Discussion on Innovative Teaching Methods for Industry-Education Integration in Colleges and Universities in the Context of Interactive Digital Media

With the deepening of the national reform and development of vocational education, the integration of industry and education, as an important educational strategy, is gradually becoming a key force in promoting the deep integration of higher education and industry [1]. As an innovative educational model, the integration of industry and education is of great significance for the cultivation of high-quality talents that meet the needs of society. Therefore, the development of industry-teaching integration in applied undergraduate colleges and universities is not only an inevitable choice for the development of higher vocational education, but also a general trend for the future reform of vocational education [2]. Starting from analyzing the value implication of industry-teaching integration in applied undergraduate colleges and universities, we further analyze the implementation dilemma of industry-teaching integration in applied colleges and universities in depth, and then point out its corresponding breakthrough strategy [3].

The integration of industry and education is mainly through the synergistic cooperation between local governments, universities and enterprises to promote the organic connection between local talent cultivation and local industries [4]. In recent years, the demand for applied talents by industrial enterprises has been increasing with the transformation and upgrading of the national industrial structure [5]. Science and technology is the first productive force, in the current economic transformation and rapid iteration of science and technology, the integration of industry and education pays more attention to scientific and technological innovation and results transformation, emphasizes the sound and diversified school-running system, and attaches importance to the integrated integration of education and industry, etc. [6].

Scholars have analyzed and discussed the effect of the production-teaching integration teaching mode based on the theoretical analysis method, and all of them have shown a positive attitude towards the production-teaching integration teaching mode. Literature [7] describes the production-teaching fusion approach in construction education, which promotes reform and innovation in the field of construction teaching and also enhances construction education. Literature [8] emphasized the importance of science-industry cooperation in higher education, proposing the establishment of a new mechanism for cooperation between research institutions and higher education to create inter-university laboratories to promote scientific and technological innovation and scientific and technological practice. Literature [9] suggests that the integration of science, education and industry is the basis for technological innovation, arguing that the failure to integrate planning and market mechanisms to develop education, science and technology and manufacturing production will have a huge impact on the Russian economy. Literature [10] deeply analyzes the basic problems of the integration of undergraduate colleges and universities and research synergy mechanism, and combined with the current practice of school-enterprise cooperation, to discover the applied undergraduate talent cultivation mode, which provides an important reference for the development and transformation of this local university. Literature [11] assessed the performance level of higher education industry-education integration and its sustainable development trend based on the perspective of industry-education coupling and coordination, and built an industry-education integration performance assessment framework for relevant analysis, and found that regional industry and higher education present alternating rising level trends, and the coupling strength of the two stops hindering the improvement of the performance of industry-education integration.

Digital information technology-enabled teaching classrooms facilitate classroom teacher-student interactions, and the study provides a detailed analysis of the effectiveness of interactive teaching and learning with digital technology, mainly from the students’ perspective. Literature [12] reveals that IT-enabled interactive teaching can enhance students’ motivation and creativity, and explores the effectiveness and logic of digital activities such as digital storytelling, podcasting, and YouTube conversation analytics in classroom practice. Literature [13] assesses and analyzes the practice and performance of educational technology introduced into the interactive classroom of medical education, pointing out that an educational technology-enabled interactive classroom is an effective aid to enhance student learning. Literature [14] empirically analyzed that the interactive textbook (iBook) stimulated students’ interest in learning, gathered students’ attention to the classroom, and significantly replaced the efficiency of classroom teaching, and concluded that this technology can be promoted as an optimized teaching strategy.

This paper discusses the key influencing factors of university industry-teaching integration innovative teaching, quantifies the judgment of the expert group through the fuzzy set-DEMATEL method, analyzes the centrality of the key influencing factors in university industry-teaching integration innovative teaching and the frequency and centrality of the influencing factors’ appearances to look for the key influencing factors of university industry-teaching integration innovative teaching. The fuzzy set qualitative comparative analysis method is used to analyze the combined influence of condition variables on the effect of university industry-teaching integration and innovative teaching from the perspective of the group state.Through the comprehensive consideration of the analysis results, a specific strategy for implementing the innovative teaching method of university industry-teaching integration is proposed.

2

Research on key influencing factors of university industry-teaching integration innovation

2.1

Fuzzy-DEMATEL approach

2.1.1

DEMATEL methodology

The DEMATELL method is employed to simplify the process of analyzing the system structure by filtering out the main factors. The advantage of this method lies in its ability to use expert experience to deal with complex social problems, by analyzing the relationship between the influencing factors, the primary and secondary relationships between the factors can be calculated based on the direct influence matrix.The specific steps of the DEMATEL method are as follows.

