Technology-Enabled E-Learning Platforms in Chinese Higher Education During the Pandemic Age of COVID-19

Haozhe Jiang, A. Y. M. Atiquil Islam, Xiaoqing Gu, Jonathan Michael Spector and Suting Chen

SAGE Open, 2022, vol. 12, issue 2, 21582440221095085

Abstract: The problem motivating this study is the fact that almost 19.70 million university students in China have been required to engage in e-learning under the government’s initiative of “Classes are Suspended, yet Learning is still Ongoing†during the COVID-19 epidemic, coupled with varied responses, uneven adoption of e-learning platforms and varying degrees of satisfaction toward them. Using the online database adoption and satisfaction (ODAS) model, this study examines the determinants which impact university students’ adoption of and satisfaction with e-learning platforms at this particular time in China. The ODAS model was also cross-validated using gender as a moderating variable. A purposive sampling procedure was used to survey a total of 1,136 students from six universities in five provinces or municipalities of China. The data for this survey were estimated using the Rasch model and structural equation modeling. Results exhibit that students’ adoption of and satisfaction with e-learning platforms were significantly measured by their computer self-efficacy, their intention to use e-learning platforms, and their perceived ease of use and perceived usefulness of these platforms, while the relationships among these components were moderated by gender differences. This empirically-based cross-validation of the ODAS provides recommendations for future studies, including practical implications for e-learning. This current study contributes to the body of knowledge in evaluating e-learning platforms during the COVID-19 epidemic.

Keywords: e-learning; COVID-19 epidemic; online database adoption and satisfaction model; structural equation modeling; Rasch model (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:12:y:2022:i:2:p:21582440221095085

DOI: 10.1177/21582440221095085

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