Research on the Mechanism and Path of the Integration of Digital-Enabled Civic and Political Education in Colleges and Universities with Dual Innovation Education

Accompanied by the national policy promotion in the field of “double creation”, more and more colleges and universities begin to focus on cultivating talents with innovation and entrepreneurship ability. The core of innovation and entrepreneurship education is to stimulate students’ innovation and entrepreneurship awareness and ability, not only focusing on the transfer of knowledge, but also focusing on the cultivation of practice and innovation ability [1-3]. With the development of national economy, more and more enterprises hope to recruit graduates with certain innovation and entrepreneurship consciousness, which also prompts universities to strengthen innovation and entrepreneurship education [4-5]. The core of ideological education in colleges and universities is patriotism education, especially to combine patriotism education with modernization construction, so that students deeply understand that socialist modernization construction is the road to achieve the great rejuvenation of the Chinese nation. Civic and political education should not only maintain the combination of traditional culture and modern thinking, but also focus on the cultivation of students’ ideological character and the enhancement of knowledge and ability [6-9].

The deep integration of innovation and entrepreneurship education and ideological education in colleges and universities is very necessary. First of all, innovation and entrepreneurship education emphasizes on practice, even if there is a good original idea, it is difficult to succeed if it lacks practical ability. Civic and political education can help students understand the meaning of the ideas behind practical education and cultivate students’ ability to judge things correctly [10-13]. Secondly, innovation and entrepreneurship education focuses on the value of “people-oriented”, and the ideological and political education should emphasize the power of humanistic value infection in this deep integration. In the digital era, such a deep integration can really let students understand and realize the intrinsic correlation between technological progress and social change [14-17].

Literature [18] explains the significance of the integration of ideological and political education and innovation and entrepreneurship education. It puts forward the teaching program of Civic and Political Education based on the theory of “OBE Education Concept + Innovation and Entrepreneurship Education + Ideological and Political Education + Labor Education + Professional Education”, which aims to promote the comprehensive development of students through diversified teaching methods and contents, and has a reference role in education reform. Literature [19] examines the necessity of combining innovation and entrepreneurship education with civic and political education in colleges and universities, as well as the methods of combining the two. It aims to provide ideas for popularizing the concept of innovation and entrepreneurship education in colleges and universities. Literature [20] describes that in recent years, the fusion of political education and “dual creation” education in colleges and universities has attracted much attention, and scholars’ view is that the fusion of the two is conducive to the establishment of the correct values of students, and enhances their innovative entrepreneurial qualities and abilities. However, there are problems such as backward concepts and weak teachers. Literature [21] indicates that the development of network information technology has brought rich teaching resources for ideological education and broadened its educational direction, on this basis, the integration of innovation and entrepreneurship education and ideological education has become an important educational issue that needs to be realized in the context of the Internet. Literature [22] explored the construction of the curriculum system for the integration of Civic and Political Education and Innovation and Entrepreneurship Education in colleges and universities, including the general framework and basic content, etc., and the results of its research are also of reference significance to other disciplines. Literature [23] explored the significance, current situation and solution of the combination of Civic and Political Education and Innovation and Entrepreneurship Practical Education, aiming at combining Civic and Political Education with Innovation and Entrepreneurship Education to promote the overall healthy development of students.

Literature [24] shows that it is necessary to combine the innovative education model and innovation and entrepreneurship in colleges and universities, because the combination of the two is not only conducive to promoting the brand-new development of civic and political education, but also has an important guiding role for students’ entrepreneurship, and emphasizes that the integration of civic and political education and innovation and entrepreneurship education is an inevitable trend of the development of the times. Literature [25] explored the problems related to the combination of innovation and entrepreneurship education and civic and political education. It is mentioned that the combination of the two is the need to promote the comprehensive and healthy development of students in the new era, and it is of great significance to cultivate high-quality talents and improve students’ employment. Literature [26] specifies the social demand for talents with innovation and entrepreneurship ability, and reveals that due to the neglect of students’ civic and political education in colleges and universities, which leads to the lack of ideal employment situation, it is suggested that the combination of civic and political education with innovation and entrepreneurship education is aimed at laying the foundation for the future development of students. Literature [27] discusses the significance of the integration of Civic and Political Education and Innovation and Entrepreneurship from the perspective of social development, student needs, etc. In addition to improving the teaching effect of Civic and Political Education, it also enhances the guidance of Innovation and Entrepreneurship Science, and summarizes the practical experience of cultivating “dual-capable” teachers and creating campus atmosphere. Literature [28] explores the significance and inevitability, challenges and solution strategies of combining “dual-energy” education with Civic and Political Education in colleges and universities. It shows that combining innovation and entrepreneurship with the ideological and political education program is an important measure to improve the ideological and political work of students in the new era. Based on the analysis of the pros and cons of college students’ innovation and entrepreneurship in the new era, [29] explored the combination of ideological and political education with innovation and entrepreneurship education in colleges and universities, aiming to improve the comprehensive quality and ability of students.

