Research ArticleA Coordinated and Optimized Mechanism of ArtificialIntelligence for Student Management by College CounselorsBased on Big Data

Zhen Yang1 and Muhammad Talha 2

1Guangdong AIB Polytechnic, Guangdong 510507, China2Department of Computer Science, Superior University Lahore, Pakistan

Correspondence should be addressed to Muhammad Talha; talhashoaibt@yahoo.com

Received 4 October 2021; Revised 11 October 2021; Accepted 30 October 2021; Published 25 November 2021

Academic Editor: Osamah Ibrahim Khalaf

Copyright © 2021 Zhen Yang and Muhammad Talha. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original workis properly cited.

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

1. Introduction

The data management of the counselor team has become anessential component of the process of professionalizingcounselors. At present, from the government to all occupa-tions give high attention to big data and start to build bigdata platforms and implement big data strategies to copewith the coming of the big data era. The construction ofbig data platforms in higher education is under develop-ment, and as an important part of ideological and politicalwork in colleges and universities, the professionalization ofcollege counselors is an important topic of ideological and

political education that needs to be studied. Strengtheningcounselors’ professionalization and trying to enhance theirworking capacity, academic level, and professional qualityare not only a personal necessity for counselors but also anessential effort to increase the efficacy of ideological andpolitical activity [1]. How to use the way of thinking of bigdata to change from the original scattered and inefficientmanagement to the centralized and efficient managementmode is an important issue in the current professionaliza-tion of counselors and a difficult problem that must be faced.Counselors are an important pillar for the growth of youngcollege students in the new era of education, and the

HindawiComputational and Mathematical Methods in MedicineVolume 2021, Article ID 1725490, 11 pageshttps://doi.org/10.1155/2021/1725490

scientific planning and construction of counselors’ profes-sional ability should also be an issue that needs to be paidattention to in the ideological and political work of collegesand universities and their team construction at present [2].The counselors are faced with diverse and changing workobjects, changing society, changing groups, changing men-tality, and changing information, and such changes bringchallenges to the work of counselors, who are no longercapable of dealing with the problems arising in the societyand work nowadays entirely by experience and perception[3]. Consequently, in the context of big data, it is necessaryto explore novel models, new ways of thinking about coun-selors’ professionalism, and new ways of professionalizingthem; the counselor’s capability as well as their quality mustchange to match the shifts in methods, situations, and thetasks of ideological work at colleges and universities. Thequality of college and university teaching is directly relatedto the quality of college instructors’ teaching abilities, andhigh levels of teaching ability among college teachers willencourage a continuous rise in the quality of college teach-ing. It is critical and essential for college instructors’ teachingabilities to improve, especially in the educational context offast growth of intelligent teaching [4].

Therefore, this paper selects the research on the strategyof improving the teaching power of college teachers underthe perspective of artificial intelligence as the object ofresearch and takes more than forty college teachers in Chinaas the research sample to conduct a questionnaire survey onthe current situation of the development of teachers’ teach-ing power and their development needs. For the teachers’level, it is aimed at understanding the realistic needs of theteachers’ group for the development of teachers’ teachingpower; for the universities’ level, it is aimed at understandingthe current development status of teachers’ teaching powerin universities. After identifying the many growth require-ments of college instructors’ teaching power, we set out todiscover an efficient strategy for enhancing college teachers’teaching power in light of the era’s evolving educationallandscape, and we produced a study report as a result. Thispaper’s goal is to use the study’s findings to help collegesand universities better adapt to changing times, bravelyimprove teachers’ teaching power, and dare to promote edu-cation and teaching quality improvement [5]. Through thefurther excavation of the theory of counselor professionali-zation construction under the background of big data, newideas and methods of using big data technology tostrengthen the scientific, intelligent, and data-based con-struction of the counselor team are proposed, which willhelp enrich the theoretical content of the professionalizationconstruction of college counselors in the new era. Theresearch on the professionalization construction of coun-selors is an important part of the theory of ideological andpolitical education in colleges and universities. Using bigdata technology, the massive amount of effective informa-tion about the development process of counselor functionsis standardized, effectively excavated, and scientifically ana-lyzed, so that it becomes the resource support for counselorsto strengthen the professionalization construction, whichprovides a very important theoretical guidance significance

[6]. Targeted research and analysis of the content, methods,and standards of counselor professionalization constructionin the era of big data, breaking the traditional concept ofprofessionalization construction, can further enrich the the-ory of ideological and political education in colleges and uni-versities and deepen the understanding of digital, scientific,and intelligent construction of college counselors, whencombined with the current situation of colleges and univer-sities. The research in this area will certainly enrich anddevelop the theory of professionalization construction ofcollege counselors. Facing the new orientation, a new jour-ney, and a new mission, the construction of teachers’ teamcannot fully adapt to the development requirements of thenew era, and the demand for the professionalization ofcounselors reflects the new requirements and standards forthe construction of college teachers’ team in the new eraand the completion of the tasks and goals of high-quality tal-ents training. College counselors in the new era are focusingon improving their professionalism and specialisation,which is an inherent need to stay close to the theme ofhigher education in the new era and shoulder the historicalmission of cultivating the new man of the times to assumethe great responsibility of national rejuvenation. It is alsocritical to improve the level of ideological and political workin the new era of colleges and universities and focus onimproving the quality of cultivating talents with moral, intel-lectual, physical, social, and aesthetic qualities.

