# Research on Dynamic Design of Ideological and Political Education Based on Intelligent Algorithm

Authors: Youming Cheng
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The digital civilization of human-computer coexistence has become a reality, thanks to the intelligent era. The widespread application of artificial intelligence technology in education has paved a new road for the collaborative and innovative development of ideological and political science teachers in universities and schools. In response to the problems of irregular, unprofessional, and unified resource sharing channels, duplicated, inverted, and disconnected teaching contents, and uncoordinated, insufficient, and unbalanced scientific research linkages in the integration process of university and college civics courses, the B/S three-layer architecture framework is used to build a university and college civics course teachers’ collaborative innovation platform that supports both PC and cell phone use by using jQuery, my structured query language (MySQL), hypertext preprocessor (PHP), juvenile sex offender assessment protocol (J-SOAP) crawlers, and other technologies. Teachers of civics courses can use the platform as a collaborative innovation tool. The goal of integrating resources, teaching, and scientific research of university and college civics courses is achieved, and the collaborative innovation development of university and college Civics teachers is promoted from theory to practice, thanks to the development of three major functions of the platform, namely, resource sharing, scientific research exchange, and teaching mutual assistance.


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## Body

## 1. Introduction

At the age of algorithms, ideologically motivated and political and social teaching in colleges is confronted with a number of difficulties. It is necessary to deal with the personalization of algorithmic suggestions in the technology sector, as well as the personalization of algorithmic recommendations in the social field. The problem is that conventional ideological and political education has been globalized, which presents a hurdle. When it comes to the media, it is confronted with the dilemma of “generalization” by algorithmic media and marginalization by conventional media. The marginalization of conventional media is a source of contention. Specifically, we have the issue of concealing Western intellectual infiltration as well as the dispersion of mainstream ideological communication in the ideological sphere [1–5]. We are confronted with the problem of Western ideology’s subversive infiltration and fragmentation of mainstream intellectual communication in the ideological field. By utilizing algorithmic technology that is very accurate, ideological and political workers may concentrate their efforts on the “production-filter-promotion” of educational information with more precision. Tailored learning is enabled by the precise emphasis, exact force, and exact adjustment in the “production-filtering-promotion” of educational information, the accurate positioning of college students, and the personalized learning environment. Moreover, it may exactly create the training program, precisely decide the technique of education and teaching, precisely assess the effect of education, and precisely accomplish the aim of precise education, among other things. Intelligent algorithm technology is continually speeding up and changing the way content is produced, information is disseminated, and supply methods are delivered through the Internet. When algorithms are used to push information to young people, the technical logic, symbolic logic, and capital logic of algorithmic pushing work together to unconsciously shape and embed their cognitive habits, cognitive structures, and cognitive styles, causing them to activate cognitive structures in situational experiences, increase media dependence in personalized recommendations, dissipate deep cognition in fragmented infiltration, and trigger cognitive bias in “algorithmic discrimination.” In this sense, the function of ideological and political education as a tool for cognitive education must be considered in greater detail and depth. Therefore, the institutional advantages of ideological and political education should be leveraged, and the nurturing logic, humanistic care, and value leadership inherent in ideological and political education should be used to counteract and mitigate the negative effects of the value logic of intelligent algorithmic technology capital [6–9].Information and networking have been the key trend in educational development as current science and technology has progressed. Simultaneously, as higher education evolves from elite to popular, colleges gradually increase enrollment and expand and implement multi-campus school operations, forcing ideological and political teachers to adopt a “mobile teaching” mode in teaching practice, in which ideological and political teachers and students have less and less time to interact outside of class, and students’ consolidation and review of knowledge are entirely dependent on their own efforts.The rapid growth of intelligent information technology has taken human civilization into a technological and social area without a network, making the digital society of human-computer coexistence a reality and developing a new “field” for human cognitive and behavioral activity. With the support of intelligent algorithm technology, intelligent writing, intelligent media engine, intelligent recommendation, and intelligent distribution are spurt development, which not only accelerates the upgrade and change of Internet content production, information dissemination, and supply method but also presents “spillover effect,” rapidly sweeping the “net generation.” The influence of the Internet on the learning life of young people, especially in the process of creating their cognitive habits, cognitive structures, and cognitive styles, cannot be underestimated. Networking, digitalization, and intelligence have become the “fundamental life ingredients” of the contemporary youth. This is also the essence of the times for ideological and political education to retain righteousness and innovation [10–16].The development of synergy and innovation among teachers of the university and school courses is an important aspect of the integrated construction project of the teachers of the university and school courses. It is required not only to achieve synergy in resources, teaching, and scientific research but also to invent collaborative techniques to efficiently promote the smooth development of collaborative innovation among teachers of university and college civics courses. Various countries pay significant importance to the profound impact of artificial intelligence on education and actively encourage the deep integration of artificial intelligence and education to promote educational transformation and innovation [17–21]. Based on the new period, it is necessary to stick to the problem- and goal-oriented approach, supported by technology innovation, and develop a collaborative innovation platform for teachers of civics and political science courses in schools and universities, from resource sharing to creativity. It is imperative to design a collaborative innovation platform for teachers of college and university civics courses, develop and use three major functions from resource sharing, scientific research exchange, and teaching mutual assistance, and help China’s college and university civics course teachers to further scientific and innovative development. It will enable advanced scientific, systematic, and standardized development of collaborative innovation for teachers of political science and government courses [22–24].When a computer program is loaded into a computer in order to solve a specific issue, the output is created through a series of precise procedures, data processing, and automatic reasoning techniques. Algorithms are the building blocks of computer science, and they are utilized to solve a broad variety of issues. The algorithm is defined as a highly strategic code in the modern information society that delivers unparalleled “algorithmic power” between information and its audience. Can algorithm technology be fully utilized to promote accurate thinking and politics, realize the depth of ideological and political education in theoretical logic, practical progress, and algorithm integration, complete the technical upgrade of ideological and political education methods, and meet the growing personalized needs of college students with an accurate education mode by utilizing algorithm technology? The most important question that has to be solved in order to promote proper thought politics is how to make full use of algorithm technology in order to accomplish this goal. This is a crucial issue that warrants deeper investigation and investigation.The following is a summary of the research: Section1 contains the introduction. Section 2 discusses the challenges of ideological education in universities in the era of intelligent algorithms. Section 3 discusses the problem orientation of platform design. Section 4 discusses the framework and key technologies of platform design. Section 5 discusses decision tree-based data mining in detail. Section 6 consists of discussion. Finally, the conclusion brings the paper to a finish in Section 7.


