A study on the influencing factors of university students’ online persistent learning supported by intelligent technology in the post-pandemic era: an empirical study with PLS-SEM

ABSTRACT

Online learning became more commonplace all over the world in the post-pandemic era; however, the research on how to promote Online Persistent Learning (OPL) was still in its infancy. Therefore, this study aimed to analyse the influencing factors of Online Persistent Learning Supported by Intelligent Technology (OPLSIT) based on the dimensions of user's stickiness, dispositional trust and learning satisfaction. The Partial Least Squares Structural Equation Model (PLS-SEM) method was used to analyse data collected from 385 students who experienced online learning supported by intelligent technology (OLSIT). The results showed that learning satisfaction has a significant positive impact on OPL. In addition, user stickiness and dispositional trust were also two important predictors of OPL. Learning intention, social presence and cognitive presence were positively correlated with learning satisfaction, which indirectly and positively influence OPL. Technology anxiety had a negatively impact on learning satisfaction, which indirectly and negatively affected the OPL. Therefore, suggestions that enhance OPLSIT were put forward from the perspectives of teaching presence, cognitive presence, social presence, emotional presence, learning intention and dispositional trust and user stickiness for the design and development of intelligent online learning tools.

KEYWORDS:

• Online persistent learning
• learning satisfaction
• community of inquiry
• technology anxiety
• user stickiness
• dispositional trust

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by 2022 China National Social Science Key Project: Research on the Ethics and Limits of AI in Education Scenarios (Grant No: ACA220027) and Hangzhou Normal University Postgraduate Research Innovation Promotion Project: A Study on Mechanism and Strategy of University Students’ Self-Regulated Online Learning in the Post-Pandemic Era (Grant No: 2022HSDYJSKY027).

Notes on contributors

Gaojun Shi

Gaojun Shi, is currently a graduate at Hangzhou Normal University, Hangzhou, China. His research interests include online learning, smart learning, smart education, and artificial intelligence in education (AIED).

Jiaping Li

Jiaping Li, is currently a graduate at Hangzhou Normal University, Hangzhou, China. Her research interests include synchronous online learning, smart classroom, and game-based learning.

Junfeng Yang

Junfeng Yang is a professor in Hangzhou Normal University, and also a visiting professor in Beijing Normal University. He has published more than 80 academic papers, including SSCI journal papers and outstanding Chinese journal papers. He also serves as the associate editor of the Journal of Smart Learning Environment published by Springer.