The key to whether the ideological and political work incolleges and universities is carried out well or not lies inwhether the ideological and political work team is strongor not. Carrying out theoretical and practical research onthe professionalization of college counselors helps to furthergrasp the work rules and working mechanism of collegecounselors in the new era, continuously improves the work-ing ability and practice level of college counselors, and is aneffective measure to strengthen the construction of the coun-selor team. The generation and application of big data, animportant symbol of the new era, profoundly affect the waysand methods of counselors’ work and career development.How to use the advanced technology of big data to scientif-ically improve the construction and management effective-ness of counselors’ teams has just become an importantissue at this stage. As a necessary factor for the developmentof the times, big data is incorporated into the process ofplanning, management, and construction of counselor pro-fessionalism, which can improve the scientific and effectiveconstruction of the counselor team, help guide the practiceof professional construction of college counselors, and accel-erate the process of professional construction of counselors,and is also a necessary means and long-term mechanism tomaintain the efficient and stable and scientific developmentof college students’ ideological and political work.

2. Current Status of Research

Civics course is the main channel to build students’ ideolog-ical and political education, civics teachers are the mainresponsible for this main channel, and daily civics as themain position, counselors as the main responsible for daily

2 Computational and Mathematical Methods in Medicine

civics, civics courses, and daily civics can create the effect ofcombining. The term “collaborative education” refers tocounselors and civics instructors overlapping, negotiating,increasing their strengths, and complementing their short-comings in order to create the impact of a powerful combi-nation [7]. In order to effectively promote civics education,increasing the level of collaboration between counselorsand instructors is necessary. This will increase the impactof civics education and make it more effective and relevantand be more in accordance with the law of student develop-ment. Meanwhile, it may increase the number of skilledworkers sent to the nation and help the country’s educa-tional system grow. Diverse professionals and academicshave offered a wide range of helpful viewpoints on this sub-ject [8]. There is a more structured and comprehensive cur-riculum for moral education in colleges and universities.Teachers also pay close attention to how moral educationis integrated into other professional and public courses intheir teaching practices [9]. In Japan, national moral educa-tion emphasises “loving and respecting country symbols andcultivating good national characteristics,” which is reflectednot only in the design of course content that emphasisespatriotic colours but also in the practice of visiting redvenues and watching patriotic films. In Germany, the moraleducation of college students highlights “distinctive religiouscharacteristics and individualism” and focuses on shapingcollege students’ religious beliefs, democratic freedom,national spirit, and human rights. Although there are differ-ences in the expressions of the contents of ideological andmoral education for college students in different countries,in essence, these educational contents have profound simi-larities and consistency. Therefore, these experiences of edu-cational work in foreign universities, which have similarfunctions and contents to ideological and political education,are of great reference value for conducting this study [10].

The basic theory and practical application research havebeen carried out mainly around big data education, and amore complete analysis framework and content system havebeen formed. The effect of big data on education techniques,teaching management, teaching choices, learning analysis,feedback and assessment improvement, and the creation ofdigital libraries is all covered [11]. The United States takeseducation as one of the basic fields of big data application,promotes the strategy and implementation of big data ineducation, and not only pays attention to improving thetop-level design of big data and formulating special big dataeducation planning but also makes efforts to promote theconstruction of basic supporting facilities such as databasesand educational big data research institutions, especiallythe establishment of a state longitudinal tracking data sys-tem, which has brought into play the role of big data in stu-dent management, classroom teaching, managementdecision-making, scientific research, etc. In particular, theestablishment of the state longitudinal tracking data systemhas brought into play the systemic effects of big data on stu-dent management, classroom teaching, management deci-sion-making, and research transformation [12]. When itcomes to microschools in the U.S., they use the big data plat-form to keep track of everything about their pupils, so that

they can accurately judge their learning behavior and thelearning environment, while also helping instructors makebetter choices in the classroom [13]. The focus is on schooleducation, supplemented by family education and socialeducation. The state, the government, and unified plans areall involved in the development of school education, whichis continuously modified to meet the requirements of socialgrowth. Family education mainly serves as an initiatory edu-cation for children through parental behavior, and socialeducation includes community education and corporateeducation, which transmits the spirit of dedication, solidar-ity, and hard work. Religious education is also providedthrough the spread of religion [14].

The data collected from the valid questionnaires will becounted and charted, and the current situation and effectsof collaborative parenting between counselors and teachersof civics and political science will be analyzed through thecharts. We analyze the measures of coparenting at the uni-versity level, the private coparenting situation between coun-selors and teachers of civics courses, and the recognition ofcoparenting by both and analyze the problems of coparent-ing in universities, the reasons for the problems, and theways to improve the effectiveness of coparenting by combin-ing specific cases of coparenting. We used both the question-naires and interviews at the same time to maximise ourefficiency and provide better results. Teachers and coun-selors from different departments of the university partici-pated in the interviews to learn about their perspectives oncollaborative education between counselors and civicsteachers, as well as to better understand the issues surround-ing collaborative education between counselors and civicsteachers in colleges and universities, as well as the best waysto address those issues.

