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.

College Teaching Quality Evaluation Model Based on BP Neural Network

2012 ◽  
Vol 591-593 ◽  
pp. 2186-2189 ◽  
Author(s):  
Xiao Hong Zhu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Teaching Effectiveness ◽  
Evaluation Model ◽  
College Teaching ◽  
Nonlinear Process ◽  
Teaching Quality ◽  
Wide Range ◽  
Quality Evaluation Model

The college teaching quality evaluation is a multi-factor, multi-variable fuzzy nonlinear process. This paper applied BP neural network to setup a teaching quality evaluation model according to the expert group standards, and trained the neural network model through MATLAB7.0 for learning complex knowledge and simulating capabilities. The data test validation shows that the evaluation result agreed to the actual teaching effectiveness, and has a wide range of applications in various types of teaching management.

Intelligent online teaching system based on SVM algorithm and complex network

2020 ◽  
pp. 1-11
Author(s):  
Chuanxin Fang
Keyword(s):  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Research Work ◽  
Principal Component ◽  
Teaching Quality ◽  
Support Vector ◽  
School Teaching ◽  
The Impact ◽  
Quality Evaluation Model

English Online teaching quality evaluation refers to the process of using effective technical means to comprehensively collect, sort and analyze the teaching status and make value judgments to improve teaching activities and improve teaching quality. The research work of this paper is mainly around the design of teaching quality evaluation model based on machine learning theory and has done in-depth research on the preprocessing of evaluation indicators and the construction of support vector machine teaching quality evaluation model. Moreover, this study uses improved principal component analysis to reduce the dimensionality of the evaluation index, thus avoiding the impact of the overly complicated network model on the prediction effect. In addition, in order to verify that the model proposed in this study has more advantages in evaluating teaching quality than other shallow models, the parameters of the model are tuned, and a control experiment is designed to verify the performance of the model. The research results show that this research model has a certain effect on the evaluation of school teaching quality, and it can be applied to practice.

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.

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.

Company employee quality evaluation model based on BP neural network

2020 ◽  
pp. 1-10
Author(s):  
Tsui-Yuan Tseng ◽  
Qinglan Luo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Relative Error ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Performance Data ◽  
Average Relative Error ◽  
Model Training ◽  
Quality Evaluation Model

With the development of science and technology and the continuous improvement of people’s living standards, the traditional staff quality evaluation can no longer meet the needs of production and life, and the BP neural network has also appeared many shortcomings in practical applications. This article mainly studies the company’s employee quality evaluation model based on BP neural network. This article first collects and preprocesses employees’ usual performance data, and then predicts their corresponding quality scores based on BP neural network. And use MATLAB software to simulate the constructed prediction model, and finally develop a complete set of employee performance data prediction system based on this model, so as to achieve the purpose of employee quality evaluation. The experimental data in this paper shows that the average relative error of model training output tends to be stable. After the 40th iteration of training, the average relative error of model training can reach 0.0128. After the prediction model training was completed, 15 sets of verification samples were used to verify the model. The verification results found that the average relative error of the model converged, so the model did not overfit. Experimental results show that although BP neural network has two excellent functions of adaptive and nonlinear approximation, it can solve the complex nonlinear relationship between normal performance and overall performance. But BP neural network still has its own inevitable shortcomings in some aspects. For example the redundancy between the employee scoring sample data; the problem that the input variable dimensionality is too high, which leads to the low efficiency of the model; the fuzzy neural network is easy to fall into the local optimum and it is difficult to find the global optimum.

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