## 2. The Challenges of Ideological Education in Universities in the Era of Intelligent Algorithms

The algorithm is more than simply a collection of computer code; it is also a collection of “agenda-setting” artificial intelligence system design, with the user serving as the focal point of the algorithm’s overall architecture. When it comes to young college students, their views are very malleable, making them particularly open to the intervention of algorithmic ideology. Students’ “residual data,” which includes implicit traces of online behavior (such as social gestures like likes, favorites, and subscriptions), is analyzed by the algorithms in order to correctly capture and identify them as college students. In order to provide customers with precisely recommended “personalized” information, the algorithm mixes digital interpersonal relationship-based cooperation with them. The algorithm may serve as both technological support for accurate political thought and a decision-making tool, depending on the situation. When applied to precise ideology, the algorithm may serve as a technical support tool as well as a tool for improving the accuracy of material distribution by different stakeholders in different domains.


### 2.1. Technical Field: Personalization of Algorithmic Recommendations and Generalization of Traditional Ideological and Political Education

In the era of big data, it is impossible for college students to spend a lot of time and energy to sift through the huge amount of information, numerous data types, and severely fragmented information, so the most effective way is to delegate the power of information brokerage to algorithms that provide “personalized” push. Based on the binary relationship between audience and information, the algorithm model collects the semi-structured or unstructured “residual data” and digital interpersonal relationships of the audience with the help of social media and other system structures and captures the value orientation and preference of the audience precisely through “profiling.” Preferences are calculated and filtered by dissecting content, leveraging popular style models to edit content that matches their interests. The “content pool” is calculated and filtered, and the audience’s favorite style model is used to edit the content that fits their interests, so as to achieve the precise matching between information distribution and audience preferences. The algorithm seems to be machine-rational code, but in fact, due to the closed development environment of the algorithm, the value orientation and interests of the algorithm subject are the same. The value orientation and interest attempts of the algorithm subject can be freely implanted in the design process, forming a “black box.” The algorithm development environment is closed; the value orientation and interest intent of the algorithm owner can be embedded in the design process at will, forming a “black box society.” Therefore, the result of personalized information customization inevitably leads to the asymmetry of information acquisition. The emergence of the data divide has led to the commercialization of the audience’s “surplus data” as a tool for the digital consumption of social services.It is the algorithm that pushes personalized and precise information for college students in order to produce pleasant information, but it also hides information on the other end of the value spectrum, which will spread rapidly on social media without self-correction, resulting in the solidification and closure of thoughts and concepts. The result is a “content pool” of value convergence that surrounds college students, creating an “information cocoon chamber” for them to learn in. Codes with the same value orientation are collected via the content-coding of social media in order to achieve a feeling of belonging and to complete the development of one’s own identity, according to the study. Due to the prejudice inherent in the raw data provided by group emotion, the material created and suggested by the algorithm will intensify the bias of college students, make them more self-aware, and establish a barrier between communities. Ideological and political education will confront the new issue of being “divided by groups with differing ideas and polarized, and it will be difficult to achieve societal agreement,” according to the World Economic Forum.At the same time, the current ideological and political education is not sensitive to the individual needs of college students, and the subjectivity of college students has not been accurately integrated into the education program. At the same time, there is no continuous and dynamic attention to different groups, and there is a lack of special surveys for college students of different grades, hobbies, regions, etc., and it fails to effectively identify the differentiated needs of different college’s students. The logical and speculative horizon of ideological and political education has not yet been extended to the horizon of students’ individual needs, is unable to integrate flexible individuals with rigorous theories, and has not yet achieved value transcendence in the individual dimension. This has led to the “one size fits all” in the formulation and implementation of traditional ideological and political education programs, which is more of a general indoctrination of “one thousand people,” and lacks precise education with hierarchical and classified design.When comparing two searches, we must convert the text into a vector space model in order to determine their similarity. Accordingly, the query phrase retrieved from the method annotation information is lowercased and divided, stop words are deleted, and the stem is extracted. The weight of each word is calculated using the term frequency-inverse document frequency (TF-IDF) algorithm, which is described in more detail below. Document frequency refers to the frequency of occurrences of a certain term in a document, normalized by the number of words in order to avoid biasing the results towards lengthy texts. For a wordtj in a specific document dj, the calculation formula of its term frequency (TF) is as follows:(1)TFi,j=ni,j∑kni,j.Among them,ni,j is the number of times the word ti appears in the file dj, and the denominator is the sum of the times of all the words in the file dj.Inverse document frequency is a measure of the general importance of words. The formula for calculating the inverse document frequency (IDF) of a particular wordti is as follows:(2)IDFi=logD1+j:ti∈dj,where |D| is the total number of documents in the corpus and j:ti∈dj is the number of documents in the corpus that contain the word ti.