3. Optimization Mechanism of ArtificialIntelligence Coordination for StudentManagement of College Counselors withBig Data

3.1. Analysis of Artificial Intelligence CoordinatedOptimization Mechanism for Big Data.With the backgroundof cross-border integration of big data, reform, and innova-tion of ideological and political work and in-depth explora-tion of the practice of collaborative education in collegesand universities, this study is based on the study and analysisof relevant research results and important experiences in theacademic field, to grasp the current research reality and theweak points and establish the important and difficult pointsof the study [15]. We generate theoretical support for thisstudy by analyzing the theoretical system and ideologicalresources of collaborative ideological and political work,clarifying the basic connotation of collaborative ideologicaland political work in colleges and universities in the era ofbig data through the interpretation of objectives, principles,characteristics, and values, and examining the current situa-tion of collaborative ideological and political work in col-leges and universities. The aim is to cultivate the emergingvitality and endogenous momentum of the innovative

3Computational and Mathematical Methods in Medicine

development of collaborative ideological and political workin colleges and universities and promote the developmentof socialist education with Chinese characteristics in thenew era. Finally, under the technological push representedby big data, we scientifically investigated and assessed thegrowth trend of collaborative education of ideological andpolitical activity in colleges and universities [16]. Due tothe limitation of data saving and processing ability, underthe scientific data analysis method, the sampling method ofdata is to take part of sample data in the full set of dataand infer the overall characteristics of the full set of datathrough the analysis of the sample data. The amount of thesample data is usually considerably less than the completeset of data; thus, the goal of data analysis may be accom-plished at a reasonable cost. Big data provides ample possi-bilities for the storage and processing of massive data,distributed file systems, and distributed database technolo-gies, and with the support of big data, scientific analysiscan be directed entirely to the full-set data rather than tothe sample data:

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Sampling, one of the preferred methods of traditionalscientific analysis, requires a high degree of methodologicalaccuracy, but the results of its analysis come from only afraction of the full sample, and such results are subject to rel-ative error when used for the overall set of survey data. It ispossible that minor sampling mistakes may balloon into bigones once they are applied to the whole dataset. The accu-racy of sample analysis findings must be ensured in orderto keep the error within an acceptable range when appliedto the whole set of data. As a result, conventional data anal-ysis techniques place a higher priority on improving algo-rithm correctness than on increasing algorithm efficiency.To avoid this, full-sample analysis has replaced sampling inthe age of big data, ensuring that no errors have been intro-duced into the study:

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Therefore, the pursuit of high accuracy is no longer theprimary goal of sampling. To improve the quality of coun-selor professionalism in the context of big data, the mostimportant thing is to improve the active understandingand active use of big data by counselors and related manage-ment staff and to be able to make scientific decisions andplay an effective role in the work through data analysis.Based on undertaking the traditional education experience,the requirements of the subject gradually establish a workingsystem that adapts to the new situation, as shown inFigure 1.

Objectively, the information era changes counselors’professional development and working environment, neces-sitating new ways of thinking, improved cognition, andstrengthened business training and ability cultivation forcounselors confronted with digital working techniques andapproaches. The traditional ideological and political educa-tion of college students and the flat and single way of dailywork of the counselor team make it difficult to scientificallydo a good job of professional training and management ofthe counselor team at present, and there are inefficienciesor ineffectiveness in the working ability of the counselorgroup. Modern electronic information technology makesthe data management of the counselor team no longer a sin-gle and closed system. With the gradual penetration of newmedia technology on the Internet, counselors need to adoptnew thinking and perspectives, adapt to new practices anddevelopments, use the thinking and technology of big datapatterns, collect the massive information of individual coun-selors and groups, filter, analyze, and adopt it professionally,derive effective information, and make scientific decisions:

Gain Að Þ = inf o Dð Þ + inf oA Dð Þ,

Gain rate Að Þ = Gain Að Þ2Split inf o Dð Þ inf oA Dð Þ :

ð3Þ

In recent years, big data is becoming a new soil of educa-tion development. Big data is a bridge between the physicalworld, information space, and the three-dimensional worldof human society from a macroperspective; big data is apowerful driving force for a new generation of informationtechnology from a value analysis perspective; big data canprovide human beings with new methods of scientific prac-tice and new paradigms of scientific research from a valueanalysis perspective. Artificial intelligence will be the newengine for the development of human society in the next30 years, and the future war between countries will be awar of data assets instead of land and oil, and it can be saidthat the one who has data will win the world. Big data ineducation is born almost simultaneously with big data, andbig data analysis technology can provide good support foreducation development. Education big data theory is ori-ented to the whole process of education, which is a collectionof data of many types of full samples in education space andtime, including teaching behavior big data, teaching resourcebig data, teaching assessment big data, and teaching man-agement big data. Education big data theory advocates theuse of big data in classroom teaching to build a bridgebetween theory and practice, promote the diversified devel-opment of education model, and change the traditionalteaching framework and thinking of teachers, with the char-acteristics of stratification, scale, diversity, prediction, andstage, as shown in Figure 2.

