Innovative Research on Digital Talent Cultivation Mode of Higher Vocational Innovation and Entrepreneurship Education Promoted by Industry-Teaching Integration

With the arrival of the digital era, information technology has brought many new changes to people’s lives and work. In the context of digitalization, constantly improving the quality of digital talent training in higher vocational schools helps to promote the development of the market economy and meet the needs of enterprises for talent [1–2]. In view of the regional economic and social construction and development, conforming to the requirements of the employment market, actively cultivating professional applied and skilled digital talents needed for production, service and other front-line positions is the primary task of the current educational work of higher vocational colleges and universities [3–4]. At present, whether for the national policy orientation, the need for sustainable social and economic development, or the inherent requirements of the reform and development of colleges and universities themselves, we should vigorously develop innovation and entrepreneurship education in colleges and universities and build a scientific, adaptable to the development of the times, and able to support the lifelong development of students’ innovation and entrepreneurship education talent cultivation model [5–6]. As an important type of higher education, higher vocational colleges and universities have their own unique characteristics and development paths, and in the reform process of innovation and entrepreneurship education, higher vocational colleges and universities aiming to cultivate high-quality skilled digital talents cannot be absent. The implementation of innovation and entrepreneurship education is not limited to the type and level of school but should face all students of all majors. Every year, higher vocational colleges and universities send a large number of graduates to society who devote themselves to China’s economic construction and make important contributions to the social and economic development and the enhancement of the country’s comprehensive national strength. They also play a vital role in various fields of social production [7–9]. However, it is undeniable that due to the gaokao system, students admitted to Chinese higher vocational colleges and universities have problems such as short school hours, low scores, weak foundation, poor learning ability, etc., which puts them at a disadvantage in terms of competitiveness in the severe employment situation.

On the other hand, employers also suffer from difficulties in recruiting talents with high quality and high skills, and the structural contradiction of employment is prominent [10–11]. It can be seen that carrying out innovation and entrepreneurship education in higher vocational colleges and universities, fully developing students’ personalities through the scientific digital talent cultivation model, cultivating high-quality builders with innovative thinking, entrepreneurial ability, the courage to explore, not afraid of challenges, and the ability of continuous learning, and improving students’ employability is a must for the reform of higher vocational colleges and universities education. He must be realized through a scientific and reasonable throughout the whole process of talent training, innovation and entrepreneurship education digital talent training model [12–14].

In order to adapt to industrial upgrading and structural adjustment, higher vocational colleges and universities focus on innovation and entrepreneurship education for talents under the background of industry-education integration so as to give society the opportunity to cultivate innovation and entrepreneurship talents that are urgently needed for industrial development. Literature [15], under the background of industrial empowerment, studies the strategic necessity of optimizing the professional layout of vocational education under the dynamic development of industry, explores the paths and strategies of effectively connecting education and industry, and aims to cultivate talents who can promote industrial and economic reform. Literature [16] takes the accounting specialty of higher vocational colleges as an example, constructs an accounting talent cultivation model based on the integration of industry and education, and evaluates and analyzes the model by using hierarchical analysis, and verifies that the accounting talent cultivation model is at a medium level through actual cases. Literature [17] points out the urgent demand of society for high-quality, innovative talents and technical skills, takes the two core issues of “what kind of professionals to cultivate” and “how to cultivate” as the entry point, and puts forward the idea of establishing a system of collaborative practice teaching between industry, academia and research and establishing a system of collaborative practice teaching between industry, academia and research. With the two core issues of “what kind of professional talents to cultivate” and “how to cultivate” as the starting point, it puts forward the proposal of “establishing a collaborative practice teaching system of industry-academia-research and deepening the collaborative innovation ability cultivation mode of industry-academia-research” to help cultivate the professional talents in local colleges and universities in the era of integration of industry and education. Literature [18] puts forward an innovation and entrepreneurship talent cultivation path for the problems of innovation and entrepreneurship education in colleges and universities, which is based on the five aspects of clarifying the thinking concept, innovating the education mode, promoting the combination of industry, teaching and research, strengthening the construction of teachers and establishing a sound evaluation mechanism, in order to comprehensively improve the level of innovation and entrepreneurship talent cultivation in colleges and universities.

