Optimizing the Systematic Characteristics of Online Learning Systems to Enhance the Continuance Intention of Chinese College Students

Abstract

Different from systems that directly provide online shared courses such as MOOC, online learning systems such as Tencent Classroom simulate a real classroom environment for students and teachers to realize online face-to-face teaching, utilized during the COVID-19 pandemic. Nevertheless, due to the limitation of physical distance, the intelligent design of online learning systems is necessary to provide students with a good learning experience. This study notes that an unexpected optimization effect is the impact of system characteristics on the flow experience of online learning systems, which has not been studied, but plays a vital role in the effectiveness of online learning systems. In the study, a questionnaire was created and multi-stage sampling was used to investigate 623 college students. Based on the DeLone and McLean model of IS success and flow theory, a model for optimizing system characteristics and flow experience was constructed and its effectiveness was tested. The results reveal that system characteristics have a positive impact on continuance intention and flow experience. Additionally, flow experience and learning effect have a positive impact on continuance intention. Furthermore, flow experience has a positive impact on the learning effect. This study emphasizes the flow experience of online learning systems and reveals the optimization direction of online virtual face-to-face classrooms to provide references for the Ministry of Education, schools, and enterprises providing education systems.