Artificial intelligence is the output, while big data is theinput, and the Internet is the basic equipment that connectsthe input and output functions, and these three are essen-tially different. Artificial intelligence is essentially the powersource of the Internet brain to produce intelligent intelli-gence, and big data is essentially the various valuable

4 Computational and Mathematical Methods in Medicine

datasets accumulated and transmitted by the nervous systemof the Internet brain in the process of operation, while theInternet essentially plays the role of a bridge to connect thefunctions. Big data is the cornerstone of artificial intelli-

gence, Because big data is a type of classical computing, itdoes not operate on the basis of findings but rather on thebasis of searching for them; its usefulness is only in provid-ing a huge dataset for deep learning [17]. When algorithms

Domain expert

Interactive interface

Knowledge acquisition

User Improve the quality of counselorprofessionalism

Context of big data

Important thing is to improve theactive understanding and active

knowledge base database

Interpreter

Management staff and to be ableto make scientific decisions

Effective role in the workthrough data analysis Data training set

Inference engine

Pursuit of high accuracy

Figure 1: System framework.

Development

Teaching evaluation and reflection

Intelligent teaching ability

Constituent elements

Teaching organization and management

Instructional

Design

Application andresearch Educational Technology

New soil ofeducation

development

Macro perspective

Big data

Driving force

Three-dimensional world ofhuman society

From the perspective of theinformation industry

Figure 2: Artificial intelligence coordination and optimization mechanism.

5Computational and Mathematical Methods in Medicine

and large data are combined, artificial intelligence (AI)improves decision-making and learning, which is thenapplied to the Internet’s neural networks and smart termi-nals, enabling all of the Internet’s neural systems to growconcurrently.

Artificial intelligence, big data, and the Internet are inde-pendent and complementary to each other. On the onehand, artificial intelligence needs a large amount of data asthe basis for “analysis” and “decision-making”; on the otherhand, big data also need artificial intelligence technology toreflect the value of data, and the connection of the Internetis the necessary guarantee to realize the value of artificialintelligence and big data operation. If artificial intelligenceis a newborn baby, big data is the milk powder that feedsthe baby, and the Internet is the milk bottle that makes milkpowder for the baby. There is only one way to maximise thevalue of milk powder and bottles: by using them to supporthealthy baby development, and by using baby developmentto further human progress and advancement. The unifyingaim of artificial intelligence (AI), big data (BD), and theInternet, whether they exist separately or are intertwined,is to improve the lives of human beings.

3.2. Optimal Design of Student Management Coordinationamong Counselors in Higher Education. Teaching organiza-tion and management ability is the teacher’s ability to effec-tively coordinate all elements of the teaching process in theprocess of teaching practice, use scientific teaching methods,and effectively play the management functions of planning,organizing, coordinating, and controlling, so that each func-tion can run in an orderly manner, to improve the quality ofteaching [18]. Teachers who want to teach a good classshould not only carefully plan their lessons but also payattention to returning the class to the students and do a goodjob of classroom management at all times, coordinating andcontrolling the relationships between all teaching factors sothat they can form an orderly whole to ensure that teachingactivities run smoothly. The teaching organization and man-agement ability of teachers in higher education are mainlyreflected in the classroom organization and managementability of teachers, including the ability to interact with stu-dents, the ability to mobilize students to actively participatein learning activities, the ability to stimulate students’ learn-ing motivation, the ability to create a classroom teachingenvironment, the ability to organize a variety of teachingactivities, the ability to manage student behavior and disci-pline in classroom teaching, and the ability to provide feed-back and regulate. Ideological and political work is a valuedissemination activity with distinct ideological attributesand must adhere to the correct political direction. China’sideological and political work has the nature of socialist ser-vices and collaborative education for the cultivation ofsocialist talents, so it must adhere to the socialist direction.

The principle of topicality dictates that ideological andpolitical work in colleges and universities should be taughtin a way that responds to current events, fits in with the sur-rounding context, follows current trends, and adapts to thecurrent situation. Colleges and universities should makeadjustments and changes in response to current events and

society and integrate with the current realistic environmentand social atmosphere. First and foremost, the relevance ofjoint ideological and political activity may be seen in howwell it fits into the historical context. The era of big data con-stitutes the most distinctive background of the current era,and along with the iterative upgrading of big data, cloudcomputing, and artificial intelligence technology, the era ofbig data is transitioning and transforming to a moreadvanced stage, and there is a tendency of intelligent formdevelopment. Promoting the development of collaborativeideological and political work should correctly understandthe inevitability and importance of the era of big data, takethe era of big data as an important follow, seize this raredevelopment opportunity in the era of big data, scientifically,reasonably, and prudently use big data to innovate andtransform methods and means, and reflect the sense of thetimes of work. The second is reflected in the grasp of theissues of the times, as shown in Figure 3.

