University, Hangzhou 311121, China)

  • Mingjie Lu

    (Research Center for Intelligent Social Governance, Zhejiang Lab, Hangzhou 310005, China)

  • Ruyi Lin

    (School of Education, Hangzhou Normal University, Hangzhou 311121, China)

  • Junfeng Yang

    (School of Education, Hangzhou Normal University, Hangzhou 311121, China)

Abstract

A smart learning environment, featuring personalization, real-time feedback, and intelligent interaction, provides the primary conditions for actively participating in online education. Identifying the factors that influence active online learning in a smart learning environment is critical for proposing targeted improvement strategies and enhancing their active online learning effectiveness. This study constructs the research framework of active online learning with theories of learning satisfaction, the Technology Acceptance Model (TAM), and a smart learning environment. We hypothesize that the following factors will influence active online learning: Typical characteristics of a smart learning environment, perceived usefulness and ease of use, social isolation, learning expectations, and complaints. A total of 528 valid questionnaires were collected through online platforms. The partial least squares structural equation modeling (PLS-SEM) analysis using SmartPLS 3 found that: (1) The personalization, intelligent interaction, and real-time feedback of the smart learning environment all have a positive impact on active online learning; (2) the perceived ease of use and perceived usefulness in the technology acceptance model (TAM) positively affect active online learning; (3) innovatively discovered some new variables that affect active online learning: Learning expectations positively impact active online learning, while learning complaints and social isolation negatively affect active online learning. Based on the results, this study proposes the online smart teaching model and discusses how to promote active online learning in a smart environment.

Suggested Citation

  • Shaofeng Wang & Gaojun Shi & Mingjie Lu & Ruyi Lin & Junfeng Yang, 2021. "Determinants of Active Online Learning in the Smart Learning Environment: An Empirical Study with PLS-SEM," Sustainability, MDPI, vol. 13(17), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9923-:d:628594
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    References listed on IDEAS

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    1. Armstrong, J. Scott & Overton, Terry S., 1977. "Estimating Nonresponse Bias in Mail Surveys," MPRA Paper 81694, University Library of Munich, Germany.
    2. Shaofeng Wang & Ahmed Tlili & Lixin Zhu & Junfeng Yang, 2021. "Do Playfulness and University Support Facilitate the Adoption of Online Education in a Crisis? COVID-19 as a Case Study Based on the Technology Acceptance Model," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    3. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    4. Marko Sarstedt & Jun-Hwa Cheah, 2019. "Partial least squares structural equation modeling using SmartPLS: a software review," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 196-202, September.
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    Cited by:

    1. Mahdi Mohammed Alamri, 2023. "A Model of E-Learning through Achievement Motivation and Academic Achievement among University Students in Saudi Arabia," Sustainability, MDPI, vol. 15(3), pages 1-25, January.
    2. Norah Banafi, 2023. "Knowledge Attitude and Practice of Students Towards Online Communication in EFL," World Journal of English Language, Sciedu Press, vol. 13(6), pages 1-25, July.

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