Study on Application of Optimized GA-BPNN Algorithm in English Teaching Quality Evaluation System

Abstract

The assessment of teaching quality is a very complex and fuzzy nonlinear process involving many factors and variables, so developing a mathematical model is difficult, and the traditional method of evaluating teaching quality is no longer fully competent. In order to evaluate teaching quality effectively and accurately, an optimized GA-BPNN algorithm based on genetic algorithm (GA) and backpropagation neural network (BPNN) is proposed. Firstly, an index system of teaching quality evaluation is established, and a questionnaire is designed according to the index system to collect data. Then, an English teaching quality evaluation system is established by optimizing model parameters. The simulation shows that the average evaluation accuracy of the GA-BPNN algorithm is 98.56%, which is 13.23% and 5.85% higher than those of the BPNN model and the optimized BPNN model, respectively. The comparison results show that the GA-BPNN algorithm in teaching quality evaluation can make reasonable and scientific results. The GA-BPNN technique developed in this study searches locally near the global optimal solution, which effectively overcomes the classic approach's sluggish convergence speed while also overcoming the problem of being easily local confined to the minimum.