Meanwhile, based on the immature innovation and entrepreneurship education concept, the imperfect innovation and entrepreneurship education system, the shortage of educational resources and the contradiction with the traditional teaching mode, combined with the characteristics of regional economy and culture, literature [19] put forward the corresponding innovation and entrepreneurship talent cultivation path in applied undergraduate colleges and universities, and the study provides reference value for the reform and improvement of talent cultivation path in Chinese institutions of higher education. Literature [20] points out that the innovation and entrepreneurship education in higher vocational colleges and universities at present is not compatible with the needs of economic and social development, takes the e-commerce major in higher vocational colleges and universities as an example, analyzes the current situation and problems of its innovation and entrepreneurship talent cultivation, and combines with the characteristics of e-commerce professional innovation and entrepreneurship practice, puts forward the talent cultivation mode of e-commerce majors in higher vocational colleges and universities, and offers a new way of thinking for the research of innovation and entrepreneurship education. Literature [21] emphasizes the importance of cultivating the innovation and entrepreneurship ability of contemporary college students in the context of the Internet era and indicates that higher vocational colleges and universities can cultivate practical innovation and entrepreneurship talents through school-enterprise cooperation, which mainly discusses the innovation and entrepreneurship reform mechanism in the Internet era, the great achievements of the innovation and entrepreneurship talent cultivation program, and the key role in the construction of high-quality colleges and universities. After analyzing the current situation of innovation and entrepreneurship education in colleges and universities, literature [22] takes the cultivation of “mass entrepreneurship and innovation” as the entry point and explores the path of constructing a competence-centered, student-oriented and market-oriented innovation and entrepreneurship curriculum system, so as to cultivate innovation and entrepreneurship talents and meet the new requirements of national innovation construction for innovation development.

The integration of industry and teaching is not only an inevitable trend for the high-quality development of higher vocational education, but also an effective way to cultivate digital talent in vocational education. By analysing the trend of the development of the integration of industry and education, this paper lays the foundation for the research on the digital cultivation of innovation and entrepreneurship education in higher vocational colleges from the perspective of the integration of industry and education. In order to achieve a scientific analysis of the dilemmas and problems of the development of industry-education integration, this paper constructs a comprehensive evaluation index system of industry-education integration by using the fuzzy hierarchical analysis method. Through the evaluation results of the integration of industry and education in higher vocational innovation and entrepreneurship education, the resource allocation problems of integration between industry and education are analyzed. In order to solve the problem of the development of industry-teaching integration, this paper proceeds to the talent cultivation mechanism and resource cooperation path to put forward the digital talent cultivation model of higher vocational innovation and entrepreneurship education, which is of great theoretical and practical significance.

2

Development of industry-teaching integration for higher education in innovation and entrepreneurship education

2.1

Study on the Evolution of Trends in the Development of Industry-Education Integration in Higher Education

The integration of industry and education refers to further expanding the social service functions of higher vocational colleges and universities, strengthening the integration of higher vocational education with the economy and industry, and closely combining industry and teaching. The main subjects of the integration include the government, higher vocational colleges and universities, enterprises, etc., and its main characteristic is marketability, i.e., its contents and methods are adjusted according to the needs of the changes in economic and social development. The strategic deployment of the modern vocational education system at the national level clearly points out that it is necessary to build a perfect modern vocational education system, increase the integration of industry and education, and encourage school-enterprise co-operation, with schools conveying talents to enterprises, and enterprises providing schools with funds, practical training and other support, so as to improve the quality of talent training and ensure that society can have a large number of highly skilled personnel. To promote employment and service development as the general policy of running the school, to promote the mechanism of continuous and in-depth reform, strengthen the innovation of vocational education model, good industrial integration, support school-enterprise cooperation, and effectively achieve the unity of knowledge and practice, to play a good role in guiding the community, so that they can take the initiative to participate in the activities of vocational education, vocational education to inject new vitality, and to build a perfect vocational education system. At the same time, vocational education should be in line with the trend of technological progress and changes in the mode of production, which requires the further development of modern vocational education, matching the social public services, and truly realise the integration of industry and education, so that the social forces actively participate in the construction of vocational education. The integration of industry and education helps to modernise vocational education, vocational colleges and universities should follow this requirement and actively cooperate with industries and enterprises to ensure that the professional settings effectively meet the needs of regional industries, the content of the curriculum effectively meets the occupational standards, the teaching process better connects with the production process, the graduation certificate seamlessly connects with the vocational qualification certificate, vocational education effectively integrates with lifelong learning, and the formation of a community of interest, to truly achieve a win-win situation for all. The creation of a community of interest will lead to a win-win situation for all parties.

