Research on the construction of the intrinsic mechanism path of collaborative cultivation in Civic and Political Education, Innovation and Entrepreneurship Education based on Machine Learning Models

In recent years, the state has issued a series of policy documents, emphasizing that innovation and entrepreneurship education is carried out throughout the whole process of talent cultivation, and at the same time, clearly requiring the strengthening and improvement of ideological and political work in colleges and universities, which provides a policy basis and directional guidance for the integration of the two [1]. The integration of ideological and political education and innovation and entrepreneurship education can not only help students establish a correct worldview, outlook on life and values, stimulate their patriotic sentiment and sense of social responsibility, but also enhance their adaptability and problem-solving ability in the face of complex environments, laying a solid foundation for their future employment or entrepreneurship [2]. At the same time, this also helps to build a more open and inclusive campus cultural atmosphere, promote the organic combination of academic research and social practice, and deliver more outstanding talents with innovative spirit and practical ability to the society [3].

College students’ innovation and entrepreneurship education must follow the trend of the times, effectively develop a scientific and reasonable innovation and entrepreneurship education basic curriculum, provide comprehensive and systematic innovation and entrepreneurship education and guidance to students, so that they can clearly define their learning and employment goals, take the initiative to learn and consolidate their professional abilities, and prepare for innovation and entrepreneurship [4]. Civic and political education as an important part of university education, special systematic ideological and political education for college students, so that they establish a correct view of the rule of law and professional ethics, to improve the success rate of college students’ innovation and entrepreneurship to add help. Therefore, attention should be paid to finding the fit between the ideological and political education and the “Foundation of Innovation and Entrepreneurship for College Students” course, and exploring the practical path of integrating the ideological and political education into the “Foundation of Innovation and Entrepreneurship for College Students” course, so as to promote the strategic development of China’s college students’ education, and to help college students’ comprehensive quality cultivation and improvement. In order to promote the strategic development of China’s college students’ education and help them cultivate and improve their comprehensive quality [5–6].

Innovation and entrepreneurship education has a positive effect on students’ entrepreneurial awareness, and many scholars have carried out a large number of studies on the design and reform and innovation of entrepreneurship education methods, and the factors affecting students’ entrepreneurial awareness. Yang, Y used a questionnaire survey method to examine the current situation of the development of innovation and entrepreneurship education in colleges and universities and the performance of the practice of creativity education, and pointed out that the factors affecting the entrepreneurial mindset of arts and sciences students differed, and that the family economy significantly affected the students of arts and sciences, whereas the practical activities of sports had a greater impact on the entrepreneurial mindset of sciences students [7]. Lv, M et al. examined and analyzed the performance of entrepreneurship education in Chinese engineering universities, and proposed the opinion of deepening the combination of industry and university to promote innovation and entrepreneurship of college students while balancing the imbalance of employment and job supply and demand, and also proposed to reform from the dimensions of the curriculum system, talent cultivation mode, etc., in order to adapt to the development of the current socio-economic development, and finally envisioned a framework that contains specific performance indicators such as patent filing, thesis publication, and organizational arrangements, satisfaction, and other non-specific performance indicators to support the assessment of innovation and entrepreneurship education performance [8]. Wang, J et al. discussed that the reform of innovation and entrepreneurship education should be in line with the new engineering education concept and the new structure of disciplines and specialties and be implemented into the education concept, curriculum system and teaching methods, etc., and the research has made a positive contribution to the cultivation of innovative talents in the society and the country [9]. Johannisson, B et al. provide an in-depth analysis of entrepreneurial kernels and concepts and a comprehensive analysis of practical examples of academic training in entrepreneurship, which provides an important reference for the orientation and operationalization of student entrepreneurship [10]. Lv, Y et al. constructed a conceptual analytical model to understand the impact of entrepreneurship on economic and social development, and also conducted a related study based on a hypothetical analytical framework of the relationship between entrepreneurial competence and entrepreneurial intention in entrepreneurship education, stating that entrepreneurship teaching, entrepreneurship competitions, and entrepreneurship practice all promote entrepreneurial intention [11].

