Higher Education in China during the Pandemic: Analyzing Online Self-Learning Motivation Using Bayesian Networks

Jiang Li, Yating Chang, Shaobo Liu (), Chang Cai (), Qingping Zhou, Xiaoxi Cai, Wenbo Lai, Jialing Qi, Yifeng Ji and Yudan Liu
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Jiang Li: School of Architecture and Art, Central South University, Changsha 410083, China
Yating Chang: School of Architecture and Art, Central South University, Changsha 410083, China
Shaobo Liu: School of Architecture and Art, Central South University, Changsha 410083, China
Chang Cai: School of Mathematics and Statistics, Central South University, Changsha 410083, China
Qingping Zhou: School of Mathematics and Statistics, Central South University, Changsha 410083, China
Xiaoxi Cai: School of Art and Design, Hunan First Normal University, Changsha 410205, China
Wenbo Lai: School of Architecture, South China University of Technology, Guangzhou 510006, China
Jialing Qi: School of Architecture and Art, Central South University, Changsha 410083, China
Yifeng Ji: School of Architecture and Art, Central South University, Changsha 410083, China
Yudan Liu: School of Architecture and Art, Central South University, Changsha 410083, China

Sustainability, 2024, vol. 16, issue 17, 1-16

Abstract: The COVID-19 pandemic has led to an unprecedented shift towards online learning, compelling university students worldwide to engage in self-directed learning within remote environments. Despite the increasing importance of online education, the factors driving students’ motivation for self-directed online learning, particularly those involving economic incentives, have not been thoroughly explored. This study aims to address this gap by analyzing large-scale data collected from 19,023 university students across China during the pandemic. Using mixed Bayesian networks and multigroup structural equation modeling, the study explores the complex relationships between personal characteristics, academic characteristics, the academic environment, and students’ motivation for self-directed online learning. The results reveal significant associations between online self-directed learning motivation and personal characteristics. such as gender and age, academic characteristics, such as education level and learning incentives, and the geographic location of the school within the academic environment. Moreover, the causal relationship between school location and online self-directed learning motivation varies by gender and educational level. This research not only provides new empirical support for the theoretical framework of online learning motivation but also contributes to the broader fields of educational psychology and online learning research.

Keywords: online self-directed learning motivation; online search; learning-incentive money; hybrid Bayesian networks; multigroup structural equation modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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