Step 1: Organize experts and scholars in related fields to score the degree of interaction between the factors in the system, the numerical size of the score indicates the degree of interaction between the factors, according to the scoring to generate the direct impact matrix A = [a_ij].

Step 2: Convert the direct influence matrix A into the standardized influence matrix D.

(1)

D = \frac{1}{\max_{1 \leq i \leq 14} \sum_{j = 1}^{14} a_{i j}} A

Step 3: Calculate the Integrated Impact Matrix T from the Standardized Impact Matrix D.

(2)

T = D {(1 - D)}^{- 1}

Step 4: Calculate the sum of the rows (r_i) and columns (c_j) of the T matrix. r_i denotes the value of the combined influence of the i factor on the other factors, called the influence degree (D). c_j denotes the sum of the combined influence of the j factor on all the other factors, called the influenced degree (R). When i = j, r_i + c_j denotes the degree of importance of the i factor in the system, called the centrality degree (D + R), which denotes the position of the element in the system and the magnitude of the role it plays. r_i–c_j is called the degree of cause (D–R). When r_i–c_j > 0, the factor i is the cause factor and when r_i–c_j < 0, the factor i is the effect factor.

(3)

r_{i} = \sum_{j = 1}^{14} t_{i j}

(4)

c_{j} = \sum_{i = 1}^{14} t_{i j}

2.1.2

Expert language defuzzification based on fuzzy set theory

The article uses the triangular fuzzy number to quantify the judgment of the expert group according to the fuzzy set theory [15], and converts the experts' determination of the inter-influence relationship between the 14 influencing factors using linguistic variables into the triangular fuzzy number $z_{i j}^{k} (l_{i j}, m_{i j}, r_{i j})$ , where k = 1,2,3,…,K;i,j = 1,2,3,…,K, denotes the degree to which the krd expert believes that factor i influences factor j. The conversion criteria between linguistic variables and triangular fuzzy numbers are shown in Table 1.

Table 1.

The relationship between language variables and fuzzy Numbers

Linguistic Variable	The relative value of the three elements
No Influence	(0,0.1,0.3)
Very Low Influence	(0.1,0.3,0.5)
Low influence	(0.3,0.5,0.7)
High Influence	(0.5,0.7,0.9)
Very High Influence	(0.7,0.9,1.0)

The method of transforming fuzzy numbers into accurate values is used, assuming $z_{i j}^{k} = (l_{i j}, m_{i j}, r_{i j})$ , where 1 ≤ k ≤ K, the triangular fuzzy number de-fuzzying steps are as follows.

Step 1: Normalize the triangular fuzzy number of each expert.

(5)

x l_{i j}^{k} = \frac{l_{i j}^{k} -_{1 \leq k \leq K}^{m i n} l_{i j}^{k}}{Δ_{m i n}^{m a x}}

(6)

x m_{i j}^{k} = \frac{m_{i j}^{k} -_{1 \leq k \leq K}^{m i n} l_{i j}^{k}}{Δ_{m i n}^{m a x}}

(7)

x r_{i j}^{k} = \frac{r_{i j}^{k} -_{1 \leq k \leq K}^{m i n} l_{i j}^{k}}{Δ_{m i n}^{m a x}}

Step 2: Calculate the left and right values of the normalized fuzzy numbers $x l s_{i j}^{k}$ and $x r s_{i j}^{k}$ .

(8)

x l s_{i j}^{k} = \frac{x m_{i j}^{k}}{1 + x m_{i j}^{k} - x l_{i j}^{k}}

(9)

x r s_{i j}^{k} = \frac{x r_{i j}^{k}}{1 + x r_{i j}^{k} - x m_{i j}^{k}}

Step 3: Calculate the total standardized value.

(10)

x_{i j}^{k} = \frac{x l s_{i j}^{k} (1 - x l s_{i j}^{k}) + x r s_{i j}^{k} x r s_{i j}^{k}}{1 - x l s_{i j}^{k} + x r s_{i j}^{k}}

Step 4: Calculate the impact value of Factor i on Factor j for the kst expert evaluation.

(11)

w_{i j}^{k} = \min_{1 \leq k \leq K}^{k} l_{i j}^{k} + x_{i j}^{k} Δ_{\min}^{\max}

Step 5: Calculate the impact value of Factor i on Factor j quantified by K expert's comprehensive evaluation.