Civic education is of great significance to the establishment of the correct three views of students and the cultivation of the spirit of self-improvement and struggle, so it is very necessary to carry out research on how to improve the effectiveness of Civic teaching and analyze the kernel of Civic education in depth. Chen, C et al. attempted to explore the path of integrating moral and aesthetic education based on the Great Civics model, discussed the historical conditions, rationality of the combination of the two, and acquired an effective way to integrate aesthetic education into Civics education [12]. Fang, D et al. emphasized the moral role model played by teachers in the process of building Civics and the leadership role they play in teaching Civics to impart Civics knowledge to students in a subtle way [13]. Zhang, H et al. elucidated the significance of Civic Education, and combined the literature method and interview method to reveal the current situation of Civic Education practice in colleges and universities and the existing problems, and finally put forward the solution suggestions in a targeted manner [14]. Asif, T et al. used mixed methods to collect data related to the practices and beliefs of Civic Teachers’ Education in colleges and universities and analyzed the data by combining the interaction model and ANOVA strategy, the results of the study showed that Pakistani teachers are more conservative in their thinking and believe that the ultimate goal of moral education objectives lies in the sense of sovereignty, constitutional loyalty, and sense of social service, whereas Chinese Civic Education emphasizes on the collectivism, family and social values [15].

This paper takes the theory and relationship between innovation and entrepreneurship education and ideological and political education as the theoretical basis, and proposes an evaluation model of DA-BP combined with AHP in order to improve the precision of teaching evaluation. The teaching evaluation indexes for innovation, entrepreneurship, and ideological and political synergy education are constructed using the DA-BP neural network model, which produces 2 target layers, 6 criterion layers, and 27 indicator layers. These evaluation indicators are inputted into AHP, and the indicators are assigned weights. The output is the evaluation results of innovative entrepreneurship and ideological and political collaborative education teaching. It provides reliable technical means and innovative ideas for exploring the intrinsic mechanism of collaborative education.

2

Research on the relationship between innovation and entrepreneurship and ideological and political synergy in higher education

2.1

Value Leadership of Ideological and Political Education on Innovation and Entrepreneurship Education

Value is a mutual relationship between a person as a subject and an object. In the subject-object practical relationship, cognitive relationship, permeated with the subject based on their own needs of the object selection and utilization of the relationship, or the object can meet the subject of a certain need inherent properties. This relationship between the subject’s needs and the object’s attributes is the value relationship between the subject and the object, which is an important source of mobility and creativity in human activities. Innovation and entrepreneurship values are a more stable value orientation that is formed by elements such as cognition, expectation and certain social responsibility of innovation and entrepreneurship, which is related to the benign development of innovation and entrepreneurship and the achievement of the overall goal. Whether young people choose to start a business, what entrepreneurial goals they set, what entrepreneurial methods they choose, and the process of entrepreneurship are all influenced by entrepreneurial values. Entrepreneurs themselves may not be aware of it, but innovative entrepreneurial values as a systematic concept play an important role in regulating entrepreneurial behavior at any time and place. Moreover, whether the entrepreneurial motivation is correct, whether there is enough self-confidence, whether the entrepreneurial process is operated in good faith, and whether the entrepreneurial behavior is guided by the law, are all affected by entrepreneurial value orientation.

2.2

Disciplinary contribution of innovation and entrepreneurship education to ideological and political education

Innovation and entrepreneurship education is a kind of comprehensive education integrating management and other disciplines in one, filling in the blank areas appearing in the operation of practice for ideological and political education from the perspective and research paradigm of management science. Ideological and political education is a kind of educational activity built on the basis of practice, while management science focuses more on the solution of technical problems, with the aim of achieving the desired goals through the combination of various elements. Management is a philosophy of practitioners, and the contradiction between inherent resources and the pursuit of achieving maximum performance is its main contradiction. Innovation and entrepreneurship education is precisely to enrich the necessary management activities, different from simple classroom teaching activities, more involved in the operation of the practice in the real world, and its overall educational objectives to achieve the effective operation of management activities to indicate the direction. Management lay the foundation for innovation and entrepreneurship education and provides a broad space for development. The management of innovation and entrepreneurship education means realizing the maximum benefit output of input resources, reducing the consumption and waste of educational resources, and cultivating high-quality and excellent talents more efficiently.