Counselor competency is founded on the practitioner’sown physical and psychological characteristics, and it is aprogressive process that demonstrates various requirementsin different circumstances, and the attributes are representedas competence after continuous training. The fundamentalcharacteristics of a counselor’s professional competencewere previously organized; they are often the general traitsthat all professionals should possess, as well as the smartmix of professional guidance needed for the vocation, sub-ject to the influence of subjective factors such as mental,emotional, psychological, and spiritual and objective condi-tions such as family cultural background and learning expe-rience, belonging to the background and foundation of theconstruction of professional ability [19]. Vocational skills,on the other hand, are a very direct expression of a specificability that can be described qualitatively and quantitatively,which can fulfill a specific occupational function.

Ideology is the precursor of action, and advanced ideo-logical concepts play a positive guiding role in the practiceof ideological and political work, while the opponent playsa hindering role. The concept of personalized education,data education, and collaborative education of the mainbody of ideological and political work in colleges and univer-sities in the era of big data has yet to be strengthened. First,the concept of personalized nurturing of ideological andpolitical work in colleges and universities has not yet beenhighlighted. “Satisfying the needs of the object to adapt tosociety, enjoyment, and development is the most prominentpresentation of educational effect R,” and the application ofbig data technology makes it possible to maximise the needsof college students in all aspects under the implementationof personalized ideological and political work mode. Accord-ing to the survey data, college teachers’ enthusiasm foradopting big data thinking as a teaching method is very highonly 19.17 percent and relatively high 27.13 percent, whilethe average is as high as 47.69 percent, relatively poor 4.07percent, and not at all 1.94 percent. It can be seen that cur-rent college teachers subjected to ideological education workfail to actively use the characteristics of the big data methodand logical reasoning to find the right answer. Needs, ideo-logical changes, behavior patterns, consumption habit

6 Computational and Mathematical Methods in Medicine

characteristics, teachers’ personalized service awareness,awareness of teaching according to the material, and tar-geted guidance awareness are not strong, resulting in its sup-ply of service content and mode and the separation of theneeds of college student objects; the differentiation effect isnot significant. Secondly, the awareness of ideological andpolitical work data management and protection is weak.The effective use of data is an important strategy for thetransformation of ideological and political work informati-zation, wisdom, and personalization, but the excessive col-lection, storage, organization, and analysis of data ofideological and political work teachers and students maylead to the problems of data flooding, data ethics, privacyinfringement, and data dependence. Survey data show thatas many as 44.89% of students believe that big data drivessociety forward while triggering different privacy ethics chal-lenges, which puts higher demands on teachers’ data literacy.Some teachers’ awareness of data management and protec-tion is weak, and their understanding and positioningregarding the jurisprudential and ethical boundaries of bigdata collection and use are not yet clear, and they are lessproactive in thinking about this type of issue. Finally, collab-orative thinking needs to be enhanced, as shown in Figure 4.

Competence encompasses a wide range of abilities, bothinherent and learned, and it is difficult to distinguish betweenthem. Most people have a mix of abilities that may bestrengthened via exercise, although at a slower pace and overlonger periods of time. The capacity to select between severalprofessional paths is a result of one’s transferable skills. With

regard to counseling capacity-building, the entire model takesinto account key competencies, laying out the information andabilities included in competencies and tying them togethereffectively to create the intrinsic desire to develop competen-cies. The model’s design has a significant impact on coun-selors’ overall career development cognition, and it serves asa worldwide work deployment guide for counselor teams inhigher education institutions’ long-term development plan-ning [20]. The model is intended to provide counselors witha career lifecycle plan that includes everything from preem-ployment training (career cognition, basic career skills, andwork concepts) to professional competency use and debug-ging, career competency enhancement and the formation ofa career competency building team, and finally, becomingexpert counselors. The research results and research innova-tions will continue to expand the influence and help theresearch on the professional competence of counselors toachieve interaction and mutual assistance.

4. Analysis of Results

4.1. Artificial Intelligence for Big Data CoordinatedOptimization Mechanism Performance. First of all, datacleaning is performed, i.e., the default data which is valuablein the dataset is populated and the noisy data in the datasetis removed to make the data reliable. After importing thedata of this subject into the MySQL database, the data clean-ing work was performed. There were 6 duplicates among the2591 graduate students’ data, which were first deleted; therewere 21 missing key information fields that could not bemanually filled, which were also deleted; 113 people choseto continue their studies after graduation, and among theremaining data, 109 people were unemployed or lackedemployment information. Since the data mining researchof this topic is mainly for students’ career planning, andthe most important content of career planning is employ-ment guidance, all these related data in the dataset shouldbe discarded. After data cleaning, 2342 useful data wereobtained, among which 1675 were from 2015 to 2020 and667 were from 2018. In this project, the first 1675 data areused as the training set for data mining, and the latter 667data are used as the test data of the system. Then, data con-version is carried out, i.e., data types that cannot be directdata mined are transformed into discrete value types thatcan be mined by normalization and discretization, and datawith too large values that are not easy to handle are con-strained to the range that is easy to operate for cluster anal-ysis, as shown in Figure 5.

