A research on English teaching quality in ethnic colleges and the application of BP neural network

2015 ◽  
pp. 135-142
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Teaching Quality ◽  
English Teaching

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.

scholarly journals Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Luxin Jiang ◽  
Xiaohui Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Particle Swarm ◽  
Teaching Quality ◽  
Particle Swarm Algorithm ◽  
Bp Model ◽  
Quality Evaluation Model

In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process (AHP) and particle swarm optimization BP neural network (PSO-BP) is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network.

The Model of Evaluating Teaching Quality Based on BP Neural Network Algorithm

2015 ◽  
Vol 719-720 ◽  
pp. 1297-1301
Author(s):  
Lei Bai ◽  
Xiao Xin Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Hot Spot ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Research Field ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Neural Network Algorithm

Teaching quality evaluation plays a key role for universities to improve its teaching quality and becomes a hot spot research field for related researchers. In this paper, we established the evaluation model of teaching quality based on BP neural network. Firstly an evaluation index system of teaching quality is designed. Then, according to the system we design the structure of BP neural network, determine the parameters and give the algorithm description. Finally, we program and verify the validity of the model in MATLAB environment. The experimental results show that the model can evaluate teaching quality practically by the evaluation index.

The Application of Modified BP Neural Network Method to the Evaluation of the Quality of Teaching

2013 ◽  
Vol 710 ◽  
pp. 628-632
Author(s):  
Rong Song ◽  
Tao Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Calculation Error ◽  
Neural Network Method ◽  
Network Method ◽  
Calculation Results ◽  
Function Relationship ◽  
The Stability

Modified BP neural network method was used to solve the problem of teaching quality evaluation. The neural network was built to fit the function relationship between the second-floor indicator and teaching quality evaluation. So quality teaching evaluation could be implemented. At first, the theory of BP neural network method was introduced, then, students` evaluation of the secondary indicators was taken as inputs, and scores from the Steering Group as output, and 20 lessons scores as researched data, and then, calculating characters of BP method were analyzed. The calculating result showed that the calculation results of the method have the stability, its feasibility was proved. After that, the optimized calculating method was used to optimize result. The calculation results showed that the method had high accuracy, and predictive value calculation error was less than 2.02%, and it verified the feasibility of the method.

Research on the Application of the BP Neural Network

2014 ◽  
Vol 687-691 ◽  
pp. 2813-2816
Author(s):  
Cao Yu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Input Data ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Back Propagation ◽  
Evaluation Process ◽  
Teaching Quality ◽  
Practical Teaching

The paper constructs an evaluation model for practical teaching quality based on Back Propagation (BP) neural network. It makes the indicators of evaluating practical teaching quality as input data, while practical teaching quality as output results. The empirical conclusion obtained from the use of Excel is that BP neural network is suitable for practical teaching quality evaluation and also makes a better analogy to the experts’ evaluation process. The results are satisfactory with wide application.

scholarly journals Evaluation model of art internal auxiliary teaching quality based on artificial intelligence under the influence of COVID-19

2020 ◽  
Vol 39 (6) ◽  
pp. 8713-8721
Author(s):  
Luo Yuan ◽  
Zhao Xiaofei ◽  
Qiu Yiyu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Empirical Method ◽  
Bp Network ◽  
Art Teaching ◽  
The Impact

At present, the evaluation of normal teaching order and teaching quality has been seriously interfered by the impact of COVID-19. In order to ensure the quality of art classroom teaching, this article uses BP neural network technology to build a model for art teaching quality evaluation during the epidemic. Based on the introduction of the BP neural network model and the problems of art teaching quality evaluation, the article focuses on the art teaching quality evaluation indicators and the BP neural network algorithm and process. In addition, the article also uses an empirical method to verify the effect of the BP network model training method, and obtains the expected effect. Finally, it discusses the problem of information processing in art teaching evaluation.

scholarly journals Evaluation of teaching quality of computing method course based on improved BP neural network

2021 ◽  
Vol 1774 (1) ◽  
pp. 012026
Author(s):  
Xiaotong Deng ◽  
Yi Gu ◽  
Fei Li ◽  
Xiaohui Liu ◽  
Guoqiang Zeng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Teaching Quality ◽  
Evaluation Of Teaching ◽  
Computing Method
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