### 2.2. The Media Field: The “Pan-Entertainment” of Algorithmic Media and the Traditional Marginalization of Media

In response to the widespread use of intelligent algorithm technology, algorithmic media has risen to prominence, causing a breakdown in the traditional media’s top-down communication control system, resulting in a redistribution of information sharing strength between algorithmic traditional media such as television media. Because of its smart-driven, socially oriented, and accurate communication characteristics, algorithmic media are rapidly becoming the focal point of information distribution and dissemination. Traditional media, on the other hand, which are primarily responsible for positive and negative information regulation, are confronted with serious challenges in the propaganda paradigm, such as reduced communication coverage and accuracy, weakened communication influence, and reduced conceptual guidance effectiveness, among others. Algorithmic media as commercial subjects are not included in the regulatory framework of traditional media and do not need to take the function of public opinion guidance as their main responsibility, but chase profits as their commercial nature. With the characteristics of commercial-led, autonomous operation and rapid expansion, algorithmic media can monitor the traffic changes of content in real-time through big data, obtain hot content with a surge in traffic, analyze feedback data on pushed content, shorten the response time of content, and realize the accuracy of content distribution. The digital information consumption structure of the general audience determines that the amount of pushing of pan-entertainment information by algorithmic media exceeds that of current affairs information. Driven by the commercial competition for attention economy, algorithmic media further amplify the entertainment attribute.In order to precisely increase the traffic attraction to college students, algorithmic media analyze the characteristics of college students’ “resident” or similar online communities and use digital communities with similar tastes and certain trust to recommend word-of-mouth to college students. At the same time, in order to seize the fragmented time of college students, algorithmic media precisely create a dynamic and separate “surreal” space and precisely recommend “post-truth” contents that “emphasize emotion rather than truth.” In the “post-truth” content which is discontinuous and lacks specific context, the entertainment means of flirtation and banter make some college students turn elegant and noble into the secular and amusing. They are addicted to the online live broadcast, idol support, and “super topics.” They indulge in live streaming, idol support, “super topics,” and other sensory feasts. This makes some college students weaken their thinking about human business and pursuit of ideals and beliefs in the “surreal” constructed by algorithmic media, and the pursuit of ideals and beliefs, and they are lost in an irrational, emotional, and “pan-entertainment” digital landscape.


### 2.3. Ideological Field: The Concealment of Western Ideological Infiltration and the Fragmentation of Mainstream Ideological Communication

Because college students want rapid and tailored surfing experiences, algorithms that are fragmented and precise in their filtering cater to their instantaneous and personalized browsing habits. This is in accordance with their fast-paced living patterns and their need for customization. This is due to the dispersion of discourse structure and semantics in the material generated and filtered by the algorithm. This dispersion of discourse structure and semantics subverts the conventional text-based narrative logic. This would not only result in fragmented and crowded information, making it impossible to communicate the integrity of the idea, but also result in sentimentalized appeals and emotional imprints. Fundamentally, mainstream ideology is a logically developing scientific theoretical framework that requires rigorous and methodical inquiry. When confronted with precise, massive, and rapid fragmented information, young students become “tired” of accepting it and require frequent hypertext links to relevant information in order to organize, analyze, and identify it. However, nonlinear links among fragmented information disperse the semantic expression and make it difficult to form a powerful aggregation effect. Thus, the logical field necessary for mainstream ideology’s logic will be weakened, while the linear cognitive map required for the production of mainstream ideology will be impacted as a result of this.Due to the short cognitive time and the lack of a systematic analysis process, it is easy for college students to replace the complete cognition of theories with fragmented information, which weakens the cognition and identification with mainstream ideology. According to American scholar Maxwell McCombs, in information dissemination and opinion expression, mass media can influence people’s value judgment on specific events with the help of specific agenda-setting. The content of texts set by Western media agendas dismantles the reality of the “other” and does not purely reproduce facts, but reconstructs meanings that conform to specific standards and value positions. With the advantage of algorithmic origination and the monopoly of algorithmic media, the West embeds Western ideology into algorithms to increase the precision and concealment of infiltration into China. If public opinion is favorable to the West’s ideological propaganda offensive, the West will frame its value orientation and cognitive laws in the content through agenda setting and use algorithmic fragmentation to filter China’s mainstream ideological content. At the same time, the West uses Internet content providers to produce agenda-setting content that meets the preferences of different network clusters, creating an absolute advantage in the number of content releases and rapidly occupying the social media topic boards that feature “microblog topics + interest content communities.” In the one-way, closed “filter bubble” constructed by the West, the inertia of “define first, understand later” largely influences the opinions, attitudes, and value judgments of college students in the same network cluster on events.


