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

• Main Authors: Jie Zhao, Honghai Guan, Changpeng Lu, Yushu Zheng
• Format: Article
• Language: English
• Published: Hindawi Limited 2021-01-01
• Series: Computational Intelligence and Neuroscience
• Online Access: http://dx.doi.org/10.1155/2021/1867723

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.