In this section, we first study the development trend of the integration of industry and education through the combing of related literature, which helps to carry out an in-depth analysis of both industries and education.

In order to study the current situation and evolution trend of the integration of higher vocational education, this paper is based on the Chinese database and searched based on the keyword “integration of industry and education”. During the search process, a total of 640 documents were found, including 500 journal articles, 22 dissertations, and 38 newspapers, yearbooks and book patents. An in-depth analysis of the annual trends in the publication of this literature was carried out using visual econometric analysis methods. Journal papers on the topics of industry-teaching integration, school-enterprise cooperation, and community building included in the Knowledge Network Database between 2014 and 2023 were selected and analysed visually using bibliometric methods with the help of CiteSpace software, and the annual trend of literature publication is shown in Figure 1. Among them, the literature related to the integration of industry and education has a clear upward trend after 2017, and the publication year trend rises to 47 articles in 2020, followed by a decline in 2021 and 2022 shows, but then grows rapidly and has a momentum of continuous growth.

In order to further analyse the development and evolution trend of the integration of industry and education, this paper collates the analysis of the related directors of the integration of industry and education in innovation and entrepreneurship education, and the secondary themes include ten kinds of integration of industry and education, digitalization, school-enterprise cooperation, vocational colleges and universities, talent cultivation, collaborative cultivation, school-enterprise community, cultivation mode, vocational education, and integration mode, etc., which are recorded in order as Theme1~10. The distribution of the secondary themes in the literature is shown in Fig.2. The top three secondary themes of industry-education integration are 3,875 articles on industry-education integration, 855 articles on talent cultivation, and 758 articles on digitalization, respectively. According to the research on the trend of industry-education fusion, it is found that industry-education fusion is a kind of community of interest that covers all elements and all-around integration, and its purpose is to meet the common development needs of two types of organisations with highly complementary resources. The research on the integration of industry and education in innovation and entrepreneurship has gradually been integrated with the development of intelligent technology in recent years, and the digital road has gradually become mainstream. Meanwhile, the deepest manifestation of industry-education integration is the establishment of a community of interest between enterprises and schools, which is reflected in the sub-themes of school-enterprise cooperation (321 articles) and vocational colleges (501 articles). Therefore, based on the perspective of industry-education integration, in innovation and entrepreneurship education, taking “innovation” as a centre, connecting the market, industry, talents and scientific research of schools, and promoting the construction of a development community consisting of government, enterprises and colleges and universities with the collaboration of multiple subjects is the key to the cultivation of digital talents for innovation and entrepreneurship education in higher vocational education. Road.

2.2

Analysis of the Dilemma of Industry-Education Integration in Innovation and Entrepreneurship Education

The analysis of the dilemma of industry-teaching integration of innovation and entrepreneurship education should first include a set of scientific evaluation systems. For this reason, this paper, before analysing the dilemma of industry-teaching integration of innovation and entrepreneurship education, constructs a comprehensive evaluation index system of industry-teaching integration by using the fuzzy hierarchical analysis method with triangular fuzzy numbers acting as the language of evaluation and then analyses the situation of its innovation and entrepreneurship industry-teaching integration. Thus, the dilemma of talent cultivation in the development of innovation and entrepreneurship industry-education integration is being studied to develop a scientific and effective digital talent cultivation model.

2.2.1

Comprehensive evaluation methodology for industry-education integration

Hierarchical analysis is a multi-objective decision-making method that combines qualitative and quantitative analysis. Through the construction of a hierarchical analysis model, the relevant factors involved in the decision-making objectives are decomposed step by step and divided into evaluation criteria and indicators of different levels and depths, which can mathematically display the decision-making analysis thinking from a quantitative perspective and have wide and practical applications for complex decision-making problems. The hierarchical analysis model is shown in Figure 3. The hierarchical analysis method has the characteristics of systematic, practical, easy-to-use, and scientific rationality.

