Research on the Evaluation Algorithm of English Viewing, Listening, and Speaking Teaching Effect Based on DA-BP Neural Network

Improving the teaching of viewing, listening, and speaking courses is the key to produce better workers for society. Improving teaching quality is therefore a key teaching objective. One way to improve the quality of English instruction is to conduct a scientific evaluation of videos, audios, and spoken language instructions. This paper investigates the neural network model and the evaluation of teaching effect in depth in order to solve the complex nonlinear problem of evaluating the impact of English viewing, listening, and speaking. The DA-BP neural network model is proposed as a result of the existing research’s shortcomings. The BP neural network is basically an algorithm which is multilayered feed-forward network that is trained actually to the error back propagation algorithm. The accuracy of the BP neural network’s evaluation is influenced by its parameter selection in this paper, and the dragonfly algorithm (DA) is used to optimize the BP model’s initial connection weight and threshold parameters. According to the research results, DA-BP improves the accuracy of evaluating the viewing, listening, and speaking classroom teaching quality in college English and offers a new approach for evaluating the audio-visual classroom teaching effect in college English.