Affiliations: School of International Education, Yellow River Conservancy Technical Institute, Kaifeng, Henan, China | E-mail: wuqianqian521830@hotmail.com | ORCID: 0009-0008-0158-1149
Correspondence:
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Corresponding author: School of International Education, Yellow River Conservancy Technical Institute, Kaifeng, Henan, China. E-mail: wuqianqian521830@hotmail.com. ORCID: 0009-0008-0158-1149.
Abstract: Teaching evaluation is a key initiative to improve the quality of education and teaching. The research significance of this study is rooted in addressing the limitations of the traditional evaluation of teaching quality (ETQ) model, which often relies on a single evaluation index, exhibits a one-sided perspective, and suffers from pronounced subjectivity. In this context, this paper delves into the application of the backpropagation neural network (BPNN) to enhance and refine the ETQ model. The intelligent ETQ model was constructed and utilized in network English teaching to enhance the effect and quality of network English teaching. By analyzing the characteristics and needs of network English teaching, the advantages of BPNN in the ETQ were explored. The intelligent evaluation model was constructed, and its application effect in network English teaching was studied and evaluated. The total number of students satisfied with the BPNN based network English ETQ model was 151, with a total satisfaction rate of 75.5%. The ETQ model on the basis of BPNN was applied to network English teaching, which helped the average final score of Class 2 improve by 5.44 points compared to the division exam. The ETQ model based on BPNN was applied to network English teaching, which can improve the rationality of teaching evaluation and help improve students’ school English proficiency.
Keywords: Network english teaching, evaluation of teaching quality, back propagation neural network, evaluating indicator, analytic hierarchy process, radial basis function
