Exploring the Development Path of Collaborative Educational Programmes in Colleges and Universities Based on the Background of “Double-High Construction”

With the deepening development of China’s education reform, higher education institutions also put forward higher requirements, the construction of multidisciplinary curriculum teaching collaborative parenting approach has been more widely concerned and attention [1-3]. At the present stage of the development of multidisciplinary curriculum teaching collaborative education mode, most colleges and universities have carried out in-depth discussion and research, and the implementation of a series of scientific and effective optimization measures, but in improving the multidisciplinary curriculum education collaborative education enthusiasm in depth is insufficient. Colleges and universities should be based on the collaborative parenting education model to create an intrinsic logical connection [4-5].

In the new era, to promote the collaborative parenting of all types of courses in colleges and universities, to give full play to the synergy of ideological and political parenting of all types of courses, and to realize the educational effect that cannot be achieved by one course, its essential attribute is the innovation of educational governance [6-7]. In order to achieve the goal of “two hundred years”, colleges and universities will collaborate with all kinds of educational resources, gather the collaborative power of ideological education, do a good job in all kinds of courses, the effective connection of all levels of education, and play the role of the university of moral education [8].

Colleges and universities should deepen the concept of multidisciplinary education and implement it in their daily educational work. At the same time, the spirit of collaborative education is continuously integrated into all teaching activities, to recognize the differences between professional courses and disciplinary courses, define the status of the two in the multidisciplinary collaborative education, grasp the clear positioning of multidisciplinary peer-to-peer, and improve teachers’ awareness and understanding of the nature of collaborative education [9]. Literature [10] describes the important role played by undergraduate education curriculum teaching in professional development, helping students to play an active role in adapting to and familiarizing themselves with the content of vocational work, and providing a practical platform for vocational internships. Literature [11] based on a structured questionnaire approach found that moral courage of nurse graduates is influenced by more factors, but overall the world has a high level of recognition of moral courage of nurse graduates. Literature [12] in the report on moral dilemmas of trainee teachers emphasized the importance of pre-service teacher education in order to help teachers and students to establish their own ethical code, and also pointed out the contextualization of moral dilemmas, and the study provided an important reference for the guidance and management of trainee teachers. Literature [13] example analysis discusses the need for educators to prioritize guidance and assistance from the perspective of the emotional object in the process of student learning in social sciences.

Collaborative education in colleges and universities is characterized by complexity and diversity, including a number of assessment indicators and objectives of personnel training. Literature [14] describes the International Teaching and Learning Collaboration (EKT) project and proposes a learning analytics strategy to collect information and data about the teaching and learning process, as well as analyzing the process, storing and backing up the records, which contributes to the optimization of teaching and the reform of teaching standards. Literature [15] describes the importance of environmental education and the current shift in the context of real-life examples and semi-structured interviews, and argues that environmental education and the popularization of public values promotes reciprocal decision-making about things that are not human and that are subject to human decision-making. Literature [16] reveals the sharp contradictions that exist in the quality of life and health in schools and uses questionnaires to collect relevant information and employs implicit analysis and game-howell post-hoc tests, pointing out that the data and archival information obtained from the collection and analysis are very prescient in terms of health. Literature [17] explored the quality of higher education and the current status of reforms, and the results of the study showed that the reform measures in higher education involve budget planning, the training and introduction of specialists, as well as curricular training, which significantly affect the effectiveness and scientificity of the reforms.

In this paper, the evaluation index system is quantitatively analyzed from the perspective of input-output of colleges and universities, based on the design principles of evaluation indexes, the preliminary selection of evaluation indexes is carried out, the effective factors are extracted, the evaluation indexes that meet the conditions are screened and the final evaluation index system is further screened according to the design principles of the indexes and then evaluated by the DEA-Malmquist evaluation model on the double-high construction. Quantitative analysis of the evaluation index system for collaborative education between industry, university, and research in the context of the DEA-Malmquist evaluation model. Structural Equation Model: The structural equation model of University-Industry Collaborative Education is calibrated and revised with empirical statistical data to explore the correlation, influence factors, loading coefficients, and influence effects among the factors and give the actual degree of influence, so as to realize the optimization of University-Industry Collaborative Education in the context of the Double-High Construction.

