English Teaching Quality Evaluation Based on Fuzzy Comprehensive Evaluation of Neural Network Algorithm

2020 ◽  
Vol 29 (5) ◽  
Author(s):  
Jimin Gu ◽  
Ruolin Shi
Keyword(s):  
Neural Network ◽  
Quality Evaluation ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Teaching Quality ◽  
English Teaching ◽  
Network Algorithm ◽  
Neural Network Algorithm

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 A Study on the Quality Evaluation of English Teaching Based on the Fuzzy Comprehensive Evaluation of Bat Algorithm and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shu Ji ◽  
Sang-Bing Tsai
Keyword(s):  
Evaluation System ◽  
Quality Evaluation ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Qualitative Evaluation ◽  
Bat Algorithm ◽  
Fuzzy Comprehensive Evaluation ◽  
Teaching Quality ◽  
English Teaching ◽  
Evaluation Algorithm

In this paper, the fuzzy comprehensive evaluation model based on the bat algorithm quantifies the qualitative evaluation effectively and provides a feasible and convenient English teaching quality evaluation system by combining quantitative evaluation with objective index data. Firstly, the English teaching quality evaluation model is constructed based on the fuzzy comprehensive evaluation analysis method and the weight values of each factor are calculated; secondly, the three types of data in the model are processed separately. This includes standardizing the data of objective indicators such as students’ course grades and weakening the influence of course difficulty on this indicator. The fuzzy comprehensive evaluation model based on the bat algorithm quantifies the qualitative evaluation to make the calculated comprehensive evaluation of English teaching quality more comprehensive and objective; then the comprehensive calculation of English teaching quality evaluation is completed, and the English teaching quality evaluation model is constructed by extracting keywords based on the qualitative evaluation; finally, a runnable English teaching quality evaluation system is designed and implemented. A fuzzy comprehensive evaluation algorithm based on improved bat algorithm optimization is proposed. The algorithm uses the improved fuzzy comprehensive evaluation algorithm to optimize the initial clustering centers and adopts a new objective function to guide the clustering process, thus improving the clustering quality of the fuzzy comprehensive evaluation algorithm. Comparative analysis through models shows that the improved algorithm improves the clustering accuracy to a certain extent when compared with the traditional fuzzy comprehensive evaluation clustering algorithm for analysis. The bat algorithm is one of the stochastic global optimization models. It can take advantage of the group, integrate global search and local search, and achieve rapid convergence. Therefore, it plays an important role in optimizing the evaluation of English teaching quality. This study enriches the theoretical study of English teaching quality evaluation to a certain extent and can play a role in strengthening and improving English teaching quality evaluation at the present stage.

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.

scholarly journals Japanese Translation Quality Evaluation System Based on Deep Neural Network Algorithm

2022 ◽  
Vol 16 ◽  
pp. 433-439
Author(s):  
Rui Zhang
Keyword(s):  
Neural Network ◽  
Evaluation System ◽  
Deep Neural Network ◽  
Quality Evaluation ◽  
Processing Efficiency ◽  
Translation Quality ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Quality Evaluation System ◽  
Japanese Translation

The current translation quality evaluation system relies on the combination of manual and text comparison for evaluation, which has the defects of low efficiency and large evaluation errors. In order to optimize the defects of the current quality evaluation system, a Japanese translation quality evaluation system based on deep neural network algorithm will be designed. In order to improve the processing efficiency of the system, the USB3.0 communication module of the hardware system will be optimized. Based on the hardware design, the reference translation map is used to extend the reference translation of Japanese translation. The evaluation indexes of over- and under-translation are set, and the evaluation of Japanese translation quality is realized after the parameters are determined by training the deep neural network using the sample set. The system functional test results show that the average data transmission processing time of the system is improved by about 31.27%, and the evaluation error interval is smaller and the evaluation is more reliable.

scholarly journals Construction of Teaching Quality Evaluation System of Higher Vocational Project-Based Curriculum Based on CIPP Model

2021 ◽  
Vol 11 (6) ◽  
pp. 262-268
Author(s):  
Hongcheng Jiang ◽  
◽  
Yiting Liu
Keyword(s):  
Evaluation System ◽  
Quality Evaluation ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Teaching Quality ◽  
Development Stage ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Cipp Model

The project-based classroom teaching has entered the connotative development stage in the development process of Higher Vocational Education in China. It is necessary to evaluate the comprehensive teaching quality including the implementation background, implementation conditions, implementation process and implementation results, but there are few studies on this aspect at present. Therefore, this paper introduces the CIPP evaluation model, based on the analysis of the necessity and applicability of CIPP model in Higher Vocational project-based curriculum teaching quality evaluation, constructs the evaluation index system of Higher Vocational project-based curriculum teaching quality based on CIPP, and discusses the multi-level fuzzy comprehensive evaluation model on the basis of weighting the evaluation index by using AHP. Finally, it takes the teaching quality of cost accounting and practice project in Higher Vocational Colleges as an example to discuss the application of the model.

scholarly journals Optimization of Online Education and Teaching Evaluation System Based on GA-BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin Xu ◽  
Fenghu Liu
Keyword(s):  
Neural Network ◽  
Online Education ◽  
Bp Neural Network ◽  
Optimization Model ◽  
Evaluation System ◽  
Teaching Quality ◽  
Teaching Evaluation ◽  
Classroom Teaching ◽  
Network Algorithm ◽  
Neural Network Algorithm

With the popularization and application of online education in the world, how to evaluate and analyze the classroom teaching effect through scientific methods has become one of the important teaching tasks in colleges. Based on this, this paper studies the application of the GA-BP neural network algorithm. Firstly, it gives a brief overview of the current situation of online education and GA-BP neural network algorithm. Secondly, through the investigation of the online education system in many aspects, it evaluates students’ online education classroom teaching quality from five aspects, and this paper proposes a more scientific online education classroom teaching quality evaluation optimization model and finally verifies the reliability of the online education teaching evaluation model through the practice in a university. The results show that the GA-BP neural network-based evaluation optimization model can effectively evaluate the online education in the process of analyzing the quality of online education classroom teaching of most professional students.

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