This paper takes the four core civic and political elements as its base point and elaborates on them.Civic and political cases related to the theme of double creation are filtered and categorized into the case library, and the constructed Civic and political case library is dynamically improved, revised, updated, and maintained. Then a hybrid recommendation algorithm based on collaborative filtering and resource content is proposed, and the similarity results are obtained by Pearson similarity, and the collaborative filtering mechanism of the penalty factor is integrated into the algorithm, so as to improve the Pearson correlation coefficient. The system designed in this paper is divided into a four-layer structure of display layer, service layer, data layer and infrastructure layer, and the functional modules are divided into user module, resource module and background management module. The detailed construction of the system has been completed with a four-layer structure and three major modules.Finally, the practical application of the intelligent recommendation system is verified.

2

Mechanisms and paths of collaborative education

2.1

Mechanisms of collaborative parenting

Bicreative education and civic education are complementary to each other, and both are indispensable. Therefore, by utilizing the convergence point between civic education and creative education, the value of synergistic cultivation can be fully utilized.

Civic and political education itself is a compulsory public course for college students, and with the help of the elements of Civic and political education, college students are encouraged to be firm in their ideals and beliefs, cultivate their political quality in their thoughts and behaviors, and provide assistance for them to become outstanding socialist successors. Therefore, the integration of bicultural education and ideological education is practicable and conducive to the cultivation of a large number of innovative, composite and applied talents that meet the needs of various positions in the society, and establishes a good reputation and a positive image of colleges and universities while maximizing the core competitiveness of colleges and universities.

Under the background of production, education and research, colleges and universities have accelerated the education and teaching reform, actively shouldered the responsibility and mission of educating people, and based on the career-oriented perspective, combined with the career planning and employment of college students, formulated a perfect mechanism for educating people, accelerated the construction of Civic and Political Education and Dual Creation Education System, and practically cultivated college students’ innovative and entrepreneurial consciousness and thinking under the perspective of Civic and Political Education, and encouraged college students to be brave to innovate and enhance their employability skills.

2.2

Innovative teaching mode to help collaborative education

Counselors in daily education, grasp the personalized characteristics of students, real-time understanding of the ideological dynamics of students, and in the collaborative nurturing, with the help of Civic Education cases to regulate the behavior of students, increase interaction and communication with students, enrich the content of education at the same time, to improve the ideological and moral level of college students. And then play the nurturing role of political education. At present, some colleges and universities have established a perfect guarantee mechanism to coordinate the relationship between civic and political education and dual-creation education in dual-creation education and civic and political education. Counselors actively participate in education and teaching, effectively use the guiding role of ideological education, make scientific planning for college students’ innovation and entrepreneurship, effectively implement the task of collaborative education, enhance the effectiveness of education and teaching work and at the same time, provide support for the implementation of the mechanism of establishing morality and cultivating people.

Colleges and universities increase the investment of funds, encourage and guide each college, teachers to improve their own information literacy, the flexible use of modern digital education technology to assist the development of digital education and teaching, such as the construction of multimedia classrooms, the introduction of intelligent, information-based personalized teaching equipment, the construction of on-campus and off-campus training bases, to encourage college students to innovate on their own, and to organize and carry out regular innovation and skills competitions.

3

Construction of a casebook on the ideology and politics of dual-creation courses

3.1

Establishment of Civics Elements

Revising and increasing the objectives of civic politics, combining emotional attitude objectives with elements of civic politics, and setting more specific objectives of civic politics in each chapter. Taking the revised syllabus as the main body, combining with the Guidelines for Civic and Political Construction of Courses in Colleges of Higher Education on “dual-creation” education courses in practical courses and the guidelines for “dual-creation” education in disciplines such as engineering and management, we have extracted four core civic and political elements The four core elements of ideology and politics are extracted: scientific and creative aspiration, scientific and creative consciousness, scientific and creative confidence and scientific and creative ability. Based on this, the elements are analyzed through case studies.

3.2

Collection of Courses’ Civics Cases

With the four major elements of Civic and Political Education as the core, we collect the stories of the founders of technology companies or Internet companies related to the four major elements of Civic and Political Education, and the stories of Internet “dual-creation” through reviewing literature and books, logging into mobile learning platforms such as “Learning Strong Nation”, on-site interviews, and searching on the Internet. We screened cases related to the integration of Civic and Political education with dual-creation education, re-edited those suitable for teaching and put them into the case database, and categorized the cases according to the dimensions of chapters and presentation styles, and finally chose the suitable cases.

