Evaluation Index System on Coupling Degree of Economic Growth, Higher Education, Innovation Ability

Author(s):  
Haiying Xu ◽  
Wei-Ling Hsu ◽  
Xiaoyu Qin
2020 ◽  
Vol 12 (6) ◽  
pp. 2515 ◽  
Author(s):  
Haiying Xu ◽  
Wei-Ling Hsu ◽  
Teen-Hang Meen ◽  
Ju Hua Zhu

This study argues that the coupling between higher education, economic growth, and innovation ability is of great significance for regional sustainable development. Through the experience of Jiangsu Province in China, this study establishes a coupling coordination evaluation index system and applies the coupling coordination model to evaluate interactive relationships among the three. It finds that during 2007–2017, the level of coupling of 13 prefecture-level cities in Jiangsu was increasing over time, which fully verified the previous scholars’ view that the three can improve each other over a long period. However, this study finds that there are obvious differences within Jiangsu. Inadequate investment in higher education has become a crucial constraint on sustainable economic growth in northern and central Jiangsu, which are backward regions of Jiangsu. By contrast, in southern Jiangsu, which is the advanced region of Jiangsu, although the resources of higher education are abundant the growth of innovation ability cannot support sustained economic growth well. Thus, the quality of higher education should be improved to meet the needs of the innovation-based economy. Accordingly, cross-regional cooperation and balanced investment in higher education are the keys to practicing a balanced and sustained regional development. The results of this study’s coupling coordination analysis and evaluation can serve as a reference for governments in enhancing regional sustainable development.


2021 ◽  
Vol 2074 (1) ◽  
pp. 012091
Author(s):  
Chunjie Fang

Abstract In order to improve the innovation ability of enterprises and enhance international competitiveness, it is necessary to correctly analyze and evaluate the innovation ability of industrial clusters. Therefore, BP neural network is used to explain the innovation ability of industrial clusters, and the evaluation index system is established. By investigating industrial clusters and using it to provide references for the evaluation of industrial clusters’ innovation capabilities.


2014 ◽  
Vol 14 (3) ◽  
pp. 110-120 ◽  
Author(s):  
Hui Li

Abstract In order to improve the teaching quality of higher education, the paper constructed a teaching quality evaluation index system with five first level indicators and twenty two second level indicators according to the teaching level evaluation index system of ordinary higher education. For the complex nonlinear relationships between the evaluation indices, a mathematical model for evaluating the teaching quality based on WNN, whose parameters were optimized by PSO, was presented in the paper. The experimental results showed that the method proposed could better improve the accuracy of the teaching quality evaluation target by making the mean square error of the actual output value and the desired output value smaller. Simultaneously, the method has been widely used in teaching quality evaluation of our college.


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