Students' online learning adaptability and their continuous usage intention across different disciplines

Online learning, as a pivotal element in modern education, is introducing fresh demands and challenges to the established teaching norms across various subjects. The adaptability of students to online learning and their sustained willingness to engage with it constitute two pivotal factors influencing the effective operation of online education systems. The dynamic relationship between these aspects may manifest unique traits within different academic disciplines, yet comprehensive research in this area remains notably scarce. In light of this, this study constructs an Adaptive Structural Learning and Technology Acceptance Model (ASL-TAM) with satisfaction towards online teaching as the mediating variable to investigate the impact and mechanism of online learning adaptivity on continuous usage intention for students from different disciplines. A total of 11,832 undergraduate students from 334 universities in 12 disciplinary categories in mainland China were selected, and structural equation modeling was used for analysis. The results showed that the ASL-TAM model could be fitted for all 12 disciplines. The perceived ease of use, perceived usefulness, and system environment adaptability dimensions of online learning adaptivity significantly and positively affect satisfaction towards online teaching and continuous usage intention. Satisfaction towards online teaching partially mediates the relationship between online learning adaptivity and continuous usage intention. There were significant differences in the results of the single-factor analysis of the observed variables for the 12 disciplines, and the path coefficients in the ASL-TAM model fitted for each discipline were also significantly different. Compared to the six disciplines under the science, technology, engineering, and mathematics (STEM) category, six disciplines under the humanities category exhibited more significant internal differences in the results of the single-factor analysis of perceived usefulness and the path coefficients for satisfaction towards online teaching. This research seeks to bridge existing research gaps and provide novel guidance and recommendations for the personalized design and distinctive implementation of online learning platforms and courses across various academic disciplines.

Zheng Li, Xiaodong Lou, Minwei Chen, Siyu Li, Cixian Lv, Shuting Song, Linlin Li