3.3

Revision and Updating of Civics Cases

Different groups, such as front-line teachers, teaching experts, and learner representatives, are invited to review the four major elements of Civics and the corresponding Civics cases and labels outlined so far, to ensure that the cases in the Civics case bank are scientific, rigorous, reasonable, and systematic. It is guaranteed that the cases can be closely integrated with the key knowledge points within the course, as well as stimulate the interest of the learners and make them silent. Finally, according to the feedback received, the Civics and Politics case base will be improved and revised.

The Civics and Politics case library construction needs to be updated and maintained dynamically at all times. Regularly pay attention to the stories of “Double Creation” in “Learning Strong Country”, the celebrity stories of “Double Creation” in the Internet hot search and related reports, the major events of Internet companies and other related cases, and then check and screen them in the case database. The cases are checked and screened in the case database, and new cases are formed after checking and modifying by editors, and are regularly discussed with the teaching team, teaching experts and learners’ representatives to continuously improve and update the case database.

4

Hybrid recommendation algorithms based on collaborative filtering and resource content

In the previous paper, we constructed a case base for the Civics and Politics of the “dual-creation” education program. The latter paper proposes to use personalized recommendation algorithms based on collaborative filtering and resource content to recommend cases to learners, integrate the elements of civic politics into the teaching process, and help learners carry out personalized learning. This will provide a new approach to “dual-creation” education and the ideology and politics of the curriculum.

4.1

Selection of hybrid recommendation algorithms

In order to improve the real-time, accuracy, and applicability of the recommendation model of the dual-creation course Civics case, this paper proposes a hybrid recommendation algorithm based on collaborative filtering and resource content [30], due to the fact that the users of the system are relatively fixed and have the same hierarchy, and there are a large number of resources with common ratings, the Pearson’s similarity is adopted as the similarity calculation method of the recommendation model, and the collaborative filtering mechanism with the introduction of penalty factor is introduced to the Pearson correlation coefficient is improved [31], which makes up for the lack of data sparsity.

Regarding the cold-start problem of the system, the Civics case resource can be given relevant tags when it is uploaded, and the content-based recommendation algorithm can be used to extract the features of these tags, analyze the similarity between the target resource and other resources, predict the ratings of the new resource, and use the predicted ratings to fill the original user-resource matrix. The flow of this hybrid recommendation algorithm is shown in Fig. 1.

4.2

Improvement of Similarity Calculation

Civic case resources vary widely, so a penalty factor is added for these resources with too long a rating time interval. The time decay weights are shown in equation (1), where t_um and t_un represent the rating time of user u for resources m and n. It improves the insufficiency of the traditional similarity calculation that relies only on user ratings, and can more objectively and accurately recommend the desired resources for users.

(1)

f (t) = \frac{e^{t_{u m} - t_{u n}}}{1 + e^{t_{u m} - t_{u n}}}

\[f(t)=\frac{{{e}^{{{t}_{um}}-{{t}_{un}}}}}{1+{{e}^{{{t}_{um}}-{{t}_{un}}}}}\]

Combining the above two penalty factors on the Pearson correlation coefficient not only reduces the scoring ratio of active resources, but also reflects the transfer of user preferences at different times. The similarity calculation formula after combining the two penalty factors is shown in equation (2).

(2)

s i m_{1} (m, n) = θ \times f (t) \times \frac{\sum_{u \in U_{m n}} (r_{u m} - \bar{r_{m}}) (r_{u n} - \bar{r_{n}})}{\sqrt{Σ_{u \in U_{m n}} {(r_{u m} - \bar{r_{m}})}^{2} \times \sqrt{\sum_{u \in U_{m n}} {(r_{u n} - \bar{r_{n}})}^{2}}}}

\[si{{m}_{1}}(m,n)=\theta \times f(t)\times \frac{\sum\limits_{u\in {{U}_{mn}}}{({{r}_{um}}-\overline{{{r}_{m}}})}({{r}_{un}}-\overline{{{r}_{n}}})}{\sqrt{{{\Sigma }_{u\in {{U}_{mn}}}}{{({{r}_{um}}-\overline{{{r}_{m}}})}^{2}}\times \sqrt{\sum\limits_{u\in {{U}_{mn}}}{{{({{r}_{un}}-\overline{{{r}_{n}}})}^{2}}}}}}\]

4.2.1

Similarity Improvement for Collaborative Filtering Algorithms

The first step is to construct the user-resource scoring matrix. The construction of the matrix is completed according to the number of users and resources in the dataset and the rating information of users on resources.

In the second step, the Pearson correlation coefficient is used to calculate the similarity between the Civic resources, as shown in equation (3). In the formula, U_mn denotes the set of users who have rated both resource m and resource n, τ_um and r_un denote the ratings of resource m and resource n by user u, and r_m and r_n denote the mean value of the scores of resource m and resource n, respectively, in a certain time period.