The theoretical basis and analytical steps of hierarchical analysis are similar to those of the system analysis method. The first step in the use of hierarchical analysis for decision-making analysis is to determine the evaluation objectives and to construct a hierarchical analysis model on the basis of a comprehensive analysis of the influencing factors related to the evaluation objectives and to build a judgement matrix by comparing the importance of the factors in the hierarchical model, so as to derive the weight of the factors and sort them out, so as to select the most important factors or programmes. The most significant factor or program can be chosen. The hierarchical analysis method has systematic characteristics, and the hierarchical system structure that is constructed simplifies and clarifies complex problems.

Fuzzy comprehensive evaluation is a multi-factor decision-making method for evaluating things with interconnected and interacting factors, and the theoretical basis is fuzzy mathematics, which makes use of fuzzy synthesis to quantify certain fuzzy and difficult-to-quantify factors. Specifically, it is through the construction of multi-level fuzzy subsets of the evaluation of things affecting factors in accordance with the fuzzy indicators for quantification, and the final evaluation benchmarks - comprehensive indicators are obtained by using the principle of fuzzy transformation. Because it is based on fuzzy mathematics, it can better reflect the fuzzy nature of evaluation, and the quantification of indicators reflects objectivity. Therefore, fuzzy comprehensive evaluation can take into account both subjective and objective factors of evaluation.

The basic step of a fuzzy comprehensive evaluation is to quantify all the fuzzy factors affecting the evaluated things by constructing a fuzzy subset (i.e., determining the degree of affiliation), and then use the principle of fuzzy transformation to get the comprehensive evaluation index by integrating the influencing factors.

Using fuzzy comprehensive evaluation modelling based on hierarchical analysis, broadly speaking, the following four steps can be carried out: 1)

Determine the influential factor thesis of the thing being evaluated, n evaluation factor: 1 $u = {u_{1}, u_{2}, \dots \dots, u_{n}}$

2)

Create a rubric rating thesis domain v={v₁,v₂,⋯⋯,v_m} of the thing being rated, i.e., a rubric rating set. Each set of judgement ratings is equivalent to a fuzzy subset.

3)

Generate the fuzzy relation matrix R for single-factor judgement, and single-factor judgement f:U → F(V), u_i ↦ f(u_i)=(r_i₁,r_i₂,⋯r_im)∈F(V). According to the fuzzy mapping f, the fuzzy relation R_f ∈ F(U× V), i.e., R_f(u_i,v_j)=f(u_i)(v_j)=r_ij, can be induced, and thus can be represented by the fuzzy matrix R ∈ μ_n×m: 2 $R = {[\begin{array}{l} r_{11} & r_{12} & \dots & r_{1 m} \\ r_{21} & r_{22} & \dots & r_{2 m} \\ \dots & \dots & \dots & \dots \\ r_{n 1} & r_{n 2} & \dots & r_{n m} \end{array}]}_{n \times m}$

4)

Determine the weights of the factors influencing the thing under review, using hierarchical analysis to determine the normalised weights of the factors of the thing under review A=(a₁,a₂,⋯⋯,a_n). 3 $\sum_{i = 1}^{n} a_{i} = 1, a_{i} \geq 0$

5)

Synthesise the fuzzy comprehensive evaluation composite index, use the appropriate fuzzy operator to synthesise the factor weights of A and the fuzzy matrix of each evaluated thing R, and get the fuzzy comprehensive evaluation composite index of each evaluated thing B. That is: 4 $\begin{array}{l} A \circ R & = (a_{1}, a_{2}, \dots \dots, a_{n}) [\begin{matrix} r_{11} & r_{12} & \dots & r_{1 m} \\ r_{21} & r_{22} & \dots & r_{2 m} \\ \dots & \dots & \dots & \dots \\ r_{n 1} & r_{n 2} & \dots & r_{n m} \end{matrix}] \\ = (b_{1}, b_{2}, \dots \dots, b_{m}) = B \end{array}$

Where, bi indicates the degree of affiliation of the evaluated thing to the fuzzy subset of rank v¡ from the overall point of view.

