Abstract
The assessment of teaching quality is a very complex and fuzzy nonlinear process, which involves many factors and variables, so the establishment of the mathematical model is complicated, and the traditional evaluation method of 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.
Highlights
At present, English is the undisputed world language, which is mainly due to the development of economic globalization
We have concluded some contributions as follows: (1) the genetic algorithm (GA)-backpropagation neural network (BPNN) model is proposed and applied to study the English teaching quality evaluation model; (2) compared to other baseline methods, the simulation shows that the average evaluation accuracy of the GA-BPNN model is 98.56%, which is 13.23% and 5.85% higher than the BPNN model and the optimized BPNN model, respectively; and (3) results show that the GA-BPNN model in teaching quality evaluation can make reasonable and scientific results
We proposed the GA-BPNN model and presented its theories
Summary
English is the undisputed world language, which is mainly due to the development of economic globalization. Because the descent learning method adopted by neural network is local search, it is simple for neural network to divide into local minimum, and its generalization ability is weak In view of these shortcomings, literature [8] selects the maximum entropy criterion with the function of describing uncertainty distribution information to replace the mean-square-error criterion of BP algorithm and establishes the maximum entropy neural network assessment model of teaching quality. Literature [9] uses PSO neural network technology to establish a teaching quality assessment system in universities. Literature [21] combines the characteristics of military school teaching and the requirements of the Ministry of Education for military school education and establishes a military school teaching quality assessment system in view of BPNN so as to realize scientific, reasonable, and timely assessment of military school teaching quality. Teaching quality assessment involves many factors. e index system of this paper is established by referring to the opinions of frontline
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