2

Industry-university-research collaborative education in “double-high construction” colleges and universities

2.1

Double-high construction

“Double-high construction” refers to the construction plan of high-level high-vocational schools and professions with Chinese characteristics, aiming at creating a highland for cultivating technical and skilled talents and a platform for technical and skilled innovation and service, leading vocational education to serve national strategies, integrate into regional development and promote industrial upgrading.

2.2

Construction of industry-university-research cooperative education mechanism

The multi-dimensional collaborative education model is shown in Figure 1. A close cooperative relationship is established among schools, enterprises and research institutions, the roles and responsibilities of each subject in the process of industry-university-research are clarified, and the effectiveness and continuity of cooperation are ensured by signing cooperation agreements, formulating cooperation plans and establishing cooperation platforms. Oriented by talent cultivation objectives, and with deep collaboration between industry, academia and research as the key, multiple subjects such as government, schools, enterprises and research institutions jointly participate in the formulation of talent cultivation programs to ensure that the content of the curriculum is in line with the market demand, industry standards and enterprise demand, and that the hours of theoretical and practical teaching are reasonably arranged according to the objectives and requirements of talent cultivation to ensure that the students can obtain comprehensive knowledge and skills. Local governments play the roles of macro-control, mechanism guarantee, and guidance service in the collaborative education model, providing policy guidance, legislative guarantee, and financial support.Colleges and universities invite enterprise experts to participate in the formulation of talent training programs, teaching standards, and syllabi.Enterprises should increase their initiative in the collaborative education model, appropriately increase the input of professional and technical personnel, and seek long-term cooperation and benefits.Scientific research institutions offer scientific research projects and innovation platforms, broaden the scope and depth of teachers’ scientific research, construct scientific research teams, and bring fresh talent into R&D teams. Each subject gives full play to its own advantages to build a “common construction, sharing and co-management” model of education.

An effective communication mechanism is established between the participating subjects of industry, academia, and research. A substantial communication and research platform is established, and a smooth path of resource sharing is established. Schools, enterprises and research institutions share practical teaching resources, including experimental equipment, training bases, research facilities, etc., to reduce the cost of practical teaching and improve the quality and efficiency of practical teaching. Schools, enterprises, and research institutions send teachers to each other for mutual learning and exchanges. Teachers participate more in the practice of enterprises, learn about the development of related industries, the latest achievements of research institutions and the new processes and technologies of enterprises, so as to promote the matching of teaching contents with the current scientific and technological development, and the knowledge learned by students with the market demand. Schools, enterprises and scientific research institutions jointly participate in curriculum development, and develop courses that meet the actual needs and ensure the practicality and relevance of the courses by combining market demand, industry standards and enterprise culture. Schools, enterprises, and scientific research institutions carry out scientific research cooperation, joint research and technology development, and promote the organic combination of scientific and technological innovation and talent training. Diversified evaluation methods are adopted, including students’ self-assessment, mutual evaluation, teachers’ evaluation, enterprises’ evaluation and industry experts’ evaluation, etc., in order to comprehensively understand students’ learning situation and comprehensive quality.

In the process of developing and educating people, enterprises and research institutions need to demonstrate their primary role and participate in the university’s activities.According to the requirements of enterprise development and scientific research institutions, the university will conduct a specialized course that conforms to the characteristics of the new industry.Universities, enterprises, and research institutions should combine curriculum construction and project implementation with graduation practice, and conduct selection course design for local enterprises and scientific research institutions.At the same time, the three parties should unite to adopt the technology and encourage students to study in enterprises or scientific institutions, participate in the development and implementation of the project.