6)

Analyse the fuzzy comprehensive evaluation result vector

The most commonly used method in practice is the principle of maximum affiliation, but when the number of factors affecting the thing being evaluated is large, the information will be lost, making the information given by the evaluator not fully utilised and leading to incorrect evaluation results. The method of using a weighted average to find the affiliation level can effectively use all the evaluation information, thus overcoming the shortcomings of the principle of maximum affiliation.

Hierarchical analysis is a classic system engineering method from qualitative analysis to quantitative analysis. It will affect the complex system of various factors hierarchical, the importance of the subjective judgement of the factors of the main qualitative analysis of quantitative, through the relatively simple calculation of the importance of the factors of the order of factors so that a variety of complex system of factors between the differences between the numerical. The factor weights determined by the hierarchical analysis method are more objective and accurate than those given directly.

The steps of hierarchical analysis are as follows: 1)

Determine the objectives and evaluation factors

n evaluation factor indicator, u={u₁,u₂,⋯⋯u_n}.

2)

Constructing the judgement matrix

The evaluator makes a two-by-two judgement on the factor indicators to get the ratio of their relative importance. _Generally, a scale of 1 to 9 is used. That is, the judgement matrix is obtained S=(u_ij)_n×n.

3)

Calculate hierarchical single ranking

Calculate the largest eigenroot λ_max of judgement matrix S and its corresponding eigenvector A for all judgement matrices, and this eigenvector is the hierarchical single ranking weight of each evaluation factor.

4)

Calculate the total hierarchical ranking

From the hierarchical single ranking weights of the factors, iteratively calculate the hierarchical total ranking weights relative to the evaluated target. The final required factor weights are the total hierarchical ranking weights.

5)

Consistency test

The consistency test includes hierarchical single-order consistency test and hierarchical tota-lorder consistency test.

The basic concept of fuzzy hierarchical analysis is to use triangular fuzzy numbers instead of 1 to 9 scales for two-_by-two comparative judgements between scenarios. For example, under the criterion of some upper level criterion factor, option i is significantly more important than option j. This can be represented by the triangular fuzzy number a_ij=(l,3,u), where the left and right extensions l, u, indicate the degree of fuzzy judgement. When u–l is larger, the more fuzzy the comparative judgement is. When u–l=0, the comparative judgement is non-fuzzy, the same as the judgement scale 1/3 in hierarchical analysis. The triangular fuzzy number $a_{i j}^{- 1}$ indicates the relative importance of the two-two judgements obtained after the programme j and programme i, then $a_{i j}^{- 1} = {\frac{1}{u}, \frac{1}{3}, \frac{1}{l}}$ . A total of $\frac{n (n - 1)}{2}$ fuzzy judgements need to be completed, and ultimately obtain the judgement matrix A=(a_ij)_n×n, the matrix of all the factors are triangular fuzzy number, called the fuzzy judgement matrix. In general, let A=(a_ij)_n×n be all factors are triangular fuzzy numbers, a_ij=(l_ij,m_ij,u_ij), and have: 5 $l_{i j} = \frac{1}{l_{j i}}, m_{i j} = \frac{1}{m_{j i}}, u_{i j} = \frac{1}{u_{j i}}$

Secondly, in order to calculate the programme hierarchy single ranking weights under a given guideline layer factor, the following formula is relied upon: 6 $S_{i} = \sum_{j = 1}^{m} M_{E_{i}}^{j} \cdot r_{i}^{j} ▯ {[\sum_{i = 1}^{n} \sum_{j = 1}^{m} M_{E_{i}}^{j} \cdot r_{i}^{j}]}^{- 1}, i = 1, 2, \dots, n$

Find the combined importance of all elements in the stratum compared two-by-two with all elements in the model under the criterion of a criterion stratum factor S_i, hypothesis: 7 $d^{i} (A_{i}) = \min V (S_{i} \geq S_{k}), k = 1, 2, \dots, n, k \neq i$ where A_i denotes the factor, which can be made either an alternative measure or a factor at the guideline level. Then, it can be obtained: 8 $w^{i} = {(d^{i} (A_{1}), d^{i} (A_{1}), \dots, d^{i} (A_{n}))}^{T}$

After normalisation, the ranking vector for the n factor A_i(i=1,2,⋯,n) is 9 $w = {(d (A_{1}), d (A_{1}), \dots, d (A_{n}))}^{T}$

Here w is a non-fuzzy value.

