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

Secondly, the indicator system can be divided into three types: positive indicators, negative indicators, and moderate indicators. Among them, negative indicators and moderate indica- tors need to be processed positively. Negative indicators include the "student-faculty ratio in higher education" in the education subsystem, which is processed positively by using the recip- rocal method. There are three moderate indicators, namely the proportion of primary, second- ary, and tertiary industries in the industrial subsystem. These three indicators are not necessarily the larger the better. They have a moderate development trend and goal, all of which belong to moderate indicators. The industrial structure of developed countries has already become the perfect "three-two-one" structural model. Internationally, taking the United States as a reference, the proportion of primary industry output value is very small, generally remaining at around 1%. However, agricultural production has always been in a state of low input and high efficiency. The proportion of the secondary industry remains at around 20%, and the proportion of industry in the GDP of the United States is 19%. The proportion of the tertiary industry is the highest, fluctuating around 80%, and it dominates in the United States economy [55]. The industrial structure of provinces and cities such as Beijing, Shanghai, and Guangzhou in China also shows this trend. The proportion of the tertiary industry is very high, such as 81.0% and 69.9% in Beijing and Shanghai in 2018 respectively. It is a general

trend for Chongqing’s industrial structure to shift from the original "one-two-three" model to a "three-two-one" model. By 2018, the proportions of the "one-two-three" industries were 6.80%, 40.90%, and 52.30% respectively, which are close to the national overall levels of 7.20%, 40.70%, and 52.00%. In the future, the proportion of the primary and secondary industries will further decline. Taking into account internal and external factors and development trends, it is considered to set the targets for the tertiary industry structure at 5%, 35%, and 60% for optimization. Thirdly, Due to the different dimensions and data sizes of each order parameter index, it must be standardized. The commonly used standardization methods include Z-score stan- dardization and averaging, but neither of them can ensure that the standardized data is greater than 0, which cannot meet the requirements of the entropy weight method. The range stan- dardization is adopted here. For the indicators that have been processed in the forward direc- tion (the larger the better type indicator), rij = [xij − min(xij)]/[max(xij) − min(xij)] is adopted for standardization. Fourthly, the entropy weight method is used to weight the index system, which can better retain the original information of the index data and improve the objectivity of the evaluation. If the smaller the information entropy of an index, the greater the degree of variation of its index value, the greater the amount of information it provides, and the greater its role in the

comprehensive evaluation, the greater its weight should be. On the contrary, the larger the information entropy of an index, the smaller the degree of variation of its index value, the smaller the amount of information it provides, and the smaller the role it plays in the compre- hensive evaluation, the smaller the weight should be. The results are shown in Table 3. 3.2 Result analysis Performance evaluation is conducted from both subsystem and composite system levels. At the subsystem level, the effectiveness coefficient of order parameters and the orderliness of subsystems are mainly calculated, with a focus on analyzing the development trend of the orderliness of industry and education subsystems. At the composite system level, the coupling degree and coupling synergy degree are mainly calculated, with a focus on analyzing the evolu- tion trend of interactive coupling and collaborative development of the industry-education composite system. 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 13 / 22