## 2.1. Technical Field: Personalization of Algorithmic Recommendations and Generalization of Traditional Ideological and Political Education

In the era of big data, it is impossible for college students to spend a lot of time and energy to sift through the huge amount of information, numerous data types, and severely fragmented information, so the most effective way is to delegate the power of information brokerage to algorithms that provide “personalized” push. Based on the binary relationship between audience and information, the algorithm model collects the semi-structured or unstructured “residual data” and digital interpersonal relationships of the audience with the help of social media and other system structures and captures the value orientation and preference of the audience precisely through “profiling.” Preferences are calculated and filtered by dissecting content, leveraging popular style models to edit content that matches their interests. The “content pool” is calculated and filtered, and the audience’s favorite style model is used to edit the content that fits their interests, so as to achieve the precise matching between information distribution and audience preferences. The algorithm seems to be machine-rational code, but in fact, due to the closed development environment of the algorithm, the value orientation and interests of the algorithm subject are the same. The value orientation and interest attempts of the algorithm subject can be freely implanted in the design process, forming a “black box.” The algorithm development environment is closed; the value orientation and interest intent of the algorithm owner can be embedded in the design process at will, forming a “black box society.” Therefore, the result of personalized information customization inevitably leads to the asymmetry of information acquisition. The emergence of the data divide has led to the commercialization of the audience’s “surplus data” as a tool for the digital consumption of social services.It is the algorithm that pushes personalized and precise information for college students in order to produce pleasant information, but it also hides information on the other end of the value spectrum, which will spread rapidly on social media without self-correction, resulting in the solidification and closure of thoughts and concepts. The result is a “content pool” of value convergence that surrounds college students, creating an “information cocoon chamber” for them to learn in. Codes with the same value orientation are collected via the content-coding of social media in order to achieve a feeling of belonging and to complete the development of one’s own identity, according to the study. Due to the prejudice inherent in the raw data provided by group emotion, the material created and suggested by the algorithm will intensify the bias of college students, make them more self-aware, and establish a barrier between communities. Ideological and political education will confront the new issue of being “divided by groups with differing ideas and polarized, and it will be difficult to achieve societal agreement,” according to the World Economic Forum.At the same time, the current ideological and political education is not sensitive to the individual needs of college students, and the subjectivity of college students has not been accurately integrated into the education program. At the same time, there is no continuous and dynamic attention to different groups, and there is a lack of special surveys for college students of different grades, hobbies, regions, etc., and it fails to effectively identify the differentiated needs of different college’s students. The logical and speculative horizon of ideological and political education has not yet been extended to the horizon of students’ individual needs, is unable to integrate flexible individuals with rigorous theories, and has not yet achieved value transcendence in the individual dimension. This has led to the “one size fits all” in the formulation and implementation of traditional ideological and political education programs, which is more of a general indoctrination of “one thousand people,” and lacks precise education with hierarchical and classified design.When comparing two searches, we must convert the text into a vector space model in order to determine their similarity. Accordingly, the query phrase retrieved from the method annotation information is lowercased and divided, stop words are deleted, and the stem is extracted. The weight of each word is calculated using the term frequency-inverse document frequency (TF-IDF) algorithm, which is described in more detail below. Document frequency refers to the frequency of occurrences of a certain term in a document, normalized by the number of words in order to avoid biasing the results towards lengthy texts. For a wordtj in a specific document dj, the calculation formula of its term frequency (TF) is as follows:(1)TFi,j=ni,j∑kni,j.Among them,ni,j is the number of times the word ti appears in the file dj, and the denominator is the sum of the times of all the words in the file dj.Inverse document frequency is a measure of the general importance of words. The formula for calculating the inverse document frequency (IDF) of a particular wordti is as follows:(2)IDFi=logD1+j:ti∈dj,where |D| is the total number of documents in the corpus and j:ti∈dj is the number of documents in the corpus that contain the word ti.


## 2.2. The Media Field: The “Pan-Entertainment” of Algorithmic Media and the Traditional Marginalization of Media

In response to the widespread use of intelligent algorithm technology, algorithmic media has risen to prominence, causing a breakdown in the traditional media’s top-down communication control system, resulting in a redistribution of information sharing strength between algorithmic traditional media such as television media. Because of its smart-driven, socially oriented, and accurate communication characteristics, algorithmic media are rapidly becoming the focal point of information distribution and dissemination. Traditional media, on the other hand, which are primarily responsible for positive and negative information regulation, are confronted with serious challenges in the propaganda paradigm, such as reduced communication coverage and accuracy, weakened communication influence, and reduced conceptual guidance effectiveness, among others. Algorithmic media as commercial subjects are not included in the regulatory framework of traditional media and do not need to take the function of public opinion guidance as their main responsibility, but chase profits as their commercial nature. With the characteristics of commercial-led, autonomous operation and rapid expansion, algorithmic media can monitor the traffic changes of content in real-time through big data, obtain hot content with a surge in traffic, analyze feedback data on pushed content, shorten the response time of content, and realize the accuracy of content distribution. The digital information consumption structure of the general audience determines that the amount of pushing of pan-entertainment information by algorithmic media exceeds that of current affairs information. Driven by the commercial competition for attention economy, algorithmic media further amplify the entertainment attribute.In order to precisely increase the traffic attraction to college students, algorithmic media analyze the characteristics of college students’ “resident” or similar online communities and use digital communities with similar tastes and certain trust to recommend word-of-mouth to college students. At the same time, in order to seize the fragmented time of college students, algorithmic media precisely create a dynamic and separate “surreal” space and precisely recommend “post-truth” contents that “emphasize emotion rather than truth.” In the “post-truth” content which is discontinuous and lacks specific context, the entertainment means of flirtation and banter make some college students turn elegant and noble into the secular and amusing. They are addicted to the online live broadcast, idol support, and “super topics.” They indulge in live streaming, idol support, “super topics,” and other sensory feasts. This makes some college students weaken their thinking about human business and pursuit of ideals and beliefs in the “surreal” constructed by algorithmic media, and the pursuit of ideals and beliefs, and they are lost in an irrational, emotional, and “pan-entertainment” digital landscape.