3

Evaluation System of Industry-University-Research Collaborative Education in the Context of Double-High Construction

3.1

DEA model

Since the emergence of the data envelopment analysis method, hundreds of models have been developed and evolved. Data envelopment models CCR and BCC can be divided into two broad categories based on whether there is a change in the scale of the model. The premise of CCR assumes constant returns to scale. While BCC model assumes that the scale reward is variable. The CCR model planning is: if there is m type of input, s types of outputs, and n decision units, then: (1) $h_{j} = \frac{U^{T} y_{i}}{V^{T} x_{j}} = \frac{\sum_{r = 1}^{s} U_{r} Y_{r j}}{\sum_{i = 1}^{m} V_{i} X_{i j}} \leq 1, j = 1, 2, \dots, n$

In equation (1):

DMU is the decision making unit.

x_ij -- The total amount of inputs of the jnd DMU to the ird input, x_ij > 0.

y_ri -- the total amount of outputs of the jth DMU to the th output, y_rj > 0.

v_i -- one measure for the ith input, weight coefficient.

u_r -- One metric for the rth input, weight coefficient.

i -- 1,2,…,m.

j -- 1,2,…,n.

r -- 1,2,…,s.

Efficiency evaluation of the j₀st decision-making unit, the larger h_j0 means the higher input-output efficiency of this decision-making unit.

Assuming that the efficiency index of the j₀rd DMU is maximized and the efficiency index of all DMUs is less than 1 is the constraint, the model is constructed as follows: (2) ${\begin{matrix} \max h_{j 0} = \frac{\sum_{r = 1}^{s} u_{r} y_{r j 0}}{\sum_{i = 1}^{m} v_{i} x_{i j 0}} \\ s . t . \frac{\sum_{r = 1}^{s} u_{r} y_{r j}}{\sum_{t = 1}^{m} v_{i} x_{i j}} \leq 1, j = 1, 2, \dots, n \\ u \geq 0, v \geq 0 \end{matrix}$

Take the fractional planning model obtained above and do a Chames-Coopere variation such that: (3) $t = \frac{1}{v^{T} x_{0}}, w = t v, μ = tu$

So, from $t = \frac{1}{v^{T} x_{0}}$ you can get w^tx₀ = 1, which in turn gives: (4) ${\begin{matrix} \max h_{0} = μ^{T} y_{0} \\ s . t . w^{T} x_{j} - μ^{T} y_{i} \geq 0, j = 1, 2, \dots, n \\ w^{T} x_{0} = 1 \\ w \geq 0, μ \geq 0 \end{matrix}$

The pairwise planning of the above planning model is as follows: (5) ${\begin{matrix} \min θ \\ s . t . \sum_{j = 1}^{n} λ_{j} x_{j} \leq θ x_{0} \\ \sum_{j = 1}^{n} λ_{j} y_{i} \geq θ y_{0} \\ λ_{j} \geq 0, j = 1, 2, \dots, n \\ θ unconstrained \end{matrix}$

For Eq. (5) the residual variable s⁻ and slack variable s⁺ are introduced and obtained after variation: (6) ${\begin{matrix} \min θ \\ s . t . \sum_{j = 1}^{n} λ_{j} x_{j} + s^{+} = θ x_{0} \\ \sum_{j = 1}^{n} λ_{j} y_{i} - s^{-} = θ y_{0} \\ λ_{j} \geq 0, j = 1, 2, \dots, n \\ θ unconstrained, s^{+} \geq 0, s^{-} \leq 0 \end{matrix}$

Equation (6) is the pairwise planning of the original plan, which is convenient for solving the CCR model.

If the optimal solution of Eq. (6) is λ*,s*⁻,θ*, then the evaluation decision cell j₀ given by model (D) is the definition of DEA effective.

If θ* = 1, s*⁺ = 0 and s*⁻ = 1, then the DEA of DMU_j0 is efficient, that is, there is w* > 0, μ* > 0 in the solution of the original linear programming such that the optimum value h*_j0 = 1. Then the production activity of decision sheet DMU_j0 is scale efficient and technologically efficient.

If the slack variable for an input or output is greater than 0, then at this point θ* = 1, call DMU_j0 weakly DEA efficient, and technically efficient and scale efficient are not both present, proving that there is input redundancy or insufficient output at this point. If θ* < 0, then decision unit DMU_j0 is not DEA effective.

