Analysis of User Satisfaction with Online Education Platforms in China during the COVID-19 Pandemic

• Published in: Healthcare (Basel) Vol. 8; no. 3; p. 200
• Main Authors: Chen, Tinggui, Peng, Lijuan, Yin, Xiaohua, Rong, Jingtao, Yang, Jianjun, Cong, Guodong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07-07-2020; MDPI
• Subjects: COVID-19; Distance learning; emotion mining; Online education; online education platform; Psychological aspects; public health emergencies; Satisfaction (Psychology); Social aspects; Studies; Teaching; user satisfaction prediction; China; online education platform; user satisfaction prediction; public health emergencies; emotion mining
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare8030200

The outbreak of Corona Virus Disease 2019 (COVID-19) in various countries at the end of last year has transferred traditional face-to-face teaching to online education platforms, which directly affects the quality of education. Taking user satisfaction on online education platforms in China as the research object, this paper uses a questionnaire survey and web crawler to collect experience data of online and offline users, constructs a customer satisfaction index system by analyzing emotion and the existing literature for quantitative analysis, and builds aback propagation (BP) neural network model to forecast user satisfaction. The conclusion shows that users' personal factors have no direct influence on user satisfaction, while platform availability has the greatest influence on user satisfaction. Finally, suggestions on improving the online education platform are given to escalate the level of online education during the COVID-19 pandemic, so as to promote the reform of information-based education.