Performance evaluation of industry-education integration in higher education from the perspective of coupling coordination-an empirical study based on Chongqing

rural schools, have been greatly improved. The quality of the teaching staff has been further improved, and the overall promotion of education informatization has been achieved. Impor- tant progress has been made in the reform of the education system, and the reform of the talent cultivation system, school management system, evaluation system, and security system has been comprehensively deepened. Breakthroughs have been made in some key areas and links, which is the driving force for the long-term healthy development of China’s education. This paper focuses on the industry-education integration in higher education institutions. Using the school size and student size of higher education institutions to reflect the scale of higher education is somewhat representative. The per capita educational funds and educational infra- structure reflect the basic guarantee for the development of higher education. The growth of teaching staff, educational funds, and school scale reflects the development situation of higher education. The National Medium- and Long-Term Education Reform and Development Plan (2010–2020) has completed its historical mission, and China’s higher education scale and structure have achieved various set goals [46]. In terms of performance, educational performance reflects the efficiency of educational development, using both input and output to reflect higher education performance [47, 48]. Educational input refers to the various resources invested by the entire society in education,

including human, financial, and material resources, which are the basic guarantee for educa- tional development. From the perspective of the entire society’s education, good educational output means more opportunities for education and higher levels. The most representative indicators to reflect educational quality are the scale of higher education students per 100,000 population and the gross enrollment rate of higher education. The scale of higher education students per 100,000 population is one of the important determinants of the number of high- level professionals in a country’s future population, and it is also an important indicator of the average level of education among the employed population. Therefore, the number of resi- dents with university degrees determines to some extent the potential and possibilities for a country’s future social development. The gross enrollment rate of higher education reflects the overall level of higher education opportunities provided by a country, which is closely related to factors such as a country’s level of economic development and the degree of development of basic education. It is a widely cited indicator in China’s higher education academic commu- nity. Therefore, using human resource input, fixed asset input, and various energy inputs to reflect the level of higher education input, the number of higher education students per 100,000 population and the gross enrollment rate of higher education together reflect the development achievements of higher education in a period of time, forming an education sub-

system indicator system as shown in Table 2. 2.3 Entropy weight model The entropy weight method is an objective weighting method that decides the index weight through the information amount provided by the entropy value of each index. The use of the entropy weight method to weight each index can avoid the interference of human factors in the weight of each evaluation index and make the evaluation result more in line with reality. Generally, entropy refers to disorder or uncertainty. Shannon entropy was introduced by Claude E. Shannon in his 1948 paper, “A Mathematical Theory of Communication”. In infor- mation theory, entropy is a measure of the disorder degree of a system. The higher the order degree of a system is, the smaller the entropy, and the greater the amount of information contained in the system [49, 50]. According to the theory, Shannon entropy is defined as H = −s S pi ln pi. PLOS ONE Performance evaluation of industry-education integration in higher education based on coupling coordination PLOS ONE | https://doi.org/10.1371/journal.pone.0308572 September 18, 2024 10 / 22