Since 2016, more enterprises have chosen the blended learning mode for staff training, and an increasing number of schools regard it as one of the important ways of carrying out teaching activities. Qi and Chen (2022) state that through the analysis of students’ data, the correlation and matching between their learning needs and status have been improved, and the effectiveness of online teaching has been enhanced. Through data analysis, students’ learning status can be understood, after which teaching objectives can be made more specific, accurate and personalized. ![]() Wang (2017) believes that big data technology digitizes every student’s learning behavior. Lin and Xie (2019) argue that the zero-lag and high-speed characteristics of 5G communication technology have changed the situation regarding learning in fixed locations, and mobile learning has also become popular. The motivation of this research is with the fast advancement of computer information technology represented by 5G and big data, the application of new technology has enriched teachers’ online teaching methods and the effectiveness of students’ learning, and the essential requirements for the rapid development of blended learning are also in place. The epidemic situation promoted the comprehensive popularization of e-learning, as well as people’s thinking about blended learning. E-learning became the only choice of teaching methods at that time. To inhibit the spread of the epidemic, the Chinese government resolutely implemented stringent prevention and control measures, and schools stopped normal classroom teaching activities. This virus is highly infectious and difficult to control. Based on these findings, this article provides theoretical and practical suggestions for the implementation of blended learning to improve its effect.ĬOVID-19 emerged suddenly in December 2019. The results show that the technology acceptance and participation of students determine the effect of blended learning. (3) There are considerable disparities in the skill involvement and participation of computer science major college students. There is no significant difference between them in terms of perceived usefulness, perceived ease of use, or computer self-efficacy. (2) There are major variances regarding the perception of service quality between male and female computer science major students. The results show that: (1) students majoring in computer science view the factors as having a high level of influence in blended learning. The t-test method was employed to analyze the gender differences between students in regard to the topic. ![]() The mean and variance were used to examine the status of students’ technology acceptance and satisfaction with blended learning. A questionnaire was designed and distributed, and 796 valid responses were collected. By combining technology acceptance and student participation, this article proposes an analysis model for assessing the factors influencing blended learning. The process is still in its infancy in Chinese colleges and universities, and its development remains a problem to be solved. In the wake of the COVID-19 pandemic in 2019, China’s education leaders began to focus on and promote blended learning. 2School of Artificial Intelligence, Dongguan Polytechnic, Dongguan, China.1Information Management and Information System, Guangdong University of Science and Technology, Dongguan, China.Chao Deng 1, Jiao Peng 1* and ShuFei Li 2
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