(3)

s i m (m, n) = \frac{Σ_{u \in U_{m n}} (r_{u m} - \bar{r_{m}}) (r_{u n} - \bar{r_{n}})}{\sqrt{Σ_{u \in U_{m n}} {(r_{u m} - \bar{r_{m}})}^{2} \times \sqrt{\sum_{u \in U_{m n}} {(r_{u n} - \bar{r_{n}})}^{2}}}}

\[sim(m,n)=\frac{{{\Sigma }_{u\in {{U}_{mn}}}}({{r}_{um}}-\overline{{{r}_{m}}})({{r}_{un}}-\overline{{{r}_{n}}})}{\sqrt{{{\Sigma }_{u\in {{U}_{mn}}}}{{({{r}_{um}}-\overline{{{r}_{m}}})}^{2}}\times \sqrt{\sum\limits_{u\in {{U}_{mn}}}{{{({{r}_{un}}-\overline{{{r}_{n}}})}^{2}}}}}}\]

The third step is to add the weights of the popular resources penalty factor. The Civics case recommendation system in this paper aims to personalize the recommendation of high-quality Civics case resources that students are interested in, so it is necessary to reduce the influence of popular resources on the similarity, and appropriately increase the weight of the ratings of nonpopular resources to achieve accurate recommendation. The introduced weights are shown in equation (4), where I_uv represents the set of resources that user u and user v have rated together, r_ui and r_pi are the ratings of user u,v on resource i, and r_u and r_o represent the sum of ratings of user u,v own rated resources.

(4)

θ = \sqrt{\frac{Σ_{i \in I_{u v}} r_{u i} \times r_{v i}}{Σ_{i \in I_{u}} r_{u} \times \sum_{i \in I_{v}} r_{v}}}

\[\theta =\sqrt{\frac{{{\Sigma }_{i\in {{I}_{uv}}}}{{r}_{ui}}\times {{r}_{vi}}}{{{\Sigma }_{i\in {{I}_{u}}}}{{r}_{u}}\times \sum\limits_{i\in {{I}_{v}}}{{{r}_{v}}}}}\]

In the fourth step, a time decay penalty factor is added, mainly for the user to browse the resources with the change of time, as an example, in the case of the Civics case of the database course, the teaching progress with the advancement of time, students learn the content and technical points, and the Civics elements of each chapter are closely related to the specific knowledge points.

4.2.2

Similarity Improvement Based on Content Recommendation

In order to solve the cold-start problem that exists when new users enter the system [32] and produce accurate recommendation results, it is also necessary to quantify the content information of the Civic Case Resource.

Firstly, the weight value of the label to the resource is defined and the resource-label matrix is constructed as shown in equation (5).

(5)

R = [\begin{matrix} w_{11} & w_{12} & \dots & w_{1 b} \\ w_{21} & w_{22} & \dots & w_{2 b} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ w_{a 1} & w_{a 2} & \dots & w_{a b} \end{matrix}]

\[R=\left[ \begin{matrix} {{w}_{11}} & {{w}_{12}} & \cdots & {{w}_{1b}} \\ {{w}_{21}} & {{w}_{22}} & \cdots & {{w}_{2b}} \\ \vdots & \vdots & \ddots & \vdots \\ {{w}_{a1}} & {{w}_{a2}} & \cdots & {{w}_{ab}} \\ \end{matrix} \right]\]

After constructing the resource-label matrix, the weight value of the label to the user is calculated as shown in equation (6).

(6)

w = \frac{r (t_{m})}{r (u_{i})} \times \frac{b}{a}

\[w=\frac{r({{t}_{m}})}{r({{u}_{i}})}\times \frac{b}{a}\]

Then the user’s interest features and the label attributes of the resources are vectorized using the method of spatial vector representation.

(7)

s i m_{2} (m, n) = \frac{U_{m} \times V_{n}}{\sqrt{| U_{m} | \times | V_{n} |}}

\[si{{m}_{2}}(m,n)=\frac{{{U}_{m}}\times {{V}_{n}}}{\sqrt{|{{U}_{m}}|\times |{{V}_{n}}|}}\]

4.2.3

Hybrid Recommendation Algorithm Score Prediction

Combining the improved Pearson similarity sim₁(m,n) using the penalty factor and the similarity sim₂(m,n) calculated based on the content attributes to get the resource similarity of the hybrid recommendation algorithm, as shown in equation (8). Where α represents the weight accounted for by the similarity calculation method, the value range is 0-1, according to the differences in the resources can take different weights, the similarity obtained is also different.

(8)

s i m (m, n) = α \times s i m_{1} (m, n) + (1 - α) \times s i m_{2} (m, n)

\[sim(m,n)=\alpha \times si{{m}_{1}}(m,n)+(1-\alpha )\times si{{m}_{2}}(m,n)\]

According to the improved hybrid algorithm similarity calculation to find the set of nearest neighbors of the target resource, the predicted score of user u on resource m is shown in equation (9).

(9)

p_{u m} = \bar{r_{m}} + \frac{\sum_{n \in S (m, K)} s i m (m, n) \times (r_{u n} - \bar{r_{n}})}{\sum_{n \in S (m, K)} | s i m (m, n) |}

\[{{p}_{um}}=\overline{{{r}_{m}}}+\frac{\sum\limits_{n\in S(m,K)}{s}im(m,n)\times ({{r}_{un}}-\overline{{{r}_{n}}})}{\sum\limits_{n\in S(m,K)}{|sim(m,n)|}}\]

Finally, for the target user, traverse all the itemsets, select the set of resources that have not yet had a behavioral record, and generate the Civic Cases to-be-recommended list by sorting the resources according to their predicted scores from largest to smallest. Finally, the top N resources in the recommended list are selected as the Top-N recommended list.

5

Design of a Recommendation System for Civics Cases in Dual Creativity Courses

5.1

System architecture design

In order to make the system more scalable , the system uses B / S architecture , the use of MVC design ideas in the development of the system will be separated from the front and back ends , the front-end use of the Uni-app framework to ensure that the user has a good interface interaction experience , the back-end use of the Springboot framework to integrate the persistence layer of the Mvbatis framework for the development of each functional module interface to facilitate the interaction and the front-end , the simplification of JavaBean coding by adding lombok plug-ins and logging annotations . Simplify JavaBean coding by adding the Loomok plugin and Log log annotations. In order to improve the performance of the database operation added Druid database connection pooling and log monitoring. In this paper, the system is divided into four layers: display layer, service layer, data layer, and infrastructure layer. The overall design of the system architecture is shown in Figure 2.

5.2

System Functional Design

The functional modules of the system are divided into user modules: login, registration, and personal center.Resource module: resource upload, resource rating, resource recommendation, resource search.Background management module: user management and resource management.Functional design should clarify the module classification and division of labor, ensure that each module is independent of each other, and facilitate system maintenance. The functional module structure diagram is shown in Figure 3.

6

Experimental analysis and validation of recommendation algorithms

6.1

Selection of experimental evaluation indicators

In this paper, the task about recommendation is to recommend the competitions for the group that match its dimensions in knowledge and ability and are interested in them, and the Top-N recommendation list is generated by the model of this paper, in order to validate the effectiveness of this recommendation list, this paper adopts the three evaluation metrics of Hit Rate (HR), Normalized Discounted Cumulative Gain (NDCG), and Mean Reverse Ranking (MRR) to assess the accuracy of the recommendation list.

The hit rate HR is used to measure the recall rate, which serves to measure the accuracy of the recommendation results, and the larger its value is, the higher the accuracy is, and its calculation formula is shown in (10): (10) $H R @ N = \frac{N u m o f H i t @ N}{| G T |}$ \[HRN=\frac{Num\;of\;HitN}{|GT|}\] where |GT| denotes the number of items in all test sets and Num of Hit @ N denotes the intersection of the first N recommended items with the items in the test set.

The Normalized Discounted Cumulative Gain NDCG refers to the pair is used to measure the ranking quality of the recommendation list, which is calculated as shown in (11).

(11)

{\begin{matrix} N D C G @ N = \frac{D C G @ N}{I D C G @ N} \\ D C G @ N = \sum_{k = 1}^{N} \frac{2^{r e l_{2}} - 1}{l o g_{2} (k + 1)} \\ I D C G @ N = \sum_{k = 1}^{N} \frac{1}{l o g_{2} (k + 1)} \end{matrix}

\[\left\{ \begin{matrix} NDCGN=\frac{DCGN}{IDCGN} \\ DCGN=\sum\limits_{k=1}^{N}{\frac{{{2}^{re{{l}_{2}}}}-1}{lo{{g}_{2}}\left( k+1 \right)}} \\ IDCGN=\sum\limits_{k=1}^{N}{\frac{1}{lo{{g}_{2}}\left( k+1 \right)}} \\ \end{matrix} \right.\]

where the ideal case of DCG is IDCG, rel_k denotes the relevance of the recommendation result at position k in the recommendation list, and rel_k is 1 if hit and 0 otherwise.

The Mean Reverse Rank MRR denotes the reverse of the ranked position in the recommendation list of the item that the learner chooses to interact with, and is calculated as shown in (12).

(12)

M R R @ N = \frac{1}{r a n k_{k}}

\[MRRN=\frac{1}{ran{{k}_{k}}}\]

6.2

Experimental Comparison Algorithm Selection

In terms of verifying the effectiveness and excellence of the models in this paper, one recommendation fusion method (NCF-Nash) and three model fusion methods (AGREE, AFMGR, HINGR) are selected for comparison, which are described as follows.

1) NCF-Nash: Combining the implicit semantic model with the multilayer perceptual machine, the LFM_MLP model is proposed to realize the collaborative filtering recommendation between users and items, and a Nash equilibrium-based fusion strategy is proposed to fuse the recommendation scores of each user.

2) AGREE: Attention network is used to learn the representation of groups to determine the decision weights of users, then NCF is used to model the interaction between groups and items, and finally the interaction data between users and items are integrated into AGREE, so as to achieve a better recommendation effect.

3) AFMGR: A group recommendation model based on Attention Factor Decomposition Machine (AFDM), which captures the importance of the interactions between each user by incorporating AFDM, and solves the problem of group preference fusion by weighting the attention weighted sum of the interactions as a group representation.

4) HINGR: In order to solve the problem of recommending its learning resources to the competition group, etc., a heterogeneous information network for online learning of the competition group is constructed, which adopts a random wandering strategy to obtain relevant information such as junctions, calculates the weight of different meta-paths by using the attention mechanism, and finally completes the scoring prediction by utilizing the matrix decomposition.

6.3

Experimental results and analysis

The main work in this section is to compare the four comparison algorithms selected in the previous section with the recommendation algorithm proposed in this paper with respect to the overall performance, in order to verify the effectiveness and excellence of the recommendation algorithm proposed in this paper. In order to ensure the fairness of the comparison, the embedding dimension d of the methods or models involved in the experiments are set to 32, and considering that recommending too many items for the users reduces their satisfaction, the recommendation list N is set to 6-20 for the experiments. Fig. 4, Fig. 5 and Fig. 6 represent the performance of different methods or models in the evaluation indexes HR@N, NDCG@N and MRR@N, respectively, and Table 1 represents the comparison of the results of different methods or models in the evaluation indexes HR@10, NDCG@10 and MRR@10. From Figures 4, 5, 6 and Table 1, the following conclusions can be observed and obtained:

(1) The recommendation algorithm proposed in this paper outperforms other algorithms on the evaluation metrics HR@N, NDCG@N and MRR@N when the recommendation list N is set to whatever value, so it can be proved that the recommendation algorithm in this paper is effective and excellent.

(2) Compared with the AFMGR model with the best performance in the comparison algorithm, the recommendation algorithm in this paper improves the evaluation index HR@10, NDCG@10 and MRR@10 by 7.57%, 7.53% and 11.98%, respectively, indicating that the recommendation algorithm in this paper is more conducive to improving the effect of ideological and political case recommendation in entrepreneurship and entrepreneurship courses.

Table 1.

The performance of the parties in the n= 10 is compared

Method	Evaluation index
Method	HR@10	NDCG@10	MRR@10
NCF-Nash	0.4974	0.3531	0.2527
AGREE	0.5317	0.3836	0.2809
AFMGR	0.5684	0.4341	0.3356
HINGR	0.5626	0.4198	0.3125
This method	0.6114	0.4668	0.3758

7

Analysis of teaching practices and effectiveness

In order to verify the teaching effect of the above constructed “Internet+” competition to promote the realization of the pathway of ideological and political education, relying on the school competition, provincial competition and national competition of the “Internet+” competition, a part of the practical projects of the competition is taken as the pilot practical projects, and the teaching practice of the pathway of ideological and political education is carried out on them. Civic and political education realization pathway teaching practice. At the end of the teaching practice, questionnaire survey, award comparison and classroom teaching evaluation analysis are carried out on the students of the practice project team to evaluate and analyze the teaching effect of promoting the realization of the pathway of Civic and Political education from the perspectives of maturity of innovation and entrepreneurship projects, students’ satisfaction with the Civic and Political Case Recommendation System for Dual-Creation Courses, and students’ satisfaction with their own development, and so on. At the same time, through the course teaching practice, we can further accurately locate the teaching objectives of the course, improve the teaching mode of Dual-Creation Course Civic and Political Case Recommendation System, and continuously promote the improvement of the teaching quality of Dual-Creation Course Civic and Political Case Recommendation System.

7.1

Project Maturity Design and Measurement

The assessment level of the concept of technology maturity level (TRL) has been gradually subdivided and the assessment details have been gradually optimized, and the technology maturity has been formally classified into 9 levels, which has been generally recognized by various industries and has been applied to many fields such as aerospace, food, pharmaceuticals, manufacturing, and so on. In this context, drawing on the maturity scale of college students’ innovation and entrepreneurship projects, this paper establishes the Maturity Measurement Scale of College Students’ Innovation and Entrepreneurship Projects as shown in Table 2. The maturity of innovation and entrepreneurship project includes 10 grades, giving the assessment standards and assessment basis, and the assessment content mainly refers to the general path of college students’ participation in innovation and entrepreneurship practical training, which includes generating innovative ideas, reviewing literature, forming a team, designing an experimental research plan, carrying out experimental research, analyzing the data, outputting theses, patents, and winning awards of innovation and entrepreneurship competitions, etc., which is used to assess the effectiveness of the dual-creation curriculum of the Civics and Politics of Students’ Innovation and Entrepreneurship. The case recommendation system is used to evaluate the effectiveness of the cultivation of students’ innovation and entrepreneurship through ideological education.