3

Evaluation system of collaborative education based on DA-BP-AHP

With the gradual application of big data, machine learning, and other intelligent technologies in the field of educational assessment by researchers, some research scholars have already used neural networks for educational assessment. On the basis of the hierarchical analysis method DA-BP to construct the teaching evaluation index system of collaborative cultivation of college civics and innovation and entrepreneurship, the scores of 27 evaluation indexes affecting the quality of college civics teaching are used as the input of AHP, and the comprehensive scores of the quality of college civics teaching are used as the output of AHP.

3.1

BP Neural Network

The size of the value of the sample of the evaluation of the teaching of professional courses in Civics and Politics input into the BP neural network has a certain impact on the network training, and when the value is too large, it will increase the amount of computation of the operation of the BP neural network, the speed of network training will become slower, and the accuracy of the network training will also decrease. Therefore, it is necessary to normalize the sample data for training and testing, and take the maximum-minimum processing method to limit the data values to the 0~1 interval (including 0 and 1), in order to ensure the homogenization of the data and the smoothness of the distribution. The inverse normalization process resulted in the reduction of the network output values to the percentile course Civics teaching evaluation values.

BP neural network is a multilayer feed forward neural network consisting of input, hidden and output layers, if the input and output dimensions of the BP network are m and 1 respectively, and the number of hidden layers is p, the mathematical expression for the mapping of BP neural network is given in equation (1): 1 $x_{i + 1} = f (X_{i}) = \frac{1}{1 + \exp (- \sum_{j = 1}^{p} c_{j} b_{j} + ε)} j = 1, 2, \dots, p$ where f is the hidden layer excitation function; ε is the threshold value of the output layer; c_j and b_j are the connection weights from the hidden layer to the output layer and the output of the hidden layer node, respectively.

Therefore, the output of the hidden layer node of the BP neural network can be expressed as equation (2): 2 $b_{j} = \frac{1}{1 + \exp (- \sum_{i = 1}^{m} ω_{i j} x_{i} + θ_{j})} i = 1, 2, \dots, m$ where ω_ij is the connection weight from the input layer to the hidden layer; θ_j is the threshold value of the hidden layer node.

Since the prediction results of BP neural network are easily affected by the initial connection weights c_j, ω_ij and thresholds ε, θ_j, as well as its tendency to fall into the problem of local extremes, this paper uses DA to optimize the initial connection weights and thresholds of BP neural network.

3.2

DA-BP algorithm flow

Step1:

Initialize the BP neural network model and determine the network structure. Determine the number of layers of the BP neural network, the type of transfer function and training function, and the number of nodes in each layer according to the data samples. Read the data of the evaluation of the teaching quality of college Civics classroom and preprocess it, and divide the data into training set and test set.

Step2:

Coding. the DA algorithm uses real number coding to encode the connection weights c_j, ω_ij and thresholds ε, θ_j as a whole, the search space dimension of the algorithm is m, if the number of nodes in the input layer, the implicit layer, and the output layer are R, S₁, and S₂, respectively, then the coding length of S can be expressed as equation (3): 3 $S = R S + S_{1} S_{2} + S_{1} + S_{2}$

Step3:

Initialization of DA algorithm parameters: population size N, maximum number of iterations T.

Step4:

Randomly initialize the step vector ΔX and randomly generate the initial position of dragonfly individuals X.

Step5:

Make the current iteration number t = 1, input the training set into BP, calculate the fitness of all dragonfly individuals according to the fitness function formula (4), and sort to record the current optimal solution.

The mean square error is chosen as the fitness function, which is as in equation (4): 4 $f i t n e s s = \frac{1}{k} \sum_{i = 1}^{k} {(y_{i} - {\hat{y}}_{i})}^{2}$ where y_i and ${\hat{y}}_{i}$ are the actual and desired outputs of the i rd sample, respectively; k is the number of samples.

Step6:

Update the food source position X⁺(current optimal solution) and natural enemy position X^- (current worst solution), and update the five behavioral weights s, a, c, f, e and inertia weight w.

Step7:

Update S, A, C, E and F.

Step8:

Update step vector and position vector.

Step9:

If iteration number t > T, save the optimal connection weights c_j, ω_ij and thresholds ε, θ_j; otherwise, t = t + 1, return to Step5.