One of the problems with the hierarchical analysis method is that the 1 to 9 scale alone cannot better reflect the fuzziness of the evaluation language, which many scholars have criticised, so the fuzzy hierarchical analysis method using triangular fuzzy numbers acts as the evaluation language. This method has a sound theory and a practical calculation method that can be used, which is a good complement to the hierarchical analysis method.

2.2.2

Construction of a comprehensive evaluation system for industry-education integration

Based on the collection and analysis of relevant literature, interviews with relevant experts and scholars and research on the current practice of running higher vocational colleges and universities, this paper constructs indicators for assessing the ability of industry-teaching integration of innovation and entrepreneurship education talent cultivation in higher vocational colleges and universities. The indicator system includes 4 first-class indicators and 14 second-class indicators. The first-class indicators are divided into safeguard measures for industry-teaching integration, resource allocation for industry-teaching integration, process indicators for industry-teaching integration, and effect indicators for industry-teaching integration, and interviews and literature provide the second-class indicators.

A total of 25 experts were selected to participate in the consultation in this study, from higher vocational colleges and enterprises. The survey respondents were selected to distribute questionnaires to 65 people from higher vocational colleges and universities as well as 15 people from enterprise staff. The questionnaire was based on the model scale of the evaluation index system finalised after two rounds of an expert consultation. A total of 80 questionnaires were distributed after the reliability test, 78 were recovered, and the validity rate of the recovered questionnaires was 100%. Subsequently, the weights of each index are determined through the method of this paper, and the framework and weights of the comprehensive evaluation index system for the integration of industry and education are shown in Table 1. According to the evaluation method of this paper, the indicator system framework was normalized using the normalization method. The weights of industry-teaching integration safeguard measures, industry-teaching integration resource allocation, industry-teaching integration process indicators and industry-teaching integration effect indicators are 8.058%, 9.194%, 30.573% and 52.175%, respectively. Among them, the effect of industry-teaching integration and the process of industry-teaching integration indicators have the highest weights, indicating that the effect and process of industry-teaching integration are the focuses of promoting the optimal development of industry-teaching integration and talent cultivation in innovation and entrepreneurship education and training.

Table 1.

The evaluation index system and weight of the production and teaching fusion

Primary indicator	Weighting(%)	Secondary indicator	Weighting(%)
Production and education integration guarantee measures (A)	8.058	A1	9.447
		A2	27.678
		A3	62.844
Production and teaching fusion resource allocation (B)	9.194	B1	38.163
		B2	12.132
		B3	13.771
		B4	10.152
		B5	25.782
Process index of production and teaching (C)	30.573	C1	22.368
		C2	35.587
		C3	42.045
The result of the fusion effect of the production(D)	52.175	D1	22.114
		D2	30.557
		D3	47.329

2.2.3

Evaluation results of industry-teaching integration in higher education innovation and entrepreneurship education

In order to further study and analyse the dilemma of industry-teaching integration of innovation and entrepreneurship education in higher vocational colleges and universities, this section collates the final questionnaire of this study on the basis of the evaluation index system in the previous section. Subsequently, a questionnaire survey was launched to the relevant personnel of 15 higher vocational colleges and universities and 8 enterprises in X city to analyse the dilemmas and difficulties facing the industry-teaching integration of higher vocational innovation and entrepreneurship education at present. In order to make the evaluation more convenient, this paper will also quantify the evaluation results by using quantitative scores, introducing the score vector C=[20,40,60,80,100] of the judgment set V, i.e., assigning scores (20, 40, 60, 80, 100) to each of the five levels of the evaluation (very dissatisfied, relatively dissatisfied, generally satisfied, relatively satisfied, and very satisfied), and then obtaining the final scores through calculation.

The evaluation results of innovation and entrepreneurship industry-teaching integration in higher vocational colleges and universities are shown in Table 2. At present, in the evaluation index system of industry-teaching integration, the index of the effect of industry-teaching integration has the highest score of 84.109, but the score of this index of industry-teaching integration resource allocation is only 56.754, which indicates that the index of the resource allocation of industry-teaching integration is not yet very satisfactory, and that the implementation of the policy of industry-teaching integration resource allocation has a large gap with the expected goal of most of the survey respondents. There is still a big gap between the implementation of the policy and the expected goal of the majority of survey respondents, and the resource allocation for the integration of industry and education still needs to be improved continuously.