## 2.3. Ideological Field: The Concealment of Western Ideological Infiltration and the Fragmentation of Mainstream Ideological Communication

Because college students want rapid and tailored surfing experiences, algorithms that are fragmented and precise in their filtering cater to their instantaneous and personalized browsing habits. This is in accordance with their fast-paced living patterns and their need for customization. This is due to the dispersion of discourse structure and semantics in the material generated and filtered by the algorithm. This dispersion of discourse structure and semantics subverts the conventional text-based narrative logic. This would not only result in fragmented and crowded information, making it impossible to communicate the integrity of the idea, but also result in sentimentalized appeals and emotional imprints. Fundamentally, mainstream ideology is a logically developing scientific theoretical framework that requires rigorous and methodical inquiry. When confronted with precise, massive, and rapid fragmented information, young students become “tired” of accepting it and require frequent hypertext links to relevant information in order to organize, analyze, and identify it. However, nonlinear links among fragmented information disperse the semantic expression and make it difficult to form a powerful aggregation effect. Thus, the logical field necessary for mainstream ideology’s logic will be weakened, while the linear cognitive map required for the production of mainstream ideology will be impacted as a result of this.Due to the short cognitive time and the lack of a systematic analysis process, it is easy for college students to replace the complete cognition of theories with fragmented information, which weakens the cognition and identification with mainstream ideology. According to American scholar Maxwell McCombs, in information dissemination and opinion expression, mass media can influence people’s value judgment on specific events with the help of specific agenda-setting. The content of texts set by Western media agendas dismantles the reality of the “other” and does not purely reproduce facts, but reconstructs meanings that conform to specific standards and value positions. With the advantage of algorithmic origination and the monopoly of algorithmic media, the West embeds Western ideology into algorithms to increase the precision and concealment of infiltration into China. If public opinion is favorable to the West’s ideological propaganda offensive, the West will frame its value orientation and cognitive laws in the content through agenda setting and use algorithmic fragmentation to filter China’s mainstream ideological content. At the same time, the West uses Internet content providers to produce agenda-setting content that meets the preferences of different network clusters, creating an absolute advantage in the number of content releases and rapidly occupying the social media topic boards that feature “microblog topics + interest content communities.” In the one-way, closed “filter bubble” constructed by the West, the inertia of “define first, understand later” largely influences the opinions, attitudes, and value judgments of college students in the same network cluster on events.


## 3. Problem Orientation of Platform Design

Adhering to the problem orientation is a distinctive feature of Marxism, and the problem is the beginning and source of innovation. The design of the collaborative innovation platform for teachers of ideological and political courses in universities and middle and primary schools needs to start from the problem. Peter Gloor of the Massachusetts Institute of Technology first pointed out that “collaborative innovation” refers to a networking group composed of self-motivated personnel to form a collective vision, exchange ideas, information, and work status through the network, and cooperate to achieve common goals. At present, teachers of ideological and political courses in universities and middle and primary schools have not yet formed a collaborative innovation and development community due to irregular, unprofessional, and inconsistent resource sharing channels, repetitive, upside-down, and out-of-touch teaching content, and uncoordinated, insufficient, and unbalanced scientific research linkages. Only by discovering problems can we grasp the focus of solving problems and comprehensively promote the collaborative innovation and development of teachers of ideological and political courses in universities and middle and primary schools, and we need to strengthen problem orientation.


### 3.1. Resource Sharing Channels Are Not Formal, Professional, and Uniform

Most colleges and universities have established application facilities specifically for resource inquiry and acquisition through the use of network technology and other means, which serve as prerequisites for resource sharing among teachers of civics and political science courses in universities and secondary schools, among other purposes. Examples include the provision of paper and audio-visual resources by libraries in addition to the establishment of information service desks to make it easier for teachers and students to access information and materials. Teachers of civics and political science classes at Chinese universities, schools, and colleges continue to face challenges such as being unstructured, unprofessional, and uncoordinated, despite the existence of resource-sharing channels. An informal term implies that an educational platform has not yet been developed for use by teachers in colleges, universities, and secondary schools. The majority of teachers continue to use applications such as Baidu.com, WeChat, and QQ to exchange resources such as textbooks, teaching aids, and courseware with their students. These apps merely give means for sharing information, not resources. Teachers who have resources share them with other teachers who require them through these apps, making the process of resource exchange hard and time-consuming to locate, download, and save resources. If the channel for exchanging resources is unprofessional, it signifies that there is only one amateurish in nature. The majority of currently available resource-sharing apps have a jumbled layout and a plethora of features, such as entertainment news, games, and adverts that do not address the primary demands of instructors in terms of resource sharing. When we say there is a lack of uniformity, we are referring to the fact that there is no formal channel through which all of the teaching materials, courseware, publications, and literature for civics courses in schools and universities may be centralized. In addition, there is a lack of a centralized official channel that can centralize all teaching materials, textbooks, writings, literature, and other resources for instructors to use in a cohesive manner, as well as a lack of targeted and comprehensive construction.