3.3

Calculation of indicator weights based on the hierarchical analysis method

3.3.1

Calculation of indicator weights

Hierarchical analysis is a method that uses multi-factor decision-making to construct a hierarchical structure, compare the importance of factors two by two, and calculate weights. The method decomposes the decision-making problem into different levels and factors, and determines the weights between the factors through expert judgment or data analysis, and finally arrives at the optimal choice. Hierarchical analysis method is suitable for complex decision-making problems, and can quantify and structurally analyze the relationships between factors, providing objective and systematic decision-making support. The advantage of this method is that it has structured, multifactor consideration, and clear allocation of weights, etc., but it requires more experts to participate in the data support.

In this paper, the initial system construction is carried out by conducting a thorough analysis and evaluating the relative importance of each index. Subsequently, in order to ensure the practicality and accuracy of the evaluation system, the importance of the indicators is meticulously adjusted to finally form a scientific and reasonable judgment matrix of indicators.

The eigenvector corresponding to the largest characteristic root λmax of the judgment matrix is recorded as W after normalization. The element of W is the sorting weight of the same level factor for the relative importance of the factors at the previous level, and this process is called hierarchical single sorting. Based on the evaluation indexes selected in this paper, if we want to get the weights of the three indexes A1 Functional Services, A2 Business Construction, and A3 Website Design, we have to compare the importance of the three indexes two by two, so as to form a 3 × 3 judgment matrix, and then calculate the eigenvectors, and finally get the weight values of these three indexes. 1)

Normalize each column of the above judgment results in the form of a matrix: 5 $[A = \begin{matrix} 1 & 2 & 4 \\ 1 / 2 & 1 & 3 \\ 4 & 1 / 3 & 1 \end{matrix}]$

2)

Column vector normalization: 6 $[\begin{matrix} 2 / 11 & 3 / 5 & 1 / 2 \\ 1 / 11 & 3 / 10 & 3 / 8 \\ 8 / 11 & 1 / 10 & 1 / 8 \end{matrix}]$

3)

The weights of the indicators after normalization W are: 7 $[\begin{array}{r} 0.42727 \\ W = 0.25530 \\ 0.31742 \end{array}]$ where A*W is: 8 $[A W = \begin{matrix} 0.54767 \\ 0.19553 \\ 0.25679 \end{matrix}]$

4)

After the weight matrix is obtained above, the maximum characteristic root can be calculated with the formula: 9 $λ \max = \frac{1}{n} \sum_{i = 1}^{n} \frac{A W_{i}}{W_{i}}$

AW is: judgment matrix × normalized weights, then cumulative value by row. The maximum eigenvalue λmax = x/Matrix order = 3.0. according to C.I Value Eq. λmax = 3.0, n = 3, substitution leads to C.I Value = 0.0. C.I. = 0 means that the judgment matrix is completely consistent, and the larger C.I. is, the more serious the degree of inconsistency of the judgment matrix.

3.3.2

Consistency tests for matrices

The consistency test ensures that the judgment of the importance of the indicators is stable and consistent and improves the validity of the evaluation results. To measure the size of the CI, a random consistency indicator RI is introduced, and at n = 3, R.I. = 0.58, substituted into Eq: 10 $R . I . = \frac{C I_{1} + C I_{2} + C I_{3} + \dots C I_{n}}{n}$ 11 $C . R . = \frac{C . I .}{R . I .}$

The value of C.R. can be calculated to be 0/0.58 = 0. So when C.R. < 0.1, it indicates that the degree of consistency of the judgment matrix A is considered to be within tolerance, and at this point the weight vector calculation can be carried out with the eigenvectors of A.

4

Research on the construction of paths for the internal mechanism of collaborative education

4.1

The current situation of collaborative cultivation of innovation and entrepreneurship education in higher education institutions ’ civic and political education

First, through literature research and tracking the latest research results. Overall, grasp the basic views and attitudes of college students on collaborative education, ideology, innovation, and entrepreneurship education, and conduct analysis and generalization.

Secondly, design the questionnaire. In order to be able to fully understand the situation of collaborative parenting, 20 copies of the first version of the questionnaire were first issued in S colleges and universities experimentally, and continuously adjusted through feedback suggestions. The main framework of the questionnaire was finally determined as follows: first, the basic information of the survey respondents. The second is the basic status quo of college students’ ideological development in colleges and universities. Third, the current situation of innovation and entrepreneurship education development in colleges and universities. Fourth, the situation of collaborative cultivation of college students’ ideological development and innovation and entrepreneurship education in colleges and universities. Finally, data statistics and analysis. Clarify the cultivation situation and development of each of the two crops, as well as the challenges of collaborative cultivation, and analyze the reasons for their development.