3.2

Malmquist exponential analysis model

1)

Malmquist Index

(X_t,Y_t) and (X_t+1,Y_t+1) denote the inputs in periods t and t+1, and $D_{0}^{t} (X_{t}, Y_{t})$ and $D_{0}^{t} (X_{t + 1}, Y_{t + 1})$ denote the distance function between the input vector and the output vector in periods t and t+1 for the technology referenced in period t to the technology in period t, respectively. T^t,t+1 The technology in period T^t+1 is the reference.

Then the Malmquist index with technology T^t in period t as the reference is: (7) $M_{0}^{t} (X_{t}, Y_{t}, X_{t + 1}, Y_{t + 1}) = \frac{D_{0}^{t} (X_{t + 1}, Y_{t + 1})}{D_{0}^{t} (X_{t}, Y_{t})}$ t+1 period of the technology T^t+1 for the reference Malmquist index is: (8) $M_{0}^{t + 1} (X_{t}, Y_{t}, X_{t + 1}, Y_{t + 1}) = \frac{D_{0}^{t + 1} (X_{t + 1}, Y_{t + 1})}{D_{0}^{t + 1} (X_{t}, Y_{t})}$

Using the DEA method to measure the Malmquist production index, the average of the set of Malmquist indices for the two periods should be chosen for the calculation in order to avoid the discrepancy caused by the arbitrariness of the period selection. Then the Malmquist index of total factor productivity (TFP) from period t to period t+1 is: (9) $M_{0} (X_{t}, Y_{t}, X_{t + 1}, Y_{t + 1}) = {[\frac{D_{0}^{t} (X_{t + 1}, Y_{t + 1})}{D_{0}^{t} (X_{t}, Y_{t})} \times \frac{D_{0}^{t + 1} (X_{t + 1}, Y_{t + 1})}{D_{0}^{t + 1} (X_{t}, Y_{t})}]}^{\frac{1}{2}}$

2)

Decomposition of the Malmquist index

The Malmquist index is decomposed under the condition of constant scale efficiency into the index of technical efficiency change (effch, i.e., the actual level of output and the proximity of the production boundary) and the index of technical progress (techch, i.e., the degree of change in the production boundary change over time), so that the equation (9) can be decomposed as: (10) $M_{0} (X_{t + 1}, Y_{t + 1}, X_{t}, Y_{t}) = \frac{D_{0}^{t + 1} (X_{t + 1}, Y_{t + 1})}{D_{0}^{t} (X_{t}, Y_{t})} {[\frac{D_{0}^{t} (X_{t + 1}, Y_{t + 1})}{D_{0}^{t + 1} (X_{t + 1}, Y_{t + 1})} \times \frac{D_{0}^{t} (X_{t}, Y_{t})}{D_{0}^{t + 1} (X_{t}, Y_{t})}]}^{\frac{1}{2}}$ i.e., M₀(X_t+1,Y_t+1,X_t,Y_t) = TEC×TP.

When effch = 1, it indicates constant technical efficiency. When effch > 1, it indicates an increase in relative technical efficiency. When effch < 1, it indicates a decrease in relative technical efficiency.

When techch = 1, it indicates no change in technology. When techch > 1, it indicates technical progress. When techch < 1, it indicates technical decline.

When the returns to scale are variable, the technical efficiency change index (effch) can be divided into the product of the pure technical efficiency index (pech) and the scale efficiency index (sech). That is, equation (10) can be decomposed as: (11) $M_{0} (X_{t}, Y_{t}, X_{t + 1}, Y_{t + 1}) = p e c h \times sech \times t e c h c h$

3.3

Implementation steps of the DEA-Malmquist evaluation model

The evaluation steps using this model are shown in Figure 2.

1) Determine the evaluation objectives. The cultivation quality evaluation index system and data envelopment analysis model must be constructed with a clear evaluation objective in mind.In this paper, we aim to evaluate the quality of cultivation of colleges and universities based on collaborative education, and use the DEA-Malmquist model to carry out a comprehensive evaluation of their cultivation quality.

2) Selection of decision-making unit. The decision-making units of the evaluated DMUs should be selected taking into account the characteristics of homogeneity, i.e., having the same tasks and objectives, input and output indicators, and external environment. In addition, the number of DMUs should not be less than two times the total number of indicators.

