scholarly journals Application of Optimized GA-BPNN Algorithm in English Teaching Quality Evaluation System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yaowu Zhu ◽  
Junnong Xu ◽  
Sihong Zhang
Keyword(s):  
Index System ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Teaching Quality ◽  
Model Parameters ◽  
English Teaching ◽  
Comparison Results ◽  
Quality Evaluation System ◽  
Bpnn Model

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.

scholarly journals Based on Optimization Research on the Evaluation System of English Teaching Quality Based on GA-BPNN Algorithm

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Yafei Chen ◽  
Zhenbang Yu ◽  
Weihong Zhao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
English Teaching ◽  
Algorithm Evaluation ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Quality Evaluation System

English teaching is an important part of basic teaching in our country, which has been deeply concerned by all aspects. Its teaching quality not only is related to the purpose of English teaching, but also has a far-reaching impact on students’ English learning. Therefore, the construction of English teaching quality evaluation system has become the focus of research. However, the traditional English teaching quality evaluation method has some problems; for example, the subjectivity of teaching evaluation is strong, the evaluation index is not comprehensive, and the evaluation results are distorted. Therefore, this paper studies the English teaching quality evaluation system based on optimized GA-BP neural network algorithm. On the basis of BP neural network algorithm evaluation simulation, GA algorithm is introduced for optimizing, and GA-BP neural network algorithm model is further optimized by GA adaptive degree variation and entropy method. The experimental results show that the optimized GA-BP neural network algorithm has faster convergence speed and smaller error. At the same time, the optimized GA-BP neural network algorithm evaluation model has better adaptability and stability, and its expected results are more in line with the ideal value. The results of English teaching quality evaluation are more scientific, showing higher value in the application of English teaching quality evaluation.

Research on Classroom Teaching Quality Evaluation of Chinese as a Foreign Language Based on AHP Model

2013 ◽  
Vol 411-414 ◽  
pp. 2957-2960 ◽  
Author(s):  
Jing Liu
Keyword(s):  
Evaluation System ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Teaching Quality ◽  
Classroom Teaching ◽  
Ahp Model ◽  
Judgment Matrix ◽  
Quality Evaluation System ◽  
Chinese Teaching

How to evaluate the teaching quality of Chinese teacher objectively is an important subject of each university. In view of the shortage of classroom teaching quality evaluation, AHP model is introduced to evaluate the quality of TCFL, and an Chinese teaching quality evaluation system is established. Based on the evaluation content and standard of the system, combined with the the principle of AHP and expert investigation method, a judgment matrix is established, and the weight of each index to the total target is calculated. The comprehensive weight of each index and evaluation object score are multiplied, through a series of calculation, the teachers comprehensive scores can be obtained so as to evaluate the teaching quality. The results show that it is very scientific and objective to evaluate Chinese classroom teaching quality by using AHP model, it is a feasible evaluation method and has higher application value.

scholarly journals College English Teaching Quality Evaluation System Based on Information Fusion and Optimized RBF Neural Network Decision Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yajun Chen
Keyword(s):  
Neural Network ◽  
Information Fusion ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Rbf Neural Network ◽  
Teaching Quality ◽  
Decision Algorithm ◽  
College English ◽  
English Teaching ◽  
Quality Evaluation System

In the process of deepening and developing the current higher education reform, people pay more and more attention to the research of college English education. The key to improve the college English education is to improve the quality of education, and learning evaluation is the key measure to improve the quality of education and training. This paper mainly studies the college English teaching quality evaluation system based on information fusion and optimized RBF neural network decision algorithm. This paper analyzes the main problems and complexity of creating an ideal learning quality evaluation system. On the basis of analyzing the advantages and disadvantages of the previous learning quality evaluation methods, this paper summarizes the existing learning quality evaluation methods and puts forward some suggestions according to the existing evaluation methods. A learning quality evaluation model based on RBF algorithm of neural network is proposed. RBF regularization network method, RBF neural network decision algorithm, and experimental investigation method are used to study the college English teaching quality evaluation system based on information fusion and optimization of RBF neural network decision algorithm. By innovating teaching methods and enriching teaching means, college students’ thirst for English knowledge can be aroused, and teachers’ teaching level can be improved. The results show that 50% of college students think that the level of college English teaching is average and needs to be improved. In the performance evaluation system of college English teaching quality based on information fusion and optimized RBF neural network decision algorithm, it is necessary to establish a learning evaluation system, monitor the learning quality in real time, find problems and improve them in time, and recognize the current situation of education.

scholarly journals Exploration on the Teaching Reform Measure for Machine Learning Course System of Artificial Intelligence Specialty

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yizhang Jiang ◽  
Bo Li
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Teaching Quality ◽  
Teaching Reform ◽  
Intelligent Technology ◽  
Quality Evaluation System

Due to the particularity of the artificial intelligence major and the machine learning courses learned, the traditional course teaching model is not suitable for artificial intelligence major machine learning courses. Based on this background, this article proposes a new system based on machine learning curriculum teaching reform. It mainly includes the reform of curriculum teaching mode, curriculum practice reform, and teaching process reform. In order to verify the effect of the proposed new model on the teaching quality of machine learning courses, this article also proposes an evaluation method based on intelligent technology. Firstly, the feasibility of evaluation based on intelligent technology is described. Secondly, it lists the application details of the existing teaching evaluation based on intelligent technology. Finally, a novel teaching quality evaluation system based on intelligent technology is proposed. The system collects student facial expression data and uses classification algorithms to make classification decisions on the data. The result of the decision can give feedback on the quality of classroom teaching. The comparison of experiments based on different intelligent technologies shows that the teaching quality evaluation system proposed in this article is feasible and effective.

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