Based on the questionnaire data, the validity was derived using SPSS19.0 as shown in Table 1. Reliability refers to the degree of consistency of the results obtained when the same method is used to repeat the measurement of the same object. The reliability coefficient of the total scale is between 0.6-0.8.

Table 1.

Reliability

Dimension approach	Term number	Cronbach’s Alpha
The status quo of college thinking	5	0.740
The status quo of innovation and entrepreneurship education	5	0.732
The depth of thought	4	0.654
Depth of cooperation level	3	0.627
The depth of policy security	3	0.674
Span of the course system	3	0.637
The span of the practice platform	4	0.625
Span of team construction	4	0.638

A total of 730 questionnaires were returned in this research, of which 670 were valid, with an effective rate of 91.78%. The results of the survey are shown in Figure 1.

Among the innovation and entrepreneurship education programs implemented in colleges and universities, too much attention is paid to the guidance of students’ personal values, so that students can enhance their personal values through entrepreneurship. By analyzing the data from the questionnaire survey, it can be seen that the purpose of entrepreneurship is chosen by the vast majority of students is to be able to meet their personal needs, but neglected to develop students’ sense of social responsibility and fully recognize the importance of social value. It is believed that entrepreneurship can effectively solve their own employment problems accounted for 60%, can improve the conditions of life accounted for 38.4%, but that entrepreneurship responds to the call of the country only 9.6%, to realize the common wealth only 7.8%. This means that many students do not have a clear understanding of the concept of innovation and entrepreneurship education, and there is a very serious utilitarian mindset in the process of entrepreneurship.

The problems of co-curricular teaching are shown in Table 2. The curriculum of collaborative education lacks innovation and the content is too repetitive. Co-education of the characteristics of these two courses does not fully show the characteristics of innovation, as the content is very simple and mainly introduced and taught through monolithic classroom teaching methods. Furthermore, there are very few practical activities both inside and outside the classroom, which results in a low perception rate among students regarding these two synergistic disciplines. Through the results of the questionnaire, it can be seen that students mainly learn about the combination of the two through the innovation and entrepreneurship courses (92%), but very few of them learn about the combination of the two through other ways. Since the innovation and entrepreneurship course has also occupied a certain position in the university, students also pay more attention to it, but the university has not fully utilized the role of the classroom and extracurricular as the main platform to highlight the combination of the two. For S colleges and universities, the synergistic cultivation between the two should be demonstrated through a variety of ways and means to ensure the effect of synergistic cultivation.

Table 2.

Fusion mode

Innovation entrepreneurship education and ideological and political education fusion mode	Collaborative approach	Proportion
	Innovative entrepreneurship courses	0.92
	The cultural atmosphere of the creative space	0.563
	Innovative entrepreneurship competition	0.132
	Salon lecture	0.248
	The internship base of the university enterprise	0.18
	The services in the innovation and entrepreneurship process	0.12

4.2

Research on the path of the inner mechanism of collaborative education

4.2.1

Depth of collaborative parenting

Using the results of the questionnaire survey above, the number of neurons in the input layer of BP neural network combined with hierarchical analysis n is 27, which corresponds to 27 evaluation indicators. The depth evaluation indexes of collaborative parenting are analyzed by analyzing the weights of the neurons in the output layer m, which is 1. The results of the weights of the depth evaluation indexes of collaborative parenting are shown in Table 3, in the depth of ideological awareness A1, the weight of B2 is the highest, λ2=0.4281, indicating that it is very important for teachers and students to agree on the consistency of the goals of innovation and entrepreneurship education and ideological and political education in colleges and universities.

Table 3.