In summary, the strategy of improving the k-meansalgorithm using Canopy algorithm preprocessing improvesthe clustering accuracy of the traditional algorithm andmakes significant progress in the processing of high-dimensional data, and the improved algorithm improvesthe accuracy and stability of data clustering to some extent.The clustering mechanism of the Canopy algorithm effec-tively reduces the number of algorithm iterations andimproves the algorithm operation efficiency. An expert sys-tem’s knowledge base is dynamic, and to remain relevant,it must be updated and maintained on a regular basis. The

11.3%

10.5%

1%

13.8%

18.5%

8.9%

10.6%

17%

2.6%

5.8%

A BC DE FG HI J

Total B:109.61

Figure 3: Proportion of information education services.

7Computational and Mathematical Methods in Medicine

existing knowledge in the knowledge base is updated toreflect the current situation, and obsolete or incorrect infor-mation is removed from the system as it becomes available[21]. The knowledge base of this system and the source ofknowledge is the behavioral data and employment data ofthe real graduated students. Therefore, the knowledge baseof this system is updated in a cycle of one year, and at theend of the second semester of each year, the key data ofgraduating students are extracted, and new data are com-posed after data preprocessing, and these data are added to

the existing dataset of the system to reperform the data clus-tering work [22]. After the clustering is completed, new classlabels are reassigned to the new clustering results to formnew clusters, and the new clustered data is used to replacethe original clustered data, marking the completion of theknowledge base update, as shown in Figure 6.

The graphical method is primarily used to find the corre-lation between variables by drawing a scatter diagram, whilethe calculating correlation coefficient method is used to pro-vide specific quantitative data to judge the degree of

Counselor's dedicated field to help and bring Use and innovation of counselor's professionalability

Management and transformation of professionalability of shop instructors

Strengthenthe learningof the core

skills ofcounselors

Expansion ofspecial skillstraining forcounselors

AdvancedBased on

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Counselorjob

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Based on professionalstandards Primary Based on access and

configuration standards Undersupply Decrease in productioncapacity Pre-job training system

Capital goods further drives the economy into aboom phase Academic seminars and reports Daily training system

Counselorwork teambuildingtraining

Figure 4: Overall model of professional capacity building for counselors in higher education.

4 6 8 10 12 14 16 18 20 22–30

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–1.96 SD–16.43166

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Figure 5: Execution time of the two algorithms.

8 Computational and Mathematical Methods in Medicine

correlation between variables based on the scatter diagram.This paper primarily uses the graphical method to analyzethe relationship between variables, to determine whether thereis a linear relationship between a dependent variable and anindependent variable [23]. To support the regression analysis,this paper does correlation analysis on the current situation ofthe teaching power of college teachers and its influencing fac-tors; to highlight the value of the main factor, this paper usesthe maximum variance method to do a rotation on the dataand get the rotated factor loading matrix; according to therotated factor loading matrix graph, we can know the relativeimportance of each variable on the common factor and ana-lyze where the significance of each factor lies.

4.2. Optimization Results of Student ManagementCoordination among Counselors in Higher Education.Teachers in colleges and universities face great challengesin teaching philosophy, teaching design, teaching implemen-tation, information literacy, etc. Traditional lecturingteachers can no longer meet the longitudinal and horizontaldevelopment of classroom teaching in colleges and universi-ties, and teachers in colleges and universities must improvetheir information literacy and develop to TPACK abilitycomprehensively. On the one hand is the challenge broughtby the change of teaching methods and teaching model inthe new era; on the other hand is the challenge brought bythe general enhancement of students’ information literacy[24]. To a large extent, this is due to the fact that many non-information college instructors were never exposed to con-temporary information technology in school. Teachers’inability to effectively utilise contemporary informationtechnologies in the classroom is hampered by a shaky foun-dation of information literacy. Teachers must improve stu-dents’ capacity to collect, analyze, filter, and useinformation, as well as create new knowledge, in light oftoday’s rapidly evolving information technology. However,at present, college teachers, information quality, informationconcept, information theory, and information ability cannotkeep pace with the development of the new era. In addition,

there is an urgent need for college teachers to improve theirdata literacy. The big data industry will form a new branchof the information industry, and it can be said that big datais an important pillar of future education development. Edu-cation big data can be used for not only the refined manage-ment of teaching but also specific classroom teaching; tosome extent of education, big data can also present theteaching effect of teachers, as shown in Figure 7.

The system response time increases with the number ofloads. When the number of loads is low, the averageresponse time of the system is relatively low, within 2 sec-onds; when the number of loads increases to 40, the averageresponse time of the system has improved significantly, withsome reaching about 3 seconds. For the common functionssuch as information inquiry and information release, thesystem response is very fast, while the slowest response timeof the career guidance function reaches 3.1 seconds when theload is 40 units. According to experience, a response timewithin 5 seconds can bring a good user experience for users,so the system server passed the load test successfully for the40 loads reached in this test.