3) Establish the input and output indicator system. Before utilizing the model to evaluate cultivation quality, a complete set of input and output indicator systems should be established first.The evaluation objectives and design principles guide the selection of evaluation indicators. In this paper, the quality of cultivation based on collaborative parenting is taken as the evaluation goal, and the influencing factors of collaborative parenting should be taken into account when designing the indicators.

4) Collecting and organizing data information. The accuracy of data and the scientificity of data processing are prerequisites for ensuring the scientific and effective research of academic research. In this paper, a large amount of data is needed to support the construction of the training quality evaluation index body based on collaborative education and the empirical study of training quality evaluation, so it is necessary to ensure the accuracy of the input and output index data.

5) Use DEA model to do static evaluation of cultivation quality. With the help of DEA2.1 software, static evaluation of the quality of education cultivation in colleges and universities based on collaborative education can be conducted, and the evaluation results can be analyzed.

6) Do dynamic evaluation using Malmquist index analysis model. On the basis of static evaluation, the two adjacent periods are used as research objects. In this paper, for the change of cultivation quality in the two adjacent periods, Malmquist index method is applied to do dynamic evaluation and analysis of cultivation quality of colleges and universities based on collaborative parenting using DEA2.1 software.

4

Research on the evaluation of industry-university research in higher education institutions and optimization of pathways

4.1

Development of Industry-University-Research Collaborative Educational Programs

In order to promote industry-university-research cooperation among higher education institutions, scientific research institutes and enterprises and institutions in a certain region, expand advantageous postgraduate education resources, further deepen the reform of postgraduate cultivation mechanism and talent cultivation mode, vigorously develop innovative education, and strengthen the synergy among education, science and technology, economy, state-owned capital and other departments, the construction of demonstration bases for joint cultivation of postgraduate students by industry-university-research has been initiated. By looking up relevant research data and government public documents, combining interviews with people, the paper collects and statistics the relevant data. The distribution of demonstration bases for joint cultivation by industries, universities and research institutes is shown in Table 1. So far, 102 district-level joint industry-university-research postgraduate demonstration bases have been jointly established by cultivating universities, research institutes, enterprises, and institutions. According to the nature of the co-construction units, there are 27 scientific research institutes, 28 administrative institutions, 42 enterprises, and 5 medical units, which shows that the co-construction units are mainly enterprises, with equal importance given to scientific research institutes, administrative institutions, and less to medical units, and that there are fewer medical units. The reason for fewer medical units is that most of the high-quality medical resources are concentrated in clinical hospitals affiliated with universities. Through four years of operation, the demonstration base for joint cultivation of postgraduates has played an important role in the cultivation of high-level talents, realizing the complementary advantages and mutual promotion of educational and scientific resources between universities and co-built units.

Table 1.

The distribution of the demonstration base of the production and research

Classification	Base number	The inter-school joint development base
Research institute	27	9
Administrative unit	28	2
Enterprise	42	3
Medical unit	5	5
Total	102	19

4.2

Status of DEA inputs and outputs

According to the evaluation indexes, the input and output of the construction of demonstration bases in a certain region are counted, and the input and output efficiencies are shown in Table 2. The input efficiency of the joint industry-university-research training demonstration bases has been maintaining a high level of development, and it is no longer possible to increase the amount of output on the basis of the existing scale as well as under the technical conditions and management level, and the utilization rate of the resources has reached the maximum value (comprehensive efficiency 1). In order to meet the demand for educational resources for the growing scale and from the perspective of educational quality, to make the four types of demonstration bases reach the comprehensive efficiency to remain unchanged, it is necessary to Increase the input efforts, firstly, to expand the scale of the construction of the demonstration bases for joint cultivation of industry-university-research institutes in order to increase the efficiency of scale, and to increase the total amount of input fundamentally, which affects the efficiency of the outputs. Second, encourage and incentivize base co-construction units, especially enterprises, to increase the number of senior technicians with guidance qualifications, support more construction funds and provide more scientific research instruments and equipment, which can effectively increase the total amount of inputs in a short period of time, as well as provide hardware and software support for the professional degree to reach the scale of more than 50% of the whole master’s degree enrollment ratio in the future. Thirdly, improve the evaluation system, construct evaluation indexes that better reflect collaborative education, give full play to the role of schooling orientation of joint training, and promote closer integration of training quality standards with the development needs of the base. Fourthly, promote scientific management, and through the adjustment of policies and systems, promote the increase of inputs from both sides of the base, so as to guarantee and promote the efficient operation and healthy and orderly development of the demonstration base.