### 3.2. Repetition, Backwardness, and Disconnection of Teaching Contents

The integration of teaching contents of the civics and political science courses in universities and colleges has been quite effective, but some teaching contents still present the problems of repetition, inversion, and disconnection. The repetition is manifested in the crossover of knowledge points and content coverage in some teaching contents of the civics and political science courses in universities and colleges. The lack of overall planning in the preparation of teaching materials has led to a lack of “step-by-step” course content among the various school sections. Some unavoidable repetitive contents in the teaching contents of the higher levels are not the extension and deepening of the contents of the lower levels, but the meaningless mechanical repetition. Content inversion is manifested as a bad learning situation caused by the content of school and university civics courses not in line with the physical and mental development of students and the content going backward or forward. Students struggle to understand and absorb the course information in civics class because it is based on abstract notions and theories. University civics class is talking about specific norms and codes of conduct; the content lacks theoretical depth. The disconnect is manifested in the lack of transition or discontinuity between the teaching contents of the civics and political science classes in schools and universities. There are big differences in the difficulty of the content and teaching methods of the civics courses in each school section, and some of the contents are not smoothly connected. Students' knowledge understanding is not in place, and teachers of civics courses in each school section do not understand the teaching content and learning situation of other school sections, resulting in the lack of articulation of some course content in the teaching process, which is not conducive to students’ sustainable learning development.


### 3.3. Research Linkage Is Uncoordinated, Inadequate, and Unbalanced

The linkage of scientific research among teachers of university and college civics is an important link in the development of collaborative innovation of the teaching team, without scientific research linkage, not to mention collaborative innovation, and without collaborative innovation, it is difficult to realize the integration of university and college civics construction. The current scientific research linkage process presents the problem of uncoordinated, insufficient, and unbalanced. Teachers in higher education have scientific research resources, and there is a phenomenon of emphasizing scientific research over teaching. Primary and secondary school teachers, on the other hand, are more concerned with the improvement of teaching ability, and academic research is relatively lacking. The scientific research resources of colleges and universities cannot be shared with primary and secondary schools, and it is difficult for college teachers to make great progress.The research linkage is far from adequate. There is also a lack of academic exchange and research interaction between primary and secondary school teachers. The development of scientific research is extremely unbalanced between east and west and between provinces and cities. As we all know, it is difficult to innovate in teaching activities without theoretical guidance, and teaching and research are the “two wings” of teachers. Teaching and research are the “two wings” of a teacher’s life, and they are indispensable. Needless to say, the scientific research ability of teachers of civics and political science in universities and schools is relatively weak compared with teachers of other disciplines. To improve the scientific research level of this team as a whole, we must pay more attention to can linkage and strive to complement the advantages between the teachers of civics and political science courses in schools and universities. In response to the above three major problems, a new collaborative model driven by scientific and technological innovation is designed, and a collaborative innovation platform with three major functions of resource sharing, scientific research exchange, and teaching assistance is designed. It is necessary to design a collaborative innovation platform with three major functions: resource sharing, research exchange, and mutual assistance.


## 3.1. Resource Sharing Channels Are Not Formal, Professional, and Uniform

Most colleges and universities have established application facilities specifically for resource inquiry and acquisition through the use of network technology and other means, which serve as prerequisites for resource sharing among teachers of civics and political science courses in universities and secondary schools, among other purposes. Examples include the provision of paper and audio-visual resources by libraries in addition to the establishment of information service desks to make it easier for teachers and students to access information and materials. Teachers of civics and political science classes at Chinese universities, schools, and colleges continue to face challenges such as being unstructured, unprofessional, and uncoordinated, despite the existence of resource-sharing channels. An informal term implies that an educational platform has not yet been developed for use by teachers in colleges, universities, and secondary schools. The majority of teachers continue to use applications such as Baidu.com, WeChat, and QQ to exchange resources such as textbooks, teaching aids, and courseware with their students. These apps merely give means for sharing information, not resources. Teachers who have resources share them with other teachers who require them through these apps, making the process of resource exchange hard and time-consuming to locate, download, and save resources. If the channel for exchanging resources is unprofessional, it signifies that there is only one amateurish in nature. The majority of currently available resource-sharing apps have a jumbled layout and a plethora of features, such as entertainment news, games, and adverts that do not address the primary demands of instructors in terms of resource sharing. When we say there is a lack of uniformity, we are referring to the fact that there is no formal channel through which all of the teaching materials, courseware, publications, and literature for civics courses in schools and universities may be centralized. In addition, there is a lack of a centralized official channel that can centralize all teaching materials, textbooks, writings, literature, and other resources for instructors to use in a cohesive manner, as well as a lack of targeted and comprehensive construction.