Table 2.

Innovation entrepreneurship project maturity evaluation table

Maturity	Evaluation criteria	Evaluation basis
1.0	According to the signature results of the innovation and entrepreneurship project, the specific products were produced and the initial form of the company was initially formed	The product, the declaration of the company related materials
0.9	One of the iconic results, such as papers, patents and competition, has some influence	The paper is submitted or received, the patent acceptance certificate, the competition award certificate, the meeting report is selected, the paper is quoted, the new report is also reported
0.8	A complete paper, patent, work, etc	The paper is finalized, the patent application material, or the relevant photos and video materials of the work
0.7	The first draft of the paper, including the literature review, solution and experimental results, is the first draft of the paper (or the first draft), or the prototype of the work	First draft of the paper, first draft of the patent, sample picture
0.6	According to the book of work, the scientific method is used to analyze the data in a comprehensive way, and achieve the results of innovation, and achieve the desired results	The results of the experiment or survey report, including data chart
0.5	In this paper, we conducted several experiments or surveys, adopted scientific methods, made preliminary data collation and analysis, and produced plans for further experiments or investigations	Experiment or survey record, data chart, next plan
0.4	Preliminary experiment, test and number survey of project innovation and record some data	Test or investigation record
0.3	Select the development path of the project, and the students will be prepared for the experiment in the course of the project, including the platform, the site, the equipment, the raw material, the funds, etc	Photos, invoices
0.2	The students’ team has been formed to obtain the teachers’ effective guidance, demonstrate the project goals and feasibility, and specify the division of labor and the formation of the declaration	Project declaration
0.1	Make innovative ideas and complete the literature	Bibliography and review

7.2

Analysis of practical effects

Since the use of the dual innovation course ideology and politics case recommendation system, the overall award-winning situation of a college’s participation in the provincial, national, institutional and school competitions of the “Internet+” competition is analyzed.In 2023, 6,000 students participated in the competition, and 1,800 projects were submitted, which is a new record high in terms of the number of participants and the number of projects. The college put the dual-creation course Civics case recommendation system into teaching in 2021, and the awards from 2016 to 2023 are shown in Figure 7.The stability of the number of awards from 2016 to 2020 was not good, fluctuating, and the overall number of awards was small, and the number of gold awards won was no gold medal won except for a gold medal won in 2020.After 2020, it took the initiative to change the innovation and entrepreneurship Civic and politics Cultivation of ideas, focusing on the dual-creation course Civic Case Recommendation System put into teaching, the maturity of their innovation and entrepreneurship project assessment, to promote the quality of innovation and entrepreneurship projects, and to help promote the landing of high-quality innovation and entrepreneurship projects. In the past three years, focusing on the cultivation of key innovation and entrepreneurship projects and doing the maturity assessment of innovation and entrepreneurship projects, gold awards have been won every year, and the results have been constantly improved. In 2023, 4 gold awards were won, 6 silver awards were won, and 5 bronze awards were won, and the number of awards exceeded the record of awards won in the previous years, which illustrates that the Dual-Creation Curriculum Civics and Politics Case Recommendation System has a supplementary enhancement effect on the innovation and entrepreneurship ability of the students.

In addition to the comparison of the above awards, the teaching quality questionnaire was also evaluated from the perspectives of students’ satisfaction with the Dual-Creative Curriculum Civic and Political Case Recommendation System in their learning practice and their satisfaction with their own development. In the design of the satisfaction questionnaire evaluation, each index is full of 10 points, and the questionnaire evaluation was carried out on the students of the project team who put the dual-creation course Civic and Political Case Recommendation System into teaching and implementation in the past year, and the students’ satisfaction with the dual-creation course Civic and Political Case Recommendation System in learning and practice every month is shown in Table 3, and the situation of satisfaction with their own development is shown in Table 4.

Table 3.

Students’ satisfaction with the dual creation course

Month	perfection	comprehensiveness	Keep up with	Optimization of element design	Average score
January	7.58	7.68	8.13	8.22	7.90
February	7.62	7.89	8.16	8.29	7.99
march	7.48	7.95	8.41	8.41	8.06
April	7.68	8.12	8.56	8.45	8.20
may	7.89	8.23	8.74	8.62	8.37
June	8.25	8.46	8.82	8.63	8.54
July	8.36	8.59	8.91	8.81	8.67
August	8.59	8.84	9.06	8.89	8.85
September	8.88	8.96	9.08	9.07	9.00
October	9.12	9.15	9.12	9.26	9.16
November	9.25	9.24	9.28	9.38	9.29
December	9.33	9.46	9.39	9.42	9.40

Table 4.