Although artificial intelligence has progressed signifi-cantly, how to apply artificial intelligence in teaching to solvecurrent educational problems and maximise the potential ofcomputer applications in education so that educationaldevelopment can keep pace with societal development is adifficult problem that the education sector is eager to solve,prompting university teachers to urgently update their edu-cational philosophy to keep up with the development ofsociety [25]. This has led to an urgent need for universityteachers to update their educational concepts to meet theopportunities and challenges of teacher development in thenew era. Intelligent machines have more powerful and sensi-tive perception and sensory ability than humans in terms ofperception and recognition; intelligent machines have moreaccurate and faster judgement ability than humans in termsof information understanding; intelligent machines have

0 5 10 15 20 25

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Figure 6: Rotated component matrix.

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Figure 7: Average response time for each system function.

9Computational and Mathematical Methods in Medicine

more intelligent and comprehensive innovation ability thanhumans in terms of information application; intelligentmachines have more intelligent and comprehensive behaviormanipulation ability than humans in terms of behaviormanipulation. The only way for teachers to better enhancehuman-machine integration and achieve the ideal ofhuman-machine coexistence is to change their educationalphilosophy, actively practice, and pay attention to the appli-cation of AI in teaching. Of course, artificial intelligence alsohas its shortcomings; that is, there is intelligence lacking wis-dom, which requires teachers to give full play to the advan-tages of the main body, and artificial intelligence to form acommunity, to complement each other’s strengths andweaknesses, and jointly contribute to the development ofeducation, as shown in Figure 8.

According to the test results, the error rate is 0 when thenumber of simulated users is less than 200; when the num-ber of simulated users is 300-400, the system begins to showerrors, but the error rate is still very low, at less than 0.17percent; when the number of simulated users reaches 500,a higher error rate appears; and as the number of simulatedusers continues to increase, the error rate also increases [26].From this, it can be concluded that the system server canguarantee the accuracy of request processing with 400-500users [27]. Throughput refers to the number of all requestsprocessed by the server in a single test, while the throughputrate is the ratio of throughput to time and reflects theserver’s ability to process requests per unit of time.

5. Conclusion

Using big data in the collaborative education of ideologicaland political work in colleges and universities, the core topicis the development of “people,” and the transformation ofresearch threshold and paradigm is taken care of, as well as

the microdesign innovation of concept logic, content conno-tation, carrier platform, and mechanism system revealed inthe practical situation of collaborative education. The scien-tific weaving of a new model of collaborative ideological andpolitical education in colleges and universities with big datatechnology is limited by such obstacles as backward concepts,differentiated subjects, single methods, scattered resources,and disconnected mechanisms, and the effectiveness of educa-tion is impacted. Therefore, in the context of big data, collabo-rative education of ideological and political work in colleges anduniversities should make rational thinking about the basic ele-ments of collaborative education such as value, concept, subject,object, space, mechanism, and pattern from the dimensions ofhistory and reality, theory and practice, and problem anddemand and give full play to the advantages of big data mining,analysis, prediction, interconnection, and sharing to enhancethe synergistic effect of ideological and political work on col-leges and universities. This paper is only a preliminary explora-tion of this topic, and there is still a lot of room for research inthe future. More in-depth theoretical and practical research isneeded. Because my theoretical foundation is not solid enoughand my research ability is limited, there are still deficiencies andmistakes in the paper; we will make further efforts in the futurestudy; based on this study, the future research and explorationwill continue to improve, to do our humble contribution tothe collaborative education of college counselors and teachersof civics and political science.

Data Availability

Data is available on request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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10 Computational and Mathematical Methods in Medicine

References

[1] Z. Chen, C. Xiao, H. Qiu et al., “Recent advances of artificialintelligence in cardiovascular disease,” Journal of BiomedicalNanotechnology, vol. 16, no. 7, pp. 1065–1081, 2020.

[2] H. S. L. Jim, A. I. Hoogland, N. C. Brownstein et al., “Innova-tions in research and clinical care using patient-generatedhealth data,” CA: a Cancer Journal for Clinicians, vol. 70,no. 3, pp. 182–199, 2020.

[3] Y. Zhao, J. Zhao, M. Yang et al., “Local differential privacy-based federated learning for internet of things,” IEEE Internetof Things Journal, vol. 8, no. 11, pp. 8836–8853, 2021.

[4] M. Huang and C. Lijian, “The path of building curriculumresources of adult colleges and universities based on MOOCin the intelligent era,” Canadian Social Science, vol. 16, no. 6,pp. 32–38, 2020.

[5] O. Iatrellis, I. K. Savvas, A. Kameas, and P. Fitsilis, “Integratedlearning pathways in higher education: a framework enhancedwith machine learning and semantics,” Education and Infor-mation Technologies, vol. 25, no. 4, pp. 3109–3129, 2020.

[6] J. J. Reeves, N. M. Pageler, E. C. Wick et al., “The clinical infor-mation systems response to the COVID-19 pandemic,” Year-book of Medical Informatics, vol. 30, no. 1, pp. 105–125, 2021.