(12)

w_{i j} = \frac{1}{k} \sum_{k = 1}^{K} w_{i j}^{k}

2.2

Analysis of Key Influencing Factors of University Industry-Education Integration Innovation

The relevant literature was analyzed and the key influencing factors of university industry-education integration were extracted. The specific indicators of the influencing factors are shown in Table 2, and a group of experts was invited to score between the influencing factors, and the fuzzy set-DEMATEL method was used to analyze the degree of influence of the influencing factors in the process of university-industry-education integration. In order to ensure the scientificity of the study and the fairness of expert scoring, this paper invites an expert group consisting of 10 experts, including experts in industry-university research, experts in university research, and experts in enterprise technological innovation research, to score between the influencing factors. This study designs the questionnaire based on the linguistic variables used by the expert group. The questionnaire adopts likert 5-level scale to judge the mutual influence relationship between the 14 influencing factors, and each expert is invited to judge the degree of influence of each influencing factor freely according to the linguistic variable, so as to get the data of the degree of influence of different experts on the influence of each influencing factor.

Table 2.

Factors for the fusion of production

Primary dimension	Secondary dimension
Entrepreneurship will cultivate the dimension	Top college vision(A1)
	University policy positioning(A2)
	High-level entrepreneurship(A3)
Explore dimensions of entrepreneurial practice	Funding pressure(A4)
	Earnings expectations(A5)
	Entrepreneurship(A6)
	Construction of entrepreneurship system(A7)
	Business operation(A8)
Entrepreneurship ability ascension dimensions	Business endowment formation(A9)
	Business culture(A10)
	Business value presentation(A11)
Create dimensions of entrepreneurial environment	Social transformation(A12)
	Entrepreneurial policy incentives(A13)
	Technical market demand(A14)

In order to avoid the influence of experts due to subjective differences, the ratings given by the experts are fuzzy according to the fuzzy set method, so as to calculate the direct influence matrix of the factors influencing the integration of industry and education in colleges and universities, and the direct influence matrix A is converted into the standard influence matrix B through Eq. $B = \frac{A}{\max \sum_{i = 1}^{n} a_{i j}}$ . According to Eq. M = B(I – B)⁻¹, the standardized direct influence matrix is used to derive the comprehensive influence matrix M. The direct influence matrix A is converted into the standardized influence matrix B through Eq. 2. The MATLAB software was used to calculate the integrated impact matrix and the results of the integrated impact matrix are shown in Table 3.

Table 3.

The integrated matrix of the influence of the production and education

Factor	A1	A2	A3	A4	A5	A6	A7	A8	A9	A10	A11	A12	A13	A14
A1	0.094	0.091	0.170	0.121	0.154	0.205	0.158	0.210	0.133	0.156	0.148	0.057	0.054	0.062
A2	0.226	0.111	0.286	0.239	0.270	0.297	0.252	0.316	0.218	0.222	0.270	0.08	0.101	0.102
A3	0.232	0.138	0.187	0.203	0.265	0.309	0.254	0.327	0.246	0.258	0.275	0.086	0.080	0.080
A4	0.173	0.116	0.260	0.146	0.255	0.279	0.236	0.291	0.194	0.216	0.227	0.068	0.079	0.067
A5	0.215	0.145	0.275	0.232	0.191	0.312	0.248	0.322	0.230	0.254	0.249	0.086	0.081	0.080
A6	0.115	0.083	0.164	0.120	0.158	0.131	0.152	0.221	0.147	0.128	0.147	0.050	0.047	0.060
A7	0.155	0.113	0.200	0.212	0.228	0.240	0.132	0.268	0.198	0.173	0.196	0.064	0.062	0.066
A8	0.187	0.167	0.251	0.208	0.274	0.294	0.193	0.226	0.260	0.259	0.27	0.081	0.087	0.088
A9	0.162	0.098	0.186	0.203	0.215	0.229	0.160	0.239	0.137	0.230	0.211	0.058	0.066	0.057
A10	0.143	0.112	0.223	0.210	0.239	0.257	0.169	0.281	0.233	0.153	0.253	0.064	0.066	0.068
A11	0.172	0.132	0.243	0.228	0.259	0.276	0.193	0.299	0.236	0.229	0.180	0.089	0.082	0.082
A12	0.199	0.166	0.216	0.143	0.175	0.204	0.166	0.224	0.143	0.153	0.167	0.048	0.104	0.077
A13	0.186	0.189	0.311	0.268	0.296	0.321	0.250	0.346	0.252	0.259	0.300	0.121	0.073	0.134
A14	0.247	0.220	0.317	0.256	0.315	0.338	0.264	0.364	0.277	0.283	0.311	0.138	0.145	0.079

Based on the comprehensive impact matrix of university industry-teaching integration influencing factors to calculate the degree of influence, the degree of being influenced between each influencing factor, so as to calculate the center degree D (the sum of the degree of influence and the degree of being influenced) and the cause degree R (the difference between the degree of influence and the degree of being influenced) between the 14 influencing factors, and ranked, to get the ranking of the 14 influencing factors, and the results are shown in Table 4.