## 3.2. Repetition, Backwardness, and Disconnection of Teaching Contents

The integration of teaching contents of the civics and political science courses in universities and colleges has been quite effective, but some teaching contents still present the problems of repetition, inversion, and disconnection. The repetition is manifested in the crossover of knowledge points and content coverage in some teaching contents of the civics and political science courses in universities and colleges. The lack of overall planning in the preparation of teaching materials has led to a lack of “step-by-step” course content among the various school sections. Some unavoidable repetitive contents in the teaching contents of the higher levels are not the extension and deepening of the contents of the lower levels, but the meaningless mechanical repetition. Content inversion is manifested as a bad learning situation caused by the content of school and university civics courses not in line with the physical and mental development of students and the content going backward or forward. Students struggle to understand and absorb the course information in civics class because it is based on abstract notions and theories. University civics class is talking about specific norms and codes of conduct; the content lacks theoretical depth. The disconnect is manifested in the lack of transition or discontinuity between the teaching contents of the civics and political science classes in schools and universities. There are big differences in the difficulty of the content and teaching methods of the civics courses in each school section, and some of the contents are not smoothly connected. Students' knowledge understanding is not in place, and teachers of civics courses in each school section do not understand the teaching content and learning situation of other school sections, resulting in the lack of articulation of some course content in the teaching process, which is not conducive to students’ sustainable learning development.


## 3.3. Research Linkage Is Uncoordinated, Inadequate, and Unbalanced

The linkage of scientific research among teachers of university and college civics is an important link in the development of collaborative innovation of the teaching team, without scientific research linkage, not to mention collaborative innovation, and without collaborative innovation, it is difficult to realize the integration of university and college civics construction. The current scientific research linkage process presents the problem of uncoordinated, insufficient, and unbalanced. Teachers in higher education have scientific research resources, and there is a phenomenon of emphasizing scientific research over teaching. Primary and secondary school teachers, on the other hand, are more concerned with the improvement of teaching ability, and academic research is relatively lacking. The scientific research resources of colleges and universities cannot be shared with primary and secondary schools, and it is difficult for college teachers to make great progress.The research linkage is far from adequate. There is also a lack of academic exchange and research interaction between primary and secondary school teachers. The development of scientific research is extremely unbalanced between east and west and between provinces and cities. As we all know, it is difficult to innovate in teaching activities without theoretical guidance, and teaching and research are the “two wings” of teachers. Teaching and research are the “two wings” of a teacher’s life, and they are indispensable. Needless to say, the scientific research ability of teachers of civics and political science in universities and schools is relatively weak compared with teachers of other disciplines. To improve the scientific research level of this team as a whole, we must pay more attention to can linkage and strive to complement the advantages between the teachers of civics and political science courses in schools and universities. In response to the above three major problems, a new collaborative model driven by scientific and technological innovation is designed, and a collaborative innovation platform with three major functions of resource sharing, scientific research exchange, and teaching assistance is designed. It is necessary to design a collaborative innovation platform with three major functions: resource sharing, research exchange, and mutual assistance.


## 4. The Framework and Key Technologies of Platform Design

The design of the platform from function to use must include the main contents of authorization registration, resource sharing, research exchange, teaching mutual assistance, and personal information management. Authorized registration and personal information management are the basic functions of the platform, and resource sharing, research exchange, and teaching mutual assistance are the core contents of the design. The overall design framework is shown in Figure1.Figure 1
The framework and key technologies of the platform.The collaborative innovation platform for teachers of civics and political science courses in universities and schools is developed on a personal computer (PC) and cell phone. The front-end code uses PHP language, the back-end uses cloud development technology, and the data management uses MySQL + PHP to realize. The platform is used for the integrated environment PHP modules, Apache modules, and Wampserver modules for MySQL development. The control layer mainly interacts with the view layer and model layer information, and jQuery + PHP programming is used to complete the interface display in the view layer. The view layer mainly interacts with the user interface, using jQuery controls in the interface to complete the interface implementation, in addition to the use of asynchronous JavaScript and XML (AJAX) technology to achieve the timer refresh function. The database classes provided by MySQL are used in the model layer to manage the business logic, such as adding, deleting, and viewing user information. WeChat applet side uses J-SOAP crawler technology to find the required services through the browser developer tools. The application uses J-SOAP crawler technology to find the interface address of the required service through the browser developer tool and then logs into the collaborative innovation platform for teachers of civics and political science classes in universities and colleges through simulation. Then, by simulating the login to the collaborative innovation platform for teachers of university and college civics courses, we can query and use the information of textbooks, teaching aids, courseware, meetings, and courses. Goldfarb School of nursing (GSON) is used to extract key information from the returned raw JavaScript Object Notation (JSON) data and convert it into refined information. GSON is used to extract key information, convert it into a streamlined format, and display it on the front-end.