[7] L. Bickman, “Improving mental health services: a 50-year jour-ney from randomized experiments to artificial intelligence andprecision mental health,” Administration and Policy in MentalHealth and Mental Health Services Research, vol. 47, no. 5,pp. 795–843, 2020.

[8] A. Shirmarz and A. Ghaffari, “Performance issues and solu-tions in SDN-based data center: a survey,” The Journal ofSupercomputing, vol. 76, no. 10, pp. 7545–7593, 2020.

[9] M. Chaves-Maza and E. M. Fedriani Martel, “Entrepreneur-ship support ways after the COVID-19 crisis,” Entrepreneur-ship and Sustainability Issues, vol. 8, no. 2, pp. 662–681, 2020.

[10] I. Bardhan, H. Chen, and E. Karahanna, “Connecting systems,data, and people: a multidisciplinary research roadmap forchronic disease management,” MIS Quarterly, vol. 44, no. 1,pp. 185–200, 2020.

[11] E. Kurnat-Thoma, A. Baranova, P. Baird et al., “Recentadvances in systems and network medicine: meeting reportfrom the first international conference in systems and networkmedicine,” Systems Medicine, vol. 3, no. 1, pp. 22–35, 2020.

[12] G. Kaissis, A. Ziller, J. Passerat-Palmbach et al., “End-to-endprivacy preserving deep learning on multi-institutional medi-cal imaging,” Nature Machine Intelligence, vol. 3, no. 6,pp. 473–484, 2021.

[13] C. Yapa, C. de Alwis, and M. Liyanage, “Can blockchainstrengthen the energy internet?,” Network, vol. 1, no. 2,pp. 95–115, 2021.

[14] E. A. Holmes, R. C. O'Connor, V. H. Perry et al., “Multidisci-plinary research priorities for the COVID-19 pandemic: a callfor action for mental health science,” The Lancet Psychiatry,vol. 7, no. 6, pp. 547–560, 2020.

[15] J. Corral-Acero, F. Margara, M. Marciniak et al., “The ‘digitaltwin’ to enable the vision of precision cardiology,” EuropeanHeart Journal, vol. 41, no. 48, pp. 4556–4564, 2020.

[16] R. Lamb, B. Hand, and A. Kavner, “Computational modelingof the effects of the science writing heuristic on student criticalthinking in science using machine learning,” Journal of ScienceEducation and Technology, vol. 30, no. 2, pp. 283–297, 2021.

[17] K. Ternes, V. Iyengar, H. Lavretsky et al., “Brain health INno-vation Diplomacy: a model binding diverse disciplines to man-age the promise and perils of technological innovation,”International Psychogeriatrics, vol. 32, no. 8, pp. 955–979,2020.

[18] A. V. Singh, R. S. Maharjan, A. Kanase et al., “Machine-learn-ing-based approach to decode the influence of nanomaterialproperties on their interaction with cells,” ACS Applied Mate-rials & Interfaces, vol. 13, no. 1, pp. 1943–1955, 2021.

[19] L. P. Cavaliere, B. Singh, M. J. Kumar et al., “Achieving UnitedNations goals throughout the youth leadership,” TurkishOnline Journal of Qualitative Inquiry, vol. 12, no. 3,pp. 2859–2883, 2021.

[20] S. Huang, “Analysis of psychological teaching assisted by arti-ficial intelligence in sports training courses,” Journal of AppliedScience and Engineering, vol. 24, no. 5, pp. 743–748, 2021.

[21] M. Talha, M. Sohail, R. Tariq, andM. T. Ahmad, “Impact of oilprices, energy consumption and economic growth on the infla-tion rate in Malaysia,” Cuadernos de Economía, vol. 44,no. 124, pp. 26–32, 2021.

[22] M. Talha, S. Azeem, M. Sohail, A. Javed, and R. Tariq, “Medi-ating effects of reflexivity of top management team betweenteam processes and decision performance,” Azerbaijan Journalof Educational Studies, vol. 1, no. 1, pp. 105–119, 2020.

[23] M. Talha, M. Sohail, and H. Hajji, “Analysis of research onamazon AWS cloud computing seller data security,” Interna-tional Journal of Research in Engineering Innovation, vol. 4,no. 3, pp. 131–136, 2020.

[24] Y. Zhao and M. Talha, “Evaluation of food safety problemsbased on the fuzzy comprehensive analysis method,” Food Sci-ence Technology, 2021.

[25] M. Talha, “Financial statement analysis of Atlas HondaMotors, Indus Motors and Pak Suzuki Motors (evidence fromPakistan),” Ilkogretim Online, vol. 20, no. 4, 2021.

[26] M. Talha, R. Tariq, M. Sohail, A. Tariq, A. Zia, and M. Zia,Review of International Geographical Education ISO 9000:(1987-2016) A Trend's Review, vol. 10, Review of InternationalGeographical Education Online, 2020.

[27] M. Talha, “A history of development in brain chips in presentand future,” International Journal of Psychosocial Rehabilita-tion, vol. 24, no. 2, 2020.

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