Table 4.

Indexes and rankings

Factor	Influence degree	Rank	Influence degree	Rank	Center degree	Rank	Reason	Rank
A1	1.813	13	2.506	10	4.319	13	-0.693	12
A2	2.99	3	1.881	11	4.871	10	1.109	3
A3	2.94	4	3.289	3	6.229	2	-0.349	6
A4	2.607	8	2.789	9	5.396	6	-0.182	5
A5	2.92	5	3.294	4	6.214	3	-0.374	7
A6	1.723	14	3.692	2	5.415	7	-1.969	14
A7	2.307	10	2.827	8	5.134	9	-0.52	10
A8	2.845	6	3.934	1	6.779	1	-1.089	13
A9	2.251	11	2.904	7	5.155	8	-0.653	11
A10	2.471	9	2.973	6	5.444	5	-0.502	8
A11	2.7	7	3.204	5	5.904	4	-0.504	9
A12	2.185	12	1.090	14	3.275	14	1.095	4
A13	3.306	2	1.127	12	4.433	12	2.179	2
A14	3.554	1	1.102	13	4.656	11	2.452	1

According to the data in the index and ranking table of each influencing factor, with the center degree as the horizontal coordinate axis and the cause degree as the vertical coordinate axis, the causal relationship of the influencing factors is shown in Figure 1, which is more intuitive to reflect the intensity of the role between the influencing factors.

Through the fuzzy set-DEMATEL analysis of the influencing factors of university industry-teaching integration, we draw the following insights:

1) Center degree analysis. The center degree indicates the location of the factor in the system and the size of the role played, the larger the center degree, the greater the role played by the factor in the system of factors influencing the integration of university industry and education, the greater the influence. The centrality degree of entrepreneurial operation practical work (A8), high-level entrepreneurial attitude (A3), entrepreneurial benefit expectation (A5), and business value manifestation (A11) is high. Among them, the centrality of entrepreneurial operation is the largest, which is a more important influence factor, and the high-level entrepreneurial attitude, entrepreneurial gain expectation and business value manifestation are important factors in the integration of industry and education in colleges and universities.

2) Reason degree analysis. Cause degree is greater than 0, it means that the factor in the knowledge discovery system interaction quality influence factor system is more able to actively influence other factors, called the cause factor. On the contrary, a cause degree of less than 0 indicates that the factor is more susceptible to the influence of other factors, which is called the result factor. Cause factors, result factors reflect the influence characteristics of factors. The causal factors of University-Industry-Education Integration are technology market demand (A14), entrepreneurship policy incentives (A13), university policy orientation (A2), and social conceptual change (A12), and the causal degrees of technology market demand and entrepreneurship policy incentives are above 2, which have a strong influence on other factors. All other factors have a cause degree of less than 0, which is a result factor, while the cause degree of entrepreneurial atmosphere creation and entrepreneurial operation is less than -1, which indicates that they are strongly influenced by other factors. Technology market demand and government policy are objective factors in university-industry-industry fusion, which are not subject to the university’s will and can have an impact on other factors. On the other hand, entrepreneurial atmosphere and entrepreneurial operation are the influencing factors at the level of strategic objectives of universities, which are susceptible to the influence of the objective environment, which is in line with the actual situation.

Through the word frequency statistical analysis and fuzzy set-DEMATEL analysis of each influential factor in the integration of university education and industry, we get the frequency of each influential factor and its centrality in the integration of university education and industry, therefore, we use the frequency as the horizontal coordinate and the centrality as the vertical coordinate to draw the frequency-centrality coordinate graph of influential factors, and the frequency-centrality coordinate of the influential factors is shown in Figure 2.

Based on the quadrant where the influencing factors are located, we can classify the influencing factors into three categories, i.e., strong influencing factors (frequency > 9%, centrality > 5), medium influencing factors (frequency < 9%, centrality > 5) and weak influencing factors (frequency < 9%, centrality < 5).

Strong influencing factors: high-level entrepreneurial attitude (A3), entrepreneurial atmosphere creation (A6). These two factors can be considered as the key influencing factors in the integration of university industry and education, which play a decisive role in the success of university industry and education integration.

Medium-influencing factors: entrepreneurial operation practice (A8), entrepreneurial revenue expectation (A5), business value manifestation (A11), business ability cultivation (A10), school financing pressure (A4), business endowment formation (A9) and entrepreneurial system construction (A7). These seven factors can be regarded as important influencing factors in the integration of university industry and education, which are indispensable factors in the process of integration of university industry and education.

Weak influencing factors: university policy orientation (A2), technology market demand (A14), entrepreneurship policy incentives (A13), vision of first-class university (A1), and social concept change (A12). These five factors can be the most important influencing factors in the integration of the university industry and education, and play a facilitating role in the integration of the university industry and education.

3

Path analysis of innovative teaching in industry-education integration

3.1

Qualitative comparative analysis of fuzzy sets

The qualitative comparative analysis method utilizes Boolean algebra to analyze all the different cases and ultimately identify the various combinations of antecedent conditions that lead to the occurrence of the outcome variable. Based on different set characteristics, it can be further divided into clear set qualitative comparative analysis (csQCA) [16], multi-valued qualitative comparative analysis (mvQCA), and fuzzy set qualitative comparative analysis (fsQCA) [17]. The biggest difference between these three analysis methods is that they do not deal with the same data, clear set as the name suggests can only deal with dichotomous variables, which is the simplest one. And the data in this paper and most of the studies are continuous, which requires the use of fuzzy set qualitative comparative analysis, the following is the specific operation process of qualitative comparative analysis based on fuzzy set:

1) Calibration of continuous data, the purpose of this method is to transform continuous variables into set data. The number in the set of [0, 1] is used to indicate the degree to which the condition belongs to a certain set, with 0 representing no affiliation at all, 0.5 representing cross-affiliation, and 1 representing full affiliation, and the closer to 1 means the higher the affiliation.

2) Generate the truth table based on the affiliation scores. Qualitative comparative analysis is based on set theory to establish the relationship between subsets and sets, and analyze the effect of combinations of condition variables on the outcome variables, so it is necessary to find out the antecedent condition combinations that lead to the outcome variables, that is, the truth table. The specific operation process will output the truth table that contains all the groupings, and then it is necessary to set the consistency threshold and the frequency of cases according to your needs, and filter out the antecedent condition combinations that can explain the outcome variables. Consistency has a value range of 0-1 and is considered an acceptable value when it is greater than or equal to 0.75. Consistency closer to 1 means that X belongs more to the set of Y. Coverage also takes a value in the range of 0-1, and the closer it is to 1 means that the greater the probability that X belongs to Y. In this paper, 0.75 is selected as the consistency threshold and 1 as the frequency threshold to filter the sample conditions.

3) Sufficiency analysis: The meanings of several special symbols of QCA are as follows: * means and, + means “or”, and ~ means “not”. If X is contained in Y, then X is a sufficient condition for Y, and if X is contained in Y, then X is a necessary condition for Y. How to determine whether a combination consisting of antecedent conditions is a sufficient condition for the outcome variable and to what extent the combination explains the outcome variable can be explained by judging consistency and coverage. Consistency and coverage are calculated as shown below: (13) $C o n s i s t e n c y (X \leq Y) = \sum \min (x_{i}, y_{i}) / \sum x_{i}$ (14) $C o v e r a g e (X \leq Y) = \sum \min (x_{i}, y_{i}) / \sum y_{i}$

4) Simplification of the obtained solutions. There are three types of solutions that will be output by QCA analysis, which are parsimonious solution, complex solution and intermediate solution. Since the antecedent conditions of the parsimonious solution are stable and are not affected by the operator’s setting of the antecedent conditions, while the intermediate solution is generally affected by the setting of the antecedent conditions. Therefore the final solution is obtained by combining the parsimonious and intermediate solutions, and if the variable occurs in both the intermediate and parsimonious solutions, the variable becomes the core condition, and if it only occurs in the intermediate solution, it is called the auxiliary condition.

3.2

Necessity analysis of a single conditional variable

Conditional variables with a consistency of higher than 0.9 were used as necessary conditions to directly explain the results. Table 5 shows the results of the necessity analysis of individual conditions, the data were calculated by fsQCA3.0 software.The consistency scores of the five conditional variables are not more than 0.9, none of them can be a necessary condition, and none of them can be a sufficient explanation for the achievement of high performance of the integration of education and industry programs. Therefore, it is necessary to combine the conditional variables and then analyze them to find the combination of conditional variables that can reasonably explain the high performance of the industry-teaching integration project, so as to explain how the synergistic elements of industry-teaching integration can be combined to achieve good synergistic effects under different realistic situations.

Table 5.

The necessity analysis of individual conditions

Result variable		The production of the fusion project has achieved high performance
Conditional variables (“not” for logical operations)		Consistency	Coverage
Enterprise size	Size	0.62414	0.60323
Enterprise size	~Size	0.37575	0.59263
Subject type	Discipline	0.53368	0.57107
Subject type	~Discipline	0.46634	0.63545
Project driver	Impetus	0.76606	0.89189
Project driver	~Impetus	0.52205	0.64438
Project action	Measure	0.77131	0.87775
Project action	~Measure	0.52001	0.65825
Item constraint	Restraint	0.79602	0.87487
Item constraint	~Restraint	0.53303	0.70224

3.3

Sufficiency analysis of combinations of conditional variables

Considering the constraints of the questionnaire method used in this paper, which may yield low-quality incidental results due to random completion of the questionnaire by the questionnaire fillers, and the constraints that at least 75% of the total number of cases should be retained after the screening of the case frequency thresholds, this study set the case frequency threshold at 2 and the consistency threshold at 0.8.Based on this, the calibrated fuzzy set affiliation score matrix (i.e., the original truth table) can be converted into a threshold-screened truth table.

The results of the group analysis are shown in Table 6, which shows that there are five effective paths for achieving high performance in the integration of industry and education programs, and the combination of conditions corresponding to these five paths is a sufficient condition for achieving high performance in the integration of industry and education programs. The consistency of each path is greater than the empirical threshold value of 0.85, and the total consistency value is as high as 0.913259, which indicates that the empirical analysis of this study has high validity. In addition, the total coverage value is 0.798051, which means that the overall coverage is nearly 80% of the samples, which is a high degree of coverage. Comparing the coverage indexes of the above five paths, the original coverage of path 3 (0.405123) is the highest, explaining more than 40% of the resultant variables, i.e., more than 40% of the industry-education fusion projects have obtained high project effectiveness through this path, which indicates to a certain extent that the industrial parties of the current industry-education fusion projects are still dominated by large enterprises. The original coverage of paths 1, 2, 4, and 5 is 28%, 27%, 11%, and 20%, respectively, indicating the diversity of paths through which industry-teaching integration projects achieve high performance.

Table 6.

Configuration analysis results

Conditional variable	Configuration solution
Conditional variable	Path 1	Path 2	Path 3	Path 4	Path 5
Enterprise size	_¢	©	©	¢	_©
Subject type		_©		¢	_¢
Project driver			_©	_¢	_©
Project action	©				©
Item constraint	_©	©	©	_©
Original coverage	0.280365	0.268541	0.405123	0.111258	0.201543
Unique coverage	0.190365	0.052364	0.025712	0.022365	0.032542
consistency	0.954036	0.903254	0.941985	0.952356	0.905841
Total consistency	0.913259
Total coverage	0.798051

© = core condition present, ¢ = core condition missing. _©=Auxiliary conditions, where _© indicates that auxiliary conditions are present and _¢=Auxiliary conditions are missing. _¢=Edge condition is missing. “Blank” indicates that the condition is irrelevant in the configuration, i.e. its presence or absence does not affect the results.

After the study, a total of five paths to improve the effectiveness of talent cultivation in industry-teaching integration projects were identified, which are as follows: path 1 (~Size*Measure*Restraint) is manifested in the fact that when small enterprises (auxiliary conditions are missing) and different disciplines carry out the industry-teaching integration projects, with the project actions perfected (the core conditions are present) and the project constraints are in place (the auxiliary conditions are present), the industry-teaching integration project achieves high performance. The consistency of this pathway is about 0.95, indicating that when small enterprises cooperate with various disciplines, focusing on the initiatives in the process of project implementation, such as goal setting and information communication in the early stage and talent cultivation in the middle stage, as well as corresponding constraints, the effectiveness of the project can be guaranteed.

Path 2 (Size*Discipline*Restraint) shows that when a large enterprise (core condition exists) and an applied discipline (auxiliary condition exists) carry out an industry-industry fusion project, project constraints are in place (core condition exists) and the industry-industry fusion project achieves high performance. The consistency of this path is about 0.90, indicating that when large enterprises and applied disciplines cooperate, the project can achieve high performance because the purpose is relatively clear, i.e., joint research and development and formation of technological results, and both parties are likely to have previous experience in university-enterprise cooperation, with a focus on good supervision and constraints.

Path 3 (Size*Impetus*Restraint) shows that when large enterprises (core conditions exist) and different disciplines carry out industry-teaching integration projects, the project motivation is sufficient (auxiliary conditions exist) and the project constraints are in place (core conditions exist), and the industry-teaching integration project achieves high performance. The original and unique coverage of this pathway is 0.405123 and 0.025712, respectively, indicating that it explains about 40% of the cases in which the integration of industry and education projects achieve high performance, of which about 3% of the cases can be explained by this pathway only. The consistency of this path is about 0.94, suggesting that when large enterprises cooperate with various types of disciplines, with sufficient intrinsic motivation and additional extrinsic incentives, focusing on securing constraints can also contribute to the high performance of industry-education integration programs.

Path 4 (~Size*~Discipline*~Impetus*Restraint) shows that when small enterprises (core conditions missing) and basic disciplines (core conditions missing) carry out industry-teaching fusion projects, the project motivation is general (auxiliary conditions missing) but the project constraints are sufficient (auxiliary conditions present), and the industry-teaching fusion projects achieve high performance. The consistency of this path is about 0.95, indicating that when small businesses work with basic disciplines, with strong monitoring and constraining mechanisms in place, the projects can achieve good performance despite the average level of motivation for both parties to participate in the project.

Path 5 (Size*~Discipline*Impetus*Measure) shows that when large enterprises (auxiliary conditions exist) and basic disciplines (auxiliary conditions are missing) carry out industry-teaching integration projects, the industry-teaching integration project achieves high performance when the motivation of the project is sufficient (auxiliary conditions exist) and the project action is perfect (core conditions exist). The consistency of this pathway is about 0.91, indicating that when large enterprises and basic disciplines carry out industry-teaching integration projects with sufficient motivating factors, focusing on perfecting various measures during the project to ensure that the whole process of talent cultivation involves both industry and education, the projects can achieve high performance.

4

Research on Innovative Strategies of Teaching Reform for Industry-Education Integration

4.1

Enhancing the fit between educational philosophy and talent training

Under the background of interactive digital media, if we want to cultivate qualified talents, it is inevitable to integrate education and practical education deeply and effectively, and to consolidate the foundation of cultivating high-quality composite talents. Based on this, it is necessary to do the following two things: first, optimize and innovate the teaching curriculum system.Specifically, the teaching stage of professional courses should be clear, with the structure of its development requirements and appropriate arrangements.

For example, in the first stage, basic knowledge and professional knowledge can be the main teaching content. In the second stage of professional knowledge as the main teaching content, with an appropriate amount of practical teaching as auxiliary teaching content, the advantage of doing so is that in the consolidation of theoretical knowledge at the same time, can be exposed to the production of practical courses, and gradually stimulate the interest of students for practical courses, and gradually into the enterprise to carry out the production of practical training.

In the third stage, the practical teaching course is the main focus, and students can fully learn how to guide the practice of theory in practical learning, and experience how to integrate theory and practice organically in practice. Secondly, enhance the innovation of practical production training courses. On the basis of cooperation between the school and relevant enterprises, the practical teaching content is more in-depth innovation, so that students can participate in the real projects of enterprises, experience the real production environment, exercise their application ability, and find corresponding responses to their own shortcomings.

4.2

Improve the sharing of resources for practical teaching and learning

The teaching mode of industry-teaching integration breaks the traditional single teaching mode, which is of great help to the improvement of students’ application ability, and on this basis, the full sharing of practical teaching resources will have an obvious promotion effect on this help. The sharing of practical teaching resources involves three aspects: first, making full use of the Internet and other information technology to construct and innovate practical teaching courses.For example, the interactive convenience of the Internet can be utilized to maximize the sharing of a series of excellent courses to strengthen the efficiency of teachers in carrying out practical teaching.Second, it is necessary to increase the number of related academic activities to facilitate the innovation of practical teaching. Thirdly, students are encouraged to actively participate in various industry-related competitions and other activities. In the process of the activities, not only can the students’ application ability to test, but also promote teachers to pay more attention to the cultivation of students’ application ability, and constantly improve their own teaching level, in order to strengthen the efficiency and quality of the training of high-quality applied talents.

4.3

Strengthening the practicality of practical teaching

Students are required to have a high level of practical ability, so the teaching process also has a high demand for practical teaching venues and related curriculum. That is to say, the practical places and related courses support the teachers in carrying out practical teaching, but also meet the students’ practical requirements. Therefore, it is necessary to strengthen the practicality of practical teaching. First of all, it is necessary to improve the openness of the practical training base jointly created by the school and the enterprise, change the old mode of use, and carry out reasonable planning for the practice site to further improve its utilization efficiency. Students with practical needs can make appointments to use the practice site according to their needs, which can have a more positive impact on students’ ability to enhance their own application in a targeted manner.Secondly, the innovative mode of school-enterprise cooperation will deepen the content of school-enterprise cooperation and strengthen practical relief in the teaching process.Schools and enterprises in the establishment of training bases and long-term cooperation on the basis of allowing students to come into contact with more real production projects, personally in the enterprise production positions for internship.

