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has been cited by the following article:

• TITLE: Building a Neural Network Model to Analyze Teachers’ Satisfaction with Online Teaching during the COVID-19 Ravages

  AUTHORS: Gangxin Wen, Quanlong Guan, Xiaofeng Wu, Weiqi Luo

  KEYWORDS: COVID-19, Online Education, Neural Networks, Satisfaction

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.1, January 27, 2022

  ABSTRACT: The ravages of COVID-19 have forced schools in countries around the world to make a temporary shift from traditional, face-to-face teaching to online teaching. Are teachers in schools prepared to deal with this change? We conducted a survey in which we distributed questionnaires to primary and secondary school teachers in Guangdong Province, China, asking them about their views on various aspects of online education. We received 498,481 questionnaires back, and over 80% of teachers were satisfied with the online resources, and over 68% of teachers were satisfied with the online platform and software. Immediately afterward, we analyzed the differences between urban and rural teachers on specific issues using cross-sectional analysis and chi-square tests and built a neural network model to achieve predictions of teacher satisfaction with an accuracy of nearly 90%. Finally, we analyzed the features that influence the decisions of the neural network. This epidemic has prompted the widespread use of online learning, and the insights we gain today will be helpful in the future.