Table 2.

The evaluation results of the innovation of entrepreneurship and entrepreneurship

Primary indicator	Score	Secondary indicator	Score
Production and education integration guarantee measures (A)	75.194	A1	95.055
		A2	27.249
		A3	92.518
Production and teaching fusion resource allocation (B)	56.754	B1	98.855
		B2	90.265
		B3	70.115
		B4	88.324
		B5	86.485
Process index of production and teaching (C)	72.032	C1	87.445
		C2	94.125
		C3	58.442
The result of the fusion effect of the production(D)	84.109	D1	88.245
		D2	82.674
		D3	87.596

3

Construction of innovation and entrepreneurship education training model based on the integration of industry and education

Through the above analysis of the dilemmas and problems in the development of industry-teaching integration of higher vocational innovation and entrepreneurship education, this paper proposes an industry-teaching integration optimisation talent cultivation model based on the digital talent cultivation mechanism for solving the resource allocation of industry-teaching integration, according to the digital talent cultivation needs of innovation industry entrepreneurship education.

3.1

Innovative Entrepreneurship Education Training Mechanisms and Resource Co-operation Pathways

The training mechanism for digital talent training in innovative industries is as follows: 1)

Goal-oriented mechanism of innovation and entrepreneurship

In the training of digital personnel in higher vocational innovation and entrepreneurship education, the core of the training should be to improve the innovation and entrepreneurship ability of students based on the development of digital capabilities, and the construction of a digital application personnel training system that is docked with the industrial chain. Continually promote the practice of students’ quality development projects, as well as the reform of quality development projects. To promote the effective connection between innovation and entrepreneurship education and professional education, the school is reforming innovation and entrepreneurship education. Combined with the actual production process, it establishes a brand-new innovation and entrepreneurship education system and reforms all kinds of management systems for students.

2)

Competence-based curriculum reconstruction mechanism

Aiming at students’ acquisition of innovation and entrepreneurship competence, the association between the curriculum and competence at each stage of talent cultivation is clarified. Aggregate and collate effective resources to design a general education curriculum system that has both private characteristics and is compatible with the competencies of digital applied talents.

3)

Co-construction Mechanism of Digital Applied Teachers’ Teams

In order to make the cultivation of digitally applied talents more in line with the actual demand, it is indispensable to build a team of teachers full of strength together with enterprises. The team not only needs professional teachers from the school, but also senior engineers and business managers to ensure that the training program for digital application talents can be fully implemented.

4)

Three-in-one Collaborative Parenting Mechanism

The characteristics of digitally applied talents are not only reflected in the knowledge and ability structure but also the quality of the quality should have good professional qualities and professionalism, such as mental health, sense of responsibility, physical conditions, ideological qualities and so on.

5)

Multi-dimensional guarantee mechanism for teaching quality

In order to ensure that teaching quality is guaranteed and recognised, the establishment of a teaching quality guarantee system consisting of teaching links, quality standards, decision-making management, information collection and analysis, and processing is the key. The quality standard, which covers the entire process of undergraduate education, from student admission and teaching management to graduate survey, includes the operation processes and standards of each functional department. Based on the decision-making body for daily quality issues tracking management, each quality issue needs to be formed to improve the closed loop and constantly improve the quality assurance system that embodies the PDCA cycle, forming a mechanism of self-discovery, self-correction, and self-improvement.

As shown in Figure 4, the cooperation path of school-enterprise co-operation is an effective exploration of the integration of industry and education, guided by relevant policies and the co-operation of multiple stakeholders.

Among them, schools and enterprises are the core stakeholders as key participants in the integration of industry and education. The government, as the top-level design, plays a macro-coordinating function by formulating policies and allocating funds, while industry organisations participate in the integration of industry and education through industry authorities, industry associations and other industry organisations, both of which have a certain influence on the integration of industry and education, and are secondary stakeholders. The public and the media are the driving forces for the integration of industry and education, and they are marginal stakeholders through their indirect influence on the integration of industry and education. In the process of industry-education integration, mobilising all parties to actively perform their duties, prompting the intermingling of all parties and mutual interaction, and realising the synergistic governance of stakeholders in industry-education integration is of great significance to the cultivation of applied talents in private colleges and universities.

