Zhang Jing. A Comprehensive Evaluation Method of College Teachers’ Evaluation Based on K-means Algorithm. Dynamic Systems and Applications 29 (2020) No. 4, 1458 – 1471

https://doi.org/10.46719/dsa202029433

ABSTRACT.
Clustering is the process of dividing the geometry of physical or abstract objects into similar object classes or clusters. It makes the objects in the same cluster have high similarity and the objects in different clusters are highly different. Clustering analysis has been widely used in many fields. Clustering analysis is based on the principle that objects in the same cluster have as much similarity as possible, while objects in different clusters have as much dissimilarity as possible. K-means algorithm is a clustering method based on similarity between samples. The K-means algorithm is applied to teacher evaluation research, clustering the comprehensive features and single features respectively, not only giving the clustering results, but also analyzing the comprehensive clustering and single clustering results to find out the contribution degree of each feature to the comprehensive clustering results. The clustering analysis of teacher evaluation is realized, which provides the basis for improving the comprehensive quality of teachers, management evaluation strategy and subsequent evaluation and employment separation work.

Key words: K-means algorithm; Evaluation of university teachers; Cluster analysis