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Building a Neural Network Model to Analyze Teachers’ Satisfaction with Online Teaching during the COVID-19 Ravages
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作者 Gangxin Wen Quanlong Guan +1 位作者 Xiaofeng Wu Weiqi Luo 《Journal of Computer and Communications》 2022年第1期91-114,共24页
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... 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. 展开更多
关键词 COVID-19 Online Education Neural Networks SATISFACTION
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