The weight of the evaluation index of the collaborative education

Target layer	Criterion layer	Criterion layer weight	Index layer	Hierarchical weight	Overall weight
The depth of the collaborative education	The depth of thought A1	0.4122	The students and students agree with the reform and development of education reform and development of the university B1	0.2473	0.101937
			Students and students agree on the orientation of innovation entrepreneurship education and the goal of thinking policy education B2	0.4281	0.176463
			Students and students agree on the communication of education and the way of thinking and education B3	0.1632	0.067271
			Students and students agree with the innovation of education and the content of the education B4	0.1614	0.066529
	Depth of cooperation level A2	0.3287	Strengthen cooperation with industry enterprises in talent training B5	0.1760	0.057851
			We will actively cooperate with local governments in the practice of thinking on education B6	0.2762	0.090787
			Jointly build and build the practice base of and outside the campus B7	0.1480	0.048648
			We will strengthen interactive exchanges and cooperation between professional teachers, thinking teachers and innovation and entrepreneurship teachers research B8	0.3998	0.131414
	The depth of policy security A3	0.2591	The top-level design and the introduction of the curriculum and the innovation and entrepreneurship education reform measures and policies B9	0.3872	0.077782
			The relevant incentive policies encourage the students and students to participate in the curriculum and innovation entrepreneurship education activities B10	0.2028	0.052545
			The teachers’ training mechanism is dedicated to improving the teachers’ innovation and entrepreneurship level and the teaching quality of the course and the course B11	0.1098	0.028449
			The evaluation system of innovation and entrepreneurship should be more concerned with the evaluation of the political quality of students’ ideological and political quality B12	0.38720.3002	0.100324

The recognition of the goals of innovation and entrepreneurship education and civic education by teachers and students at large is the foundation and prerequisite for collaborative education between the two. In the depth of cooperation level A2, B8 has the highest weight, λ 8=0.3998, indicating that it is very important to strengthen the interactive exchange and cooperation among teachers of professional courses, teachers of ideology and politics, and teachers of innovation and entrepreneurship in scientific research. To realize the synergistic education of innovation, entrepreneurship, and ideological and political education, we should fully leverage the mutual promotion role of all teachers in schools. In the depth A3 of policy guarantee, B9 has the highest weight, λ 9=0.3872, indicating that it is very crucial to design and introduce reform measures and policies related to ideological and political education and innovation and entrepreneurship education at the top level and that the “Curriculum Civics” of colleges and universities is very important. The implementation of “Curriculum Civics and Politics” and the teaching reform of innovation and entrepreneurship education in colleges and universities, including the overall goal of ideological and political theory courses, should be well designed at the top level and systematically planned, following the laws of education, and all of them should be incorporated into the cultivation program of professional talents. In the guideline layer, the highest weight is the depth of ideological awareness A1, 0.4122, which indicates that the collaborative education of ideological and political education and innovation and entrepreneurship education, the extreme importance of the ideological awareness of each participant, only fully equipped with the ideological awareness, in order to lay a solid foundation for the smooth development of the two collaborative education.

4.2.2

Breadth of collaborative parenting

Analysis of the weight of the breadth evaluation index of collaborative parenting. The results of the weights of the breadth evaluation indexes of collaborative parenting are shown in Table 4, in the breadth A1 of the curriculum system, the weight of B2 is the highest, λ 2=0.3370, which indicates that it is very important for the college innovation and entrepreneurship education and ideological and political education courses to cover all types of students in the school in an all-round way. In the curriculum education system of colleges and universities, it is important to both turn the whole into zero and combine innovation and entrepreneurship education with ideology and politics in various forms such as compulsory courses and elective courses.

Table 4.

The scale of the evaluation index of the collaborative education

Target layer	Criterion layer	Criterion layer weight	Index layer	Hierarchical weight	Overall weight
The breadth review of collaborative education	Span of the course system A1	0.4128	Innovative entrepreneurship education and ideological and political education course group design is reasonable and organic B1	0.2378	0.098164
			Innovation entrepreneurship education and ideological and political education courses cover all kinds of students B2	0.3370	0.139114
			In the course of thinking and politics, innovation and innovation B3	0.1410	0.058205
			The innovation entrepreneurship course focuses on the integration of value B4	0.2842	0.117318
	The span of the practice platform A2	0.3269	Based on the modern information technology platform, we will promote the innovation and entrepreneurship and the guidance of the government B5	0.1409	0.04606
			The university science and technology park and other practical bases can provide targeted guidance for students B6	0.1120	0.036613
			We will provide the appropriate innovation and entrepreneurship space environment, and create a platform for innovation and incubation for college students B7	0.0763	0.024942
			Invite famous people to hold a series of distinctive innovative entrepreneurship and other cultural activities B8	0.21168	0.069198
			Strengthen the guidance of innovative entrepreneurship training programs, projects and competitions B9	0.094	0.030729
			The practice of innovation and entrepreneurship education is closely connected with the practice of social education and the cultural activities of campus B10	0.36512	0.119358
	Span of team construction A3	0.2603	We have a certain theoretical knowledge and innovative entrepreneurial experience B11	0.0906	0.023583
			Hire students outside the campus to be a team of innovative entrepreneurship practices B12	0.1615	0.042038
			Introduce part-time teachers and good entrepreneurs into the classroom B13	0.2311	0.060155
			We will cultivate the “double charge” teachers for innovation and education and ideological and political education B14	0.3052	0.079444
			We will strengthen the interaction between the teaching and research of professional teachers and thinking teachers B15	0.2116	0.055079