4.4

Exploration of new systems and models

The exploration and innovation of the new system and model should start from the following three aspects: first, optimizing the top-level design, taking the integration of industry and education as a guide, and focusing on the cultivation of application-oriented talents as the goal. This also means that the basic goal should be to improve the application ability of students, in order to achieve this goal it is necessary to optimize the training program, diversify the faculty structure, pay attention to the strategy of the integration of industry and education, and strengthen the dual-creative mindset of students, so as to ultimately improve the comprehensive ability of students to meet the employment needs of the industry. Secondly, when exploring and constructing the new curriculum system, it should be oriented towards improving abilities and making reasonable adjustments and innovations to the curriculum structure. When exploring and innovating, we should highlight the enhancement of students’ own innovation ability, and after having an objective knowledge of the development trend of the industry, we should strengthen the corresponding technical courses according to the employing demand of the enterprises, emphasize the correlation between the professional skills courses and the practical courses, and help the students to expand their thinking and improve their application ability. Thirdly, projects and competitions are used to promote the integration of industry and teaching strategies. While cooperating with enterprises, the school makes full use of the resources of enterprises in the industry, fully connects the real projects of enterprises with classroom teaching, and further strengthens the application ability of students through enterprise projects and competitions.

5

Conclusion

In this study, the fuzzy set-DEMATEL method and the fuzzy set qualitative comparative analysis method are used to explore the key influencing factors of the innovative teaching of industry-teaching integration and the optimal path to improve the efficiency of the innovative teaching of industry-teaching integration, respectively, in the context of interactive digital media.

1) The centrality and causality of the 14 influencing factors obtained by using the fuzzy set-DEMATEL method are ranked, among which the centrality of entrepreneurial operation practical work is the largest, and it is most closely connected with other influencing factors. The most active influencing factor is the entrepreneurial attitude of top-level leaders.The demand factor for technology markets has the largest degree of cause, which indicates that it contributes the most to other factors. Considering the frequency and centrality, two strong influencing factors are obtained, which are high-level entrepreneurial attitude and entrepreneurial atmosphere creation, and the above two factors are the key influencing factors of the innovative teaching of industry-teaching integration, which play a decisive role in the success of the innovative teaching of industry-teaching integration in colleges and universities.

2) Using the fuzzy set qualitative comparative analysis method, it was found that none of the five conditional variables could individually provide a sufficient explanation for the high performance achieved by the industry-teaching integration program. However, their groupings were able to adequately explain the achievement of high performance in the innovative teaching program integrating industry-teaching.The method identified five effective paths to achieve high performance in innovative teaching programs for industry-teaching integration. Among them, the third path (grouping of big business, project motivation and project constraints) has the highest original coverage of 0.405123. It is the optimal path to improve the efficiency of innovative teaching and learning through the integration of industry and education. It provides a realistic reference for the realization of innovative teaching of industry-teaching integration.

3) Based on the above work, this paper proposes a specific strategy for the innovation of teaching mode under the concept of integration of industry and education. The ultimate goal of teaching reform and innovation under the integration of industry and education is accomplished from the aspects of improving the fit between education concept and talent cultivation, improving the sharing of practical teaching resources, strengthening the practicality of practical teaching, and further exploring the new system.

Język:: Angielski

Częstotliwość wydawania:: 1 razy w roku
Dziedziny czasopisma:: Nauki biologiczne, Nauki biologiczne, inne, Matematyka, Matematyka stosowana, Matematyka ogólna, Fizyka, Fizyka, inne

Kanał RSS czasopisma

Discussion on Innovative Teaching Methods for Industry-Education Integration in Colleges and Universities in the Context of Interactive Digital Media

Liang Wu

Jing Wang

Xuefei Li

Yuxiao Zhang

Data publikacji: 21 mar 2025

Otrzymano: 23 paź 2024

Przyjęty: 30 sty 2025

DOI: https://doi.org/10.2478/amns-2025-0619

Słowa kluczoweIndustry-teaching integration, Key influencing factors, Fuzzy set-DEMATEL method, Fuzzy set qualitative comparative analysis

© 2025 Liang Wu et al., published by Sciendo

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

Słowa kluczowe
Industry-teaching integration, Key influencing factors, Fuzzy set-DEMATEL method, Fuzzy set qualitative comparative analysis