Measuring the Higher Education System
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Kexin Luan, Qing Sun, Zhenxin Song
On the basis of collecting abundant indicators of the health of the higher education system, 14 indicators were selected using principal component analysis (PCA) to evaluate the health of the system of higher education. The entropy weight method (EWM) is used to obtain the weight vector of the system, and the indicators are standardized to calculate the score . At the same time, the standard of system health is obtained through the K-means algorithm. In the end, an evaluation system including higher education background, higher education scale, higher education quality, higher education investment and higher education contribution is established. Considering the differences in the national conditions of various countries, and also in order to test the adaptability of the evaluation model, construct BP neural network for comprehensive evaluation, define the level scale division of different indicators, and finally obtain the ranking of the 8 sample countries through training samples.
Entropy weight method, Evaluation system, BP neural network