Table 2.

Input situation statistics

	Number of doctoral instructors	Master’s advisor number	Construction funds invested (10,000)	The number of equipment and equipment provided by the research practice	Output efficiency
Research institute	47	156	2487	772	1
Administrative unit	3	112	181	1335	1
Enterprise	8	172	6190	782	1
Medical unit	14	103	242	2389	1
Total	72	543	9100	5278

4.3

Evaluation of Industry-University-Research Collaborative Education in “Double-High Construction” Colleges and Universities

The main sources of data for the study are the relevant data released by 56 “double-high” construction institutions. The empirical study found that the number of DMUs participating in the evaluation should be at least twice as many as the sum of the number of inputs and outputs, and the research input and output indicators should be streamlined as much as possible. Because of the large number of output indicators selected in this study, in order to further reduce the dimensionality, the indicators were first standardized using the extreme value method, and then the entropy method, which is more commonly used in the academic community, was used to assign the weights to sum up the indicators within each dimension. In summary, the input-output evaluation index system of “double-high” construction institutions and its weights are shown in Table 3. Estimating the relative importance of the input-output indicators of “double-high” colleges and universities can enhance the rationality of the evaluation results. Among the input indicators, the importance of physical input of “double-high” universities is higher than that of financial input and human input, up to 0.651, indicating that the investment of fixed assets of “double-high” universities is an important factor to promote the capacity of scientific and technological resources of the university. In terms of output indicators, the weights of scientific and technological services and social training are higher, especially scientific and technological services with a weight of 0.472, interpreting that scientific and technological services are an important manifestation of the direct use and output of scientific and technological resources of “double-high” universities.

Table 3.

School investment - output evaluation index system

Index category	Primary indicator	Weight	Secondary indicator	Final weight
Input	Financial investment	0.223	Annual funding for raw money	0.042
			Special funds for the year	0.061
			Amount of social capital introduction	0.108
	Material input	0.651	The value of the teaching and scientific instruments and equipment of the raw	0.101
			Teaching and auxiliary, administrative room area	0.058
			The number of teaching methods in the school	0.253
			The value of the teaching equipment provided by enterprises	0.196
	Human input	0.131	The proportion of teachers’ qualified teachers	0.058
			Enrolment	0.024
			The total number of teachers	0.050
			The total number of staff workers in the field	0.049
Output	Talent culture	0.132	Employment number	0.027
			Employment rate	0.002
			Autonomous entrepreneurship	0.089
			Graduates stay local	0.008
			Employer satisfaction	0.005
	Technology service	0.472	Technical services	0.075
			Economic benefits generated by technical services	0.150
			Vertical research funds	0.141
			Technical transaction amount	0.127
	Social training	0.43	Non-education training service	0.181
	Social training	0.43	Non-education training	0.225

4.4

Exploration of the Development Path of Industry-University-Research Collaborative Education in the Context of Double-Higher Education

After evaluating the effect of collaborative parenting of college education programs in the context of double-high construction based on the DEA-Malmquist model in the previous section, this section utilizes structural equation modeling to explore the factors affecting the effect of collaborative parenting of college education programs to explore the path of development of industry-university-research collaborative parenting in the context of double-high construction.

4.4.1

Regression results of the model

The estimation results of the model are shown in Table 4, and *** indicates P<0.001. The correlation between the scientific research capacity of universities, the economic benefits of enterprises (0.934) and the economic benefits of the results of University-Industry-Research Cooperation (0.731) is not significant. In practice, University-Industry-Research cooperation produces synergistic effects, and the final manifestation is the realization of the results of University-Industry-Research cooperation, whose economic benefits can only be produced by transforming scientific research results into commodities through the transformation mechanism. Therefore, the University-Industry-Research cooperation process exists in the scientific research results have not been successfully transformed into commodities to bring economic benefits, but in the process of scientific research has produced a certain social benefits of the phenomenon. The scientific and technological achievements of universities are mainly reflected in papers, which can directly bring social benefits. However, whether they can bring economic benefits is affected by many factors.

