Learning Flow and Continuous Intention Toward Online Remote Learning: An Integrated Framework

Abstract

During the COVID-19 pandemic, higher education institutions adopted several technology-driven platforms (such as Google Meet) as an alternative to traditional in-person classrooms. Researchers have been concerned about the evaluation of learning satisfaction and continuous learning intentions. As such, the purpose of this research was to investigate how the implementation of Google Meet affected the learning flow experience of Taiwan students amid the pandemic. The hedonic (perceived enjoyment, perceived vividness, perceived interactivity) and utilitarian (system quality, service quality, information quality) factors were integrated into the perspective of learning flow. Furthermore, this study also examined how flow experiences influence satisfaction and continuous learning intention. The structural equation modeling was employed to analyze the data gathered from a sample of 213 students. According to the findings, hedonic and utilitarian factors all significantly influenced flow experiences. As learning flow increased in remote learning, satisfaction and continuous learning intention were also increased. The results can provide significant implications for flow and learning theory, and offer concrete suggestions for teaching practices and education learning platforms.