## 5. Decision Tree-Based Data Mining

Data mining is an important means to achieve full coverage of talent training elements in higher education institutions, improve the real-time and authenticity of diagnosis and reform results, and further improve the internal quality assurance system.In solving the classification problem, the algorithm with a better effect and wider range is the decision tree algorithm (DTA). The main algorithm used to solve the classification problem is the decision tree algorithm (ID3 algorithm for short). The ID3 algorithm, proposed by Quinlan, is a top-down inductive algorithm for information gain. It is a top-down induction algorithm with information gain, which is mainly used to solve the problem of discretized data values and is robust to erroneous data. The main feature is that the decision tree is generated by a “divide and conquer” strategy. The entire training sample data set is partitioned into mutually exclusive subsets, and at each node of the decision tree, the information gain of the maximized information gain of the data set as a heuristic to decide the selection of a decision tree expansion operation is performed on each node of the decision tree using the maximized information gain of the data set as a heuristic to select an attribute.The basic principle of the Iterative Dichotomiser 3 (ID3) algorithm is as follows: a subsetE is known where the subset E contains p positive examples and n negative examples so that the probability that an example belongs to the set of positive examples (PE) can be obtained as p/p+n, and by the same principle, the probability that an example belongs to the set of counterexamples (NE) that is n/p+n, a decision tree is equivalent to a positive example set and a negative example set, and the required information entropy is(3)Ip,n=−pp+nlogpp+n−np+nlognp+n.There is a genusA, where A takes Y different values, which yields the subset {A1, A2, …, AY}, which divide the subset E into Y subsets pN1,pN2,…,pNY, assuming that Ei has pi positive cases and ni negative cases, the required expectation of Ei can be derived as Ipi,ni, and the required expectation of the decision tree rooted at A is the weighted average of the required expectation of each subset as(4)EA=∑i=1Api+niP+NIpi,ni.And the enrichment information is(5)GainA=Ip,n−EA.In the mining data platform, the data volume of each record is huge, and the corresponding attribute values are relatively large. ID3 algorithm works better for discrete attributes, but it is difficult to deal with continuous attributes, which generally need to be discretized through preprocessing before they can be processed. The ID3 algorithm is suitable for the case where all the attribute values are determined, but in the real situation there are often some blank or missing values; when the ID3 algorithm is applied, the algorithm will automatically skip the blank data, and it will not be able to build a complete model well when data mining is performed. The ID3 algorithm often selects attributes with more values as test attributes, which will also affect the efficiency of decision tree generation.We replace the entropy valueE of attribute A with the simplified information entropy S.SA Contains addition, multiplication, and division operations in SA, and the operation time is relatively shorter than that of multiple logarithmic operations in EA, which is much shorter than the time of multiple logarithmic operations in EA.(6)EA=∑i=1A2pinipi+ni.This paper collects three years of curriculum construction in an institution, including attributes such as curriculum standards, annual plan of curriculum construction, curriculum construction implementation plan, curriculum teaching objectives supporting professional training objectives, curriculum training objectives aligned with vocational positions, whether the teaching content is in line with the curriculum teaching objectives, whether the teaching content is task-driven, whether it is aligned with skills certificates, and the proportion of blended teaching hours to total hours. The prediction target is as follows: the “level” of the course has been achieved. The level of the course is divided into four categories: national course, provincial course, school-level key course, and school-level general course, with a total of 3667 records.Because the acquired data contains continuous qualities, such as missing data, unlawful data, discrepancies, and so on, preprocessing of the data is required. For continuous attributes, discrete processing is used; for example, the value of the attribute “number of teaching resources” is continuous data, and the data is discretely processed according to the need by setting the interval [0, 50] to 1, [50, 100] to 2, and so on. For discrete attributes, discrete processing is used by setting the interval [0, 100] to 1, and so on. The missing characteristics of the data are filled in by using the mean value of the data type, and discrete kinds are filled in by using the values that are the most frequent. Based on the data collected by the course collection platform, 45 attributes are selected for this paper, 80 percent of the data set is used as the training set, and 20 percent is used as the test set in accordance with the 2–8 principle, which is commonly used in machine learning to determine the ratio of the test set to the training set. Using the data obtained by the course gathering platform, we have selected 45 qualities for inclusion in this report. The training data set was created by selecting 2680 records from the original data set at random from the original data set. The training data set was created by selecting 2680 records from the original data set at random, while the remaining 671 records were used as the test data set. The test data set is comprised of the remaining 671 records from the original data set.Figure2 gives a comparison of the run times for different amounts of data on the data set. From Figure 2, it can be seen that the running time of the improved algorithm is consistently lower than that of the ID3 algorithm, and as the number of data increases, the improved algorithm saves more time as the number of data increases.Figure 2
Algorithmic computing time for different amounts of data on the data set.


## 6. Discussion

In this section, we explain all the symbols and variables in the equations in detail. In equation (1), ni,j is the number of times the word ti appears in the file dj, and the denominator is the sum of the times of all the words in the file dj. In equation (2), |D| is the total number of documents in the corpus, and j:ti∈dj is the number of documents in the corpus that contain the word ti. Then, for pi positive cases and ni negative cases, the required expectation of Ei can be derived as Ipi,ni, and the required expectation of the decision tree rooted at A is the weighted average of the required expectation of each subset. In the end, the prediction target is as follows: the “level” of the course has been achieved. The level of the course is divided into four categories: national course, provincial course, school-level key course, and school-level general course, with a total of 3667 records.


## 7. Conclusion

Time is an important factor affecting ideological and political education activities. In the background of the intelligent era, the collaborative innovation platform for teachers of ideological and political science courses in schools and colleges uses information technology and is designed with the concept of openness, sharing, interaction, and collaboration, aiming to provide one-stop services of resources, teaching, and research, break the cooperation restrictions of time, space, and distance, and break through the traditional teacher team-building mode, with the advantages of convenience, comprehensiveness, and professionalism. Using computers, smartphones, and other devices, through the website or WeChat applets to log into the collaborative innovation platform, teachers of university and college civics can read, download resources, attend meetings, and listen to lectures online. Of course, the design idea of the platform should be applied to practice, the details need to be optimized, and the awareness and ability of collaborative innovation need to be improved, so as to speed up the process of integrating the construction of university and college civics courses.This paper also analyzes the data mining platform based on the ID3 algorithm and proposes an improvement plan for the shortcomings of the ID3 algorithm in the application of intelligent recommendation systems. The improved algorithm is good at improving the accuracy of the algorithm as well as reducing the time used by the algorithm, maximizing the accuracy and timeliness of self-diagnosis improvement.



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*Source: 3904316-2022-03-27.xml*