Table 4.

The initial type of return to the fruit

Path relation	Estimate	S.E.	C.R.	P
Economic benefit←University scientific ability	-0.001	0.024	-0.091	0.934
Society benefit← the ability of university	0.411	0.038	12.034	***
Economic benefit←enterprise industry benefits	-0.004	0.017	-0.355	0.731
Social benefit←enterprise industry benefits	0.082	0.026	3.268	0.002
Economic benefit←enterprise absorption capacity	0.56	0.087	7.248	***
Social benefit←enterprise absorption capacity	0.081	0.033	2.960	0.004
Economic benefit←The degree of close of the subject	0.089	0.025	4.145	***
Social benefit←The degree of close of the subject	0.093	0.026	3.590	***
Economic benefit←The external environment of the cooperation	0.362	0.088	4.194	***
Social benefit←The external environment of the cooperation	0.231	0.097	2.379	0.022

4.4.2

Estimation and testing of measurement model parameters

The results of parameter estimation for the measurement model are shown in Table 5. The indicators passed the significance level of 0.001, indicating that the model for each potential variable is valid. In the measurement model of the university’s scientific research capacity, the correlation between scientific and technological manpower, research funding, research institutions, scientific and technological projects and outcome outputs and the university’s scientific research capacity are all high, and the standardized loading coefficients of the indicators are, from the largest to the smallest, research funding (0.998), scientific and technological projects (0.958), scientific and technological institutions (0.937), scientific and technological manpower (0.926), and outcome outputs (0.901 ), all of which reflect the university’s research capacity well. In the model of measuring the economic efficiency of the enterprise, the standardized loading coefficients of the indicators are, in descending order, the operating profit margin (0.813), the amount of profit per capita (0.670), and the rate of contribution to total assets (0.521). In the measurement model of the absorptive capacity of the enterprise, the standardized loading coefficients of the indicators are, in descending order, the equivalent full-time equivalent of the enterprise’s R&D personnel (0.972), and the enterprise’s R&D expenditures (0.796). In the measurement model of the degree of closeness between the subjects of industry-university cooperation, the standardized loading coefficient of the indicator of expenditure on domestic enterprises in the external expenditures of university R&D funds (0.978) is larger than the standardized loading coefficient of the indicator of expenditure on domestic universities in the external expenditures of enterprise R&D funds (0.577). In the measurement model of the external environment of University-Industry-Research Cooperation, the standardized loading coefficients of the indicators are, in descending order, the government’s investment in scientific and technological funding (0.816), the strength of the government’s policy on University-Industry-Research Cooperation (0.815), and the number of scientific and technological intermediary service institutions (0.753). In the measurement model of economic benefits of the results of University-Industry-Research Cooperation, the standard loading coefficients of each indicator are, in descending order, the number of authorized patents (0.946), and the sales revenue of new products (0.559). In the model for measuring the social benefits of the results of University-Industry-Research Cooperation, the standard loading coefficients of the indicators, in descending order, are the number of Chinese scientific and technical papers included in the three major searching tools (0.972) and the labor productivity (0.602).

To summarize:

1) Strengthen the government’s role in supporting and guiding industry-university-research cooperation, and establish model projects and model units for industry-university-research cooperation.

2) Increase the guidance on the cooperation between industries, universities and research institutes at the national level, and support the industrial development of economically underdeveloped regions.

3) Strengthening the position of enterprises as the main body of innovation in the system of University-Industry-Research Cooperation and improving the efficiency of economic transformation of achievements.

4) Aiming at the phenomenon that the proportion of enterprises’ investment in University-Industry-Research Institutes has been decreasing year by year, it is suggested that the government further increase the investment in scientific research of University-Industry-Research Institutes, so as to improve the capacity of basic research and applied technology research of University-Industry-Research Institutes.