In the breadth of practice platform A2, B10 has the highest weight, λ 10=0.36512, indicating that it is very important for innovation and entrepreneurship education practice activities to be closely connected with daily ideological and political education practice activities as well as campus cultural activities.

In the breadth of team building A3, B14 has the highest weight, λ 14=0.3052, indicating that the cultivation of “double-quality” teachers of innovation and entrepreneurship education and ideological and political education is an important factor in realizing the synergistic cultivation of innovation and entrepreneurship education and ideological and political education. Teachers of innovation and entrepreneurship education and ideological and political education need to learn from each other, strengthen interaction, and complement each other’s strengths and weaknesses, and at the same time, the top-level design needs to emphasize the creation of a group of high-level and high-quality teachers.

4.2.3

Practice Measurement Discussion

Furthermore, the index system of collaborative education degree constructed is applied to practice, so as to objectively evaluate the current status of collaborative education degree of innovation and entrepreneurship education and ideological and political education in domestic universities. In this study, 5 colleges and universities (A~E) in a province were selected as the research objects, that is, the evaluation results of ideological and political teaching of professional courses were divided into 5 evaluation grades, of which 90~100 points (including 90 and 100) were excellent, 80~90 points (including 80 but excluding 90) were good, 60~80 points (including 60 but excluding 80) were average, 30~60 points (including 30 but not including 60) were poor, and 0~30 points (including 0 but excluding 30) were very poor. The results of this survey are shown in Table 5, based on the weighted scores of each university, we can believe that the current collaborative education of innovation and entrepreneurship education and ideological and political education in colleges and universities is not ideal, only the score of school C is above good, and the scores of A, C and E are above 60.

Table 5.

Evaluation result

	A	B	C	D	E
The depth of the collaborative education	62.547	53.484	86.375	63.295	70.897
The breadth review of collaborative education	70.326	54.785	86.385	55.387	60.495

5

Conclusion

This paper analyzes the relevant information and questionnaire survey, draws on the connotation and relationship between innovation and entrepreneurship and ideological education in colleges and universities, outputs evaluation indexes of innovation and entrepreneurship and ideological education through DA-BP neural network model, and utilizes hierarchical analysis to assign weights to the indexes at each level, outputs the evaluation results of synergistic education, so as to explore the path of the inherent mechanism of synergistic education. Analyzing the research results, it can be concluded that the output of the BP neural network indicator layer corresponds to 27 indicators. Among them, in the depth of collaborative parenting, the depth of ideological awareness is the highest weight. Therefore, collaborative parenting needs to be recognized for goal consistency. In the breadth of collaborative parenting, the breadth of the curriculum system has the highest weight value, and in the future, we should broaden the scope of the curriculum of ideology and politics education, and combine innovation and entrepreneurship education with ideology and politics through various forms.

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

Research on the construction of the intrinsic mechanism path of collaborative cultivation in Civic and Political Education, Innovation and Entrepreneurship Education based on Machine Learning Models

Yang Yang

Data publikacji: 19 mar 2025

Otrzymano: 06 lis 2024

Przyjęty: 20 lut 2025

DOI: https://doi.org/10.2478/amns-2025-0397

Słowa kluczoweCollaborative parenting, DA-BP model, AHP, Weight calculation

© 2025 Yang Yang, published by Sciendo

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

Słowa kluczowe
Collaborative parenting, DA-BP model, AHP, Weight calculation