Teacher Allocation and Evaluation Based on Fuzzy C-Means Clustering

Abstract

As an important resource in the education industry, teachers play a key role in promoting the high quality of education. In order to explore the overall level of the teaching staff in various regions of China and the allocation level of teachers’ educational resources, this paper selects the data or proportion of the student-teacher ratio, teachers’ educational background, and teachers’ professional titles as indicators. The clustering algorithm based on Fuzzy C-means conducts a cluster analysis on the allocation of compulsory education teachers in 31 provinces in China. The allocation of teachers in 31 provinces is divided into four categories, and the analysis and evaluation are carried out. At the same time, according to the current situation of teacher allocation in compulsory education, relevant countermeasures and suggestions are put forward to improve the overall balanced development of teacher allocation in China and narrow the gap of teacher allocation in China, in order to promote the balanced development of teacher allocation in compulsory education.

Integration of Fuzzy C-Means and SAW Methods on Education Fee Assistance Recipients

“…It must be repeated from each membership value until the cluster center moves closer to the correct membership value [13]. Fuzzy C-Means is an unsupervised clustering method in which each cluster data is determined by its membership degree [14]. This method is very good at pattern recognition when the number of clusters is known [15].…”