3.2

Digital Talent Cultivation Model for Higher Education Innovation and Entrepreneurship Education

The digital talent cultivation model program designed in this paper for innovation and entrepreneurship education in higher vocational colleges aims to promote the comprehensive and lifelong development of students. Through the coordinated development of knowledge, ability and quality, comprehensive improvement is obtained, and the digital talent cultivation mode programme of higher vocational innovation and entrepreneurship education is shown in Figure 5. In the talent cultivation model of this paper, humanities education and other humanities education are carried through the whole process of talent cultivation, strengthening students’ professional competence, communication ability, independent innovation and other competence training so that students have the ability of lifelong learning and adapting to the ever-changing social development. At the same time, actively adapt to the market demand, digital applied talents training in addition to the laws of education, as well as to adapt to the needs of social development, and more importantly, to meet the requirements of local and regional economic development of talents. According to the changes in the market and demand, adjust the professional direction in a timely manner, pay great attention to the application of new technologies, new processes and other emerging things, and timely adjustment of talent training programmes, to better achieve the docking of talents and market demand. Secondly, to improve the employment of talented individuals, promote the reform of teaching content, adjust curriculum design, and clarify the core competencies required by relevant industries. Finally, optimize the integration of industry and education. The integration of industry and education is an important way to cultivate applied talents, seize the opportunity of pro-construction, optimize the allocation of resources, docking industrial universities, and introduce the participation of enterprises and industries in the process of talent cultivation to complete the development of cultivation programmes together.

4

Conclusion

Studying the digital talent cultivation mode of innovation and entrepreneurship education in higher vocational colleges from the perspective of industry-teaching integration holds significant theoretical value and practical significance for promoting the reform of the supply side of human resources, improving the quality of higher vocational education, and cultivating new kinetic energy for development.

Upon investigation and sorting, the relevant literature on the integration of industry and education reveals a general upward trend after 2017, reaching 47 articles in 2020, the year of publication. The top three secondary themes of integration of industry and education are, in order, talent development (855), digitalization (758), and integration of industry and education (3875). Research on the integration of industry and education in innovation and entrepreneurship is slowly integrating with digitalization. At the same time, the research on industry-education integration between two subjects, namely enterprises and schools, is also reflected in the sub-themes of school-enterprise cooperation (321 articles) and vocational colleges and universities (501 articles).

The weights of the evaluation index system of industry-teaching integration safeguard measures, industry-teaching integration resource allocation, industry-teaching integration process indexes, and industry-teaching integration effect indexes constructed in this paper are 8.058%, 9.194%, 30.573%, and 52.175%, respectively. In the evaluation results, the scores of the four indicators are 75.194, 56.754, 72.032, and 84.109 in order, among which the effect indicator of industry-teaching integration has the highest score, but the score of industry-teaching integration resource allocation is only 56.754, which indicates that there are many problems in the resource allocation of industry-teaching integration, and it needs to be constantly improved and improved.

Comprehensively, based on the above research, this paper constructs a digital talent cultivation model for higher vocational innovation and entrepreneurship education based on the education and training mechanism and resource cooperation, which focuses on improving students’ innovation and entrepreneurship ability and optimally solves problems in the integration of industry and education through the cooperation of resources, so as to promote the development of digital talent cultivation for innovation and entrepreneurship education.

Lingua:: Inglese

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Innovative Research on Digital Talent Cultivation Mode of Higher Vocational Innovation and Entrepreneurship Education Promoted by Industry-Teaching Integration

Tingting Kong

Lin Feng

Quan Guo

Pubblicato online: 05 feb 2025

Ricevuto: 15 set 2024

Accettato: 30 dic 2024

DOI: https://doi.org/10.2478/amns-2025-0044

Parole chiaveHierarchical analysis method, Fuzzy comprehensive evaluation, Evaluation index system, Industry-teaching integration, Talent cultivation mode

© 2025 Tingting Kong et al., published by Sciendo

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

Parole chiave
Hierarchical analysis method, Fuzzy comprehensive evaluation, Evaluation index system, Industry-teaching integration, Talent cultivation mode