Students’ satisfaction with their own development

Month	Basic knowledge	Business plan writing	Project road show	Project practice	Ability to ascend	Average score
January	7.08	7.55	7.72	8.15	8.29	7.76
February	7.14	7.61	7.91	8.17	8.26	7.82
march	7.5	7.69	8.01	8.44	8.36	8.00
April	7.9	7.68	8.13	8.55	8.49	8.15
may	7.9	7.89	8.26	8.74	8.58	8.27
June	8.9	8.19	8.44	8.78	8.57	8.58
July	8.88	8.38	8.67	8.95	8.82	8.74
August	8.47	8.61	8.83	9.02	8.96	8.78
September	9.73	8.9	9.01	9.06	9.07	9.15
October	8.95	9.11	9.2	9.11	9.24	9.12
November	9.2	9.28	9.25	9.3	9.31	9.27
December	9.29	9.33	9.47	9.36	9.44	9.38

From Table 3 and Table 4, it can be seen that students’ satisfaction with the dual-creation course Civic and Political Case Recommendation System and satisfaction with their own development are improving as a whole, and students’ satisfaction with the dual-creation course Civic and Political Case Recommendation System and their satisfaction with their own development reached 9.4 points and 9.12 points in December, which is at a higher level, respectively. Among them, students’ satisfaction with the dual-creation course Civic and Political Case Recommendation System in learning practice reflects the teaching effect of the system, and students’ satisfaction with their own development reflects the effect of Civic and Political Parenting. With the perfection of the content of the relevant civic cases in the dual-creation course civic and political case recommendation system, the comprehensiveness of the civic and political cases, the advancement of the civic and political cases with the times and the continuous optimization of the design of civic and political elements, the students’ satisfaction with the dual-creation course civic and political case recommendation system is improving. At the same time, in the case study and innovation and entrepreneurship practice exercise, students’ innovation and entrepreneurship awareness is increasing, innovation and entrepreneurship ability is improving, and students’ satisfaction with their own development is getting higher and higher. The higher evaluation scores indicate that students are more satisfied with their personal performance in the dual-creation course Civics and Politics case study and innovation and entrepreneurship practice exercise, and have higher recognition of themselves, thus reflecting the effectiveness of the system design.

8

Conclusion

In this paper, we constructed a dual-creation course civic case recommendation system, opened up a new path for the integration of civic education and dual-creation education, and carried out the practice of applying the dual-creation course civic case recommendation system to teaching in combination with the actuality of education.

1) Regardless of what value of N is set, the recommendation algorithm proposed in this paper outperforms several other algorithms on the evaluation indexes HR@N, NDCG@N and MRR@N. Compared with the best-performing AFMGR model, the recommendation algorithm in this paper has improved on the evaluation indexes HR@10, NDCG@10 and MRR@10, and the improvement effect is most significant on the MRR@10 evaluation indexes, with a degree of improvement of 11.98%, which illustrates that the recommendation algorithm of this paper has an excellent performance in recommending resources in the Civic and Political case base of the dual-creation course.

2) Before the system is put into use, the number of awards won by experimentally selected universities in major competitions is small and unstable. After the system was put into use, gold medals were awarded every year from 2021 to 2023, and the performance improved year by year, and a total of 15 medals were awarded in 2023, breaking the record of awards in previous years. It shows that the dual-creation course Civics Case Recommendation System makes students’ innovation and entrepreneurship abilities reach a new breakthrough.

3) Students’ satisfaction with the dual-creation course Civics and Politics case recommendation system and their own development showed an upward trend in FY2023, and in December, their satisfaction with the recommendation system and their satisfaction with their own development reached 9.4 points and 9.12 points respectively. The high evaluation scores indicate that the students are satisfied with their personal performance in the learning and practical activities of the Civic and Political Cases recommended by the dual-creation course and their own recognition, reflecting the effectiveness of the recommendation system designed in this paper, which opens up a new way of thinking about the integration of the Civic and Political Education and the dual-creation education.

Funding:

This research was supported by the Sponsored by Natural Science Foundation of Xinjiang Uygur Autonomous Region (2024D01B15).

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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Research on the Mechanism and Path of the Integration of Digital-Enabled Civic and Political Education in Colleges and Universities with Dual Innovation Education

Jinfeng Zhang

Xinwei Song

Xu Chen

Published Online: Mar 19, 2025

Received: Oct 28, 2024

Accepted: Feb 04, 2025

DOI: https://doi.org/10.2478/amns-2025-0364

KeywordsCollaborative education, Civic and political case recommendation, Penalty factor, Hybrid recommendation algorithm

© 2025 Jinfeng Zhang et al., published by Sciendo

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

Keywords
Collaborative education, Civic and political case recommendation, Penalty factor, Hybrid recommendation algorithm