Evaluation of Teachers' Educational Technology Ability Based on Fuzzy Clustering Generalized Regression Neural Network.

Jie Zhao,Yushu Zheng,Honghai Guan,Changpeng Lu

doi:10.1155/2021/1867723

Abstract

The improvement of teachers' educational technology ability is one of the main methods to improve the management efficiency of colleges and universities in China, and the scientific evaluation of teachers' ability is of great significance. In view of this, this study proposes an evaluation model of teachers' educational technology ability based on the fuzzy clustering generalized regression neural network. Firstly, the comprehensive evaluation structure system of teachers' educational technology ability is constructed, and then the prediction method of teachers' ability based on fuzzy clustering algorithm is analysed. On this basis, the optimization prediction method of fuzzy clustering generalized regression neural network is proposed. Finally, the application effect of fuzzy clustering generalized regression neural network in the evaluation of teachers' educational technology ability is analysed. The results show that the evaluation system of teachers' educational technology ability proposed in this study is scientific and reasonable; fuzzy clustering generalized regression neural network model can better accurately predict the ability of teachers' educational technology and can quickly realize global optimization. According to the fitness analysis results of the fuzzy clustering generalized regression neural network model, the model converges after the 20th iteration and the fitness value remains about 1.45. Therefore, the fuzzy clustering generalized regression neural network has stronger adaptability and has been optimized to a certain extent. The average evaluation accuracy of fuzzy clustering generalized regression neural network model is 98.44%, and the evaluation results of the model are better than other algorithms. It is hoped that this study can provide some reference value for the evaluation of teachers' educational technology ability in colleges and universities in China.

Highlights

Carrying out the evaluation of teachers’ educational technology ability is one of the necessary ways to strengthen the management efficiency of colleges and universities in China, and it is the main method to deepen the internal management system of colleges and universities
In order to effectively improve the accuracy of the evaluation of teachers’ educational technology ability, this study introduces the combination of general regression neural network (GRNN) and fuzzy c-means algorithm (FCM)
GRNN is a branch of radial basis function neural network (RBF), which is a nonlinear regression feedforward neural network. e algorithm has the advantages of small amount of calculation and fast convergence and is widely used in larger fields [6]

Summary

Introduction

Carrying out the evaluation of teachers’ educational technology ability is one of the necessary ways to strengthen the management efficiency of colleges and universities in China, and it is the main method to deepen the internal management system of colleges and universities. In order to effectively enhance the comprehensive competitiveness of colleges and universities in the field of education, it is very necessary to build a set of evaluation system of teachers’ educational technology ability suitable for their own development [2]. Erefore, colleges and universities are in urgent need of a scientific and fair evaluation method of teachers’ educational ability in order to give correct guidance and encouragement to teachers. There are more and more methods to evaluate the educational technology ability of university teachers, such as analytic hierarchy process, statistics, fuzzy c-means algorithm (FCM), and other methods, which make teachers’. In order to effectively improve the accuracy of the evaluation of teachers’ educational technology ability, this study introduces the combination of general regression neural network (GRNN) and FCM. GRNN is a branch of radial basis function neural network (RBF), which is a nonlinear regression feedforward neural network. e algorithm has the advantages of small amount of calculation and fast convergence and is widely used in larger fields [6]

Methods

Results

Conclusion