5) Establishing a complete science and technology intermediary service system, optimizing the external environment of cooperation between industry, academia and research institutes, and improving the rate of transformation of achievements.

In practice, colleges and universities need to guide the reform and development direction of education majors and strengthen the relationship between universities and businesses. The 4-layer course system of “education + professional education + practice education + innovation education” should be optimized. Colleges and universities need to analysis the goals and contents of theoretical teaching, practice teaching and innovation teaching, and sees whether it can meet the needs of local enterprises and emerging enterprises. At the same time, the professional knowledge that the students have acquired can be reinforced and linked to the skills required in the practice section.

Table 5.

Estimation of parameter of measurement model

	Standard path coefficient	The number of the circuit diameter of the final beacon	S.E.	C.R.	P
Fruit yield←the university of large science	0.901	1	--	--	***
Technical item←the university of major science	0.958	0.64	0.038	19.918	***
Scientific institution←university research ability	0.937	0.664	0.041	18.681	***
Study fee ←the university of major research	0.998	0.848	0.043	22.813	***
Technical force←the university of major science	0.926	0.419	0.029	18.041	***
The total yield of the company←the economic benefit of the enterprise	0.521	1	--	--	***
Revenue of the business ←the economic benefit of the enterprise	0.813	0.916	0.201	4.695	***
Per capita profit←the economic effect of the enterprise	0.67	0.162	0.039	4.867	***
The enterprise personnel can reduce the total time equivalent←the ability of enterprises to absorb	0.972	0.824	0.065	13.841	***
Corporate expenditure←the ability of enterprises to absorb	0.796	1	--	--	***
The university has a foreign government expenditure←the degree of the incomitence between the main body of the production	0.978	1	--	--	***
The university has paid outside the foreign companies to pay for foreign companies←the degree of the incomitence between the main body of the production	0.577	0.39	0.055	7.947	***
The government’s cooperation policy is strong←the external environment of the cooperation	0.815	1	--	--	***
The government branch is invested in←the outer part of the production study	0.816	1.788	0.201	9.149	***
Number of research services←The external environment of the cooperation	0.753	1.578	0.188	8.66	***
Sales of new products←economic benefit	0.559	1.004	--	--	***
Patent authorization←economic benefit	0.946	1.442	0.201	7.39	***
Number of Chinese technical papers←social benefit	0.972	1.004	--	--	***
Raudogenesis rate←social benefits	0.602	2.135	0.291	7.474	***

5

Conclusion

This paper applies the DEA-Malmquist method to evaluate the inputs and outputs of double-high construction colleges and universities, forms the evaluation index system of collaborative education in close cooperation between industry, academia and research, and explores the optimization of the path of collaborative education between industry, academia and research through the structural model. The results show that:

1) Universities in the sample area have established 102 demonstration bases for joint cultivation of postgraduate students by industry, university and research, and their comprehensive efficiency of inputs and outputs has reached the maximum value.

2) The fixed asset investment in the “double-high construction” of colleges and universities enhances the most important factor of scientific and technological resources of colleges and universities, and the weight reaches 0.651.

3) In the path optimization, the support and guidance of the state and the government should be strengthened, a perfect service system should be established, and the transformation efficiency should be improved.

Funding:

This research was supported by Research Project on Educational and Teaching Reform of Vocational Institutions in Yongzhou City in 2023 (yzzyjy2023048).

Langue:: Anglais

Périodicité:: 1 fois par an
Sujets de la revue:: Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres

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Exploring the Development Path of Collaborative Educational Programmes in Colleges and Universities Based on the Background of “Double-High Construction”

Haibo Wang

Publié en ligne: 17 mars 2025

Reçu: 02 nov. 2024

Accepté: 19 févr. 2025

DOI: https://doi.org/10.2478/amns-2025-0237

Mots clésDEA-Malmquist, Structural equation modeling, Industry-university-research cooperation, Evaluation index system

© 2025 Haibo Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
DEA-Malmquist, Structural equation modeling, Industry-university-research cooperation, Evaluation index system