# Innovative Research on Ideological and Political Education in Colleges and Universities Based on Intelligent Wireless Network Environment

Authors: Qingmei Fei
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The current technological growth has made artificial intelligence techniques reach every industry. One such industry is education, which uses AI techniques such as machine learning for university courses. This article considers research on the ideological and political education in colleges and universities based on artificial intelligence and machine learning in a wireless network environment. Ideology is a set of ideas that guide politics and is also referred to as a common idea that a group of people shares. Machine learning is referred to as part of artificial intelligence that uses computer algorithms and statistical models to analyze the data. It can learn and adapt itself with the help of the data. The advantage of machine learning is that it does not require human intervention. The proposed system includes machine learning and artificial intelligence in ideology and political education. Machine learning techniques will revolutionize the educational sector with their efficiency and advantages. Thus, it is found that artificial intelligence and machine learning techniques provide enhanced teaching and learning in ideological and political education provided by colleges and universities. In this research binary search algorithm (BSA) is implemented to analyze the teaching and learning process. BSA is compared with the traditional classroom education and statistical learning methodology and observed that BSA has achieved 95% accuracy in the education training and testing and is higher than the other methods.


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## Body

## 1. Introduction

Economic, political, moral, religious, and technological advancements are only a few concepts that influence how students learn about economics, politics, morality, religion, and technological advancements. It is critical to have financial resources, political protection, and legislative changes for ideological and political education to become a reality. According to the research findings presented in this paper, international businesses could reap significant benefits from an educational platform that combines deep learning [1]. Anxiety can be decreased by using efficient information management techniques and approaches. When dealing with domestic issues, corporations use an ideology-based strategy. Educational activities and instruction have been updated due to the study’s findings, resulting in increased brain plasticity and improved ideological and political education performance [2]. According to the findings of the study, an evaluation of an artificial neural network used in the innovative ideological political education platform revealed errors of 88 percent, political risk of 83 percent, evaluation student of 768.0 percent, and a prediction error rate of 79 percent, as well as an overall teaching quality of 86 percent [3]. Fourteen indices are used to measure college students’ IPE performance, which considers the educational process, policy execution, content and information system performance, and input and environmental factors, among other things. It is possible to evaluate the performance of IPEs using results that are too unsteady to be accurate [4]. The author describes the use of an educational manager and assistants, teaching objects, and the use of an IPE system for college students based on mobile AI terminals, all in the context of the BPN framework [5]. According to the authors of the research, they investigated students’ attitudes toward civic and civic participation (CCP) in a mixed-methods study using the “BPN framework [6].” According to the findings of this study, a more comprehensive approach to civic education is recommended [7]. The researchers researched the IPE trend in the information era by looking at both colleges and universities as subjects for their research. It would benefit college and university journalists to conduct more investigations into NM’s impact on campuses, in which people illustrate how their viewing, listening, and studying habits, as well as their overall behavior and approach to learning, can all be tailored. Using multimedia and network technology to construct training outcomes that impart economic and cultural lessons [8], according to the data collected, student mental health appears to have improved considerably due to the IPE changes [9]. Through the use of Wei Han, it is possible to assess how successfully college students manage their information through Wei Han, a review on how to increase students’ management effectiveness [10]. An ideological and political curriculum that emphasizes AI-driven innovation may aid in the education of students and support the IPE in progressing in a more favorable direction, among other things [11]. As previously said by the author, before moving on to the next stage, make certain that people are up to speed on the new CV work. According to these folks, creativity can contribute to the development of superior reasoning and monitoring abilities [12]. Participating in this training has increased the awareness of IPE employees about the necessity of protecting their personal information. This course’s goal is to teach students how to work with enormous data sets in the present and the future. The researchers employed machine learning techniques and a predictive learning strategy to forecast the discontinuation of their research project. Progress in artificial intelligence has a positive impact on both the university and its students. This study project by the researchers aims to investigate the RBF network in-depth and in real time. Many scientists are drawn to them because of their advantages, such as speedier education and global approach capabilities in a wide range of subjects [13]. To assess the overall algorithmic advancement of RBF networks, novel kernels, learning methods, and kernel parameter values were investigated and compared. As proved by the author, higher education institutions can function strategically and efficiently in internal governance coordination and coordination of resources [14]. The researchers utilized machine learning to analyze student behavior in the classroom and develop predictions regarding graduation rates, which they published in science. Active learning is incorporated into the educational paradigm established. Information processing systems and sensors monitor and analyze the behavior and states of pupils to make more precise and timely judgments [15]. The elasticity of a cloud computing and simulation framework and its programming interface and educational support services are all studied in this study [16]. While practicing basketball, the researchers’ usage of a hidden Markov model with hybridized motion proved useful, allowing them to make greater use of current sports science and physical education teaching methodologies. Standards and practices associated with IPE education such as sections for class exercises, cultural background information, text analysis, and a summary and discussion of the topics covered in the lesson are all included in the teaching component. Cultivating students’ critical thinking abilities and developing a sense of cultural trust in their pupils are just as important as teaching academics and culture to their students [17]. To ensure the long-term success of the USA, college students must become more self-contained, selective, flexible, active, and ideological in their thinking. For university students, it is critical to have access to a modern and growing institution and higher education in political thinking. Social democratic fundamental values guide college cultural life, and it is based on these fundamental concepts that college cultural life grows and develops. Students who put forth significant effort in their studies are more likely to achieve academic success [18]. The design of the app and the construction of a “bulletin” representation should emphasize inquisitive learning to motivate students to collaborate to produce or solve problems. With learning optimization, a novel way to design classification schemes that cope with missing values or irrelevant data may now be employed to address these issues. To make deep learning better, people should emphasize principles and concepts than collecting data.The Institute for Positive Education’s mission is to assist students in improving their overall quality of life while also increasing their ideological grasp of well-being. Given the circumstances, it will matter how productive pupils can be in the current social climate, given the cases [13]. In addition, there are significant management holes in the system as IPE has not yet been fully operationalized. It has a more substantial impact on administration and teaching since IPE may aid in the more effective implementation of school management and teaching practices in the classroom [19]. With the introduction of “Internet plus,” access to the Internet to all educational institutions is a new era of communication. Politico-ideological ideas permeate higher education institutions on a national and international scale. The field of mixed-methods education has undergone tremendous transformation. This is an excellent opportunity to spread the word about visualization while also presenting a distinct point of view on the subject [20]. Teaching for starters, “Internet plus” makes it easier to communicate with people who live in different geographical and cultural locations. The accessibility of information and the speed with which it is made available to users are essential considerations. The intellectual and political horizons of education are expanded due to this approach. Having the ability to participate in academic and political debate using a diversity of perspectives and sources of information gives students who wish to be admitted to schools and institutions must have a basic understanding of political theory and practice [21]. Teachers should be provided with a broad range of ideological and political perspectives to draw upon when teaching in the classroom. The educational tool “Internet Plus” has been successfully implemented in the classroom. Making broad use of network media will help to promote new and creative teaching techniques and instructional resources. Political theory and practice are taught together [22]. “The Internet furthermore” has been widely supported and implemented, allowing teachers and students to communicate through various techniques. Both teachers and students use communication strategies and tactics. This method aids in the formation of a new sort of teacher-student connection, thereby alleviating the mental stress associated with traditional teacher-student interaction. The Internet and other technologies are becoming increasingly linked [23]. Students and educators alike will find this to be an invaluable resource. It is possible to work while both learning and communicating in this environment. Because of this drawback, it is apparent that the soft climate in which people live is changing all the time as a result of the Internet. In order to determine the perception of universities and colleges towards ideological and political education, this study has been conducted. This study has been analyzed using wireless intelligent network.


## 2. Motivation for the Study

The investigation into political and ideological education seeks to determine how institutions of higher learning will regulate continuing to give information during the academic year. This will have transformed teaching qualifications entirely through solely online learning in such a short period. In this study, people investigated students’ perceptions of online learning, its capacity to absorb the material, and their usage of educational learning technology. An online survey was carried out using a standard questionnaire method. Three hundred eighty-two students from academic institutions supplied the data. Due to the binary search algorithm, higher education institutions, according to the report’s results, are unable to prepare solely for online learning.


## 3. Materials and Methods

Machine learning in the educational sector improves the learning techniques for students and enhances the teaching methodology that has been followed for decades. The machine learning technique redefines the educational system, transforming teaching and learning into a new experience. Machine learning can track every learner’s progress and respond accordingly. The best educational system is achieved with the help of artificially intelligent machine learning techniques in a wireless network environment. Nowadays, online education is carried out using wireless technologies, which are represented in Figure1. Machine learning and its various applications are as follows:
(1)
Increased efficiency in learning and teaching: Increased efficiency in learning in educational systems will increase the efficiency of the teaching and learning process. It has the capability of providing better content for any course. It helps the educational organization evaluate the potential of each student. It analyzes what kind of teaching method is suitable for individual students. The learning process is made comfortable for the students, which also reduces the teaching burden for the teachers. The main advantage of machine learning in education is that it can carry out tasks such as management and scheduling(2)
A personalized learning methodology: A personalized learning educational system provides customization in the educational system. With the help of this type of educational system, students can decide their own pace of studying. Students can choose the subjects they are interested in from the entire curriculum. The students select the course, the teacher, and the course timing and can learn whenever they have time within the span of the system(3)
Adaptive learning technique: The adaptive learning technique offered by machine learning can analyze students’ performance in real time and show the students individual teaching methods based on their data. It provides suggestions to the students regarding the course material and learning methodologies, thus providing a personalized learning experience through adaptive learning techniques(4)
Assessment and grading of students: The learning techniques offer accurate assessments and a precise grading system in education. The test scores are automated with the help of assessment systems. The assignments from the students are in the form of papers, essays, and presentations. Machine learning evaluates the students’ work by analyzing their narrative skills, language fluency, and presentation skills and detects plagiarism. With the help of technology, human error in the assessment and grading is avoidedFigure 1
Proposed model of the innovative research on ideological and political education.Thus, artificially intelligent machine learning increases the efficiency of the educational system (both teaching and learning) by enhancing the learning experience for the students, while the teaching methods are made easy for the teachers.Figure2 represents the technologies involved in the IPE teaching and learning process in universities and colleges. The teaching and learning process in IPE starts by transferring the data through wireless network. The role of machine learning is to optimize the teaching and learning processes by incorporating adaptive learning technique, personalized leaning methodology with assessment analysis.Figure 2
Processes involved in IPE.a1,a2 represents the binary search method used to W investigate political and ideological education observation means. Equation (1) illustrates the similarities among the educational observations because database will contain the details of the college students and their changes in the learning process which is represented as a(1)W=∑a∈TTa1a,a2a+∑i=1Tδ0+δ1Tai+εoi,where δ is specified for the functional derivatives with respect to a function which believed to be a constant and the independence ε, while other variables remain unaffected which are represented in
(2)ai=∑p=1kk2πpsinθλf0f+∑a∈oδ0+δ1Tai+εoi,φ represents the first limit ordinal is given in the Equation (3). It is denoted that ω distinguishable by the organization of a range of environmental percentages.
(3)φ=∑s=1ke−kωτ+∑p=1se−kωpsinθ/s+e−k2πpsinθ/λf0f.psinθ/s is the perception of tasks in educational institutions, political, and ideological education, which has become muddled. k represents the mean of the vector’s direction, while λf0 represents the standard deviation. Example is made up of random examples In this case, the function of human wish has become muddled. When a=a1,a2andw=a,−1, then, the following is obtained.
(4)hai=∑k=1pe−k2πpsinθ/λf_0f+∑a>bk+1ifw.a+b≥0=1ifw.a+b<0.The binary search algorithm∫n0n0+N0∀n0≥0 would be implemented in wireless communication technology, and assessments of ∂1 classroom training optimization approaches is given in the Equation (5). Researchers would particularly detail the graph’s creation strategy and the corresponding optimization method predicated on UN supervised multiprocessor learning.
(5)∂1k2=∫n0n0+N0UNτk2abτ+∑k=1n∂2k2,∀n0≥0,n∈CpAs∂1k2 expands, so does the computation. All through this probability computation approach, the structures become longer. To quantify using current hardware, use the parameters UNτk2abτ+∂2k2,∀n0≥0. The existence of fpn in such a phrase is decided by the word that comes before it and is depicted in
(6)fpn=limg⟶01gp∑q=0p−1q+∑p=1q=1pqfn−qg.The sentence is determined solely∇aa/∇aa+t. The two or more words preceding it are deployed in
(7)gp=∑a=1t∇aa∇aa+t+λea−a0=0.A student’s language performance indicator,qig, means the changes between both the student’s language stage and also the difficulty of educational materials (refer Equation (8)).
(8)qig=∑k=1nfini−gpUNτk2abτ+∇aa∇aa+t.Because learner's progress is indicated byEinn, the distinction in the learning progress lets the teacher to understand the change and the details are updated in UNτk2abτ. This change incorporates the learning resource and also the knowledge which the learner wishes to gain. The smaller that difference, the further closely its expertise points of the dedicated learning match rEijnn; the learner’s information points is given in
(9)Einr=∑j∈LEijnr×fini−gp+∑UNτk2abτIts−k2πpsinθ/λf0 current ideological and methodological detailed integrated is incapable of addressing the e−kωτ demands of university, intellectual, and political student development.
(10)φ=∑UNτk2abτ+e−kωτ=∑k∈pNe−kωpsinθ/s=e−k2πpsinθ/λf0f,φ=∑k∈pNe−k2πpsinθ/λ+∑UNτk2abτ.The use ofΘj illustrates the large information and data sharing. In IP learning, much information will be gathered and used as in ∑i=1mTijandΘj−i order (refer Equation (12)). Supporting the industry in improving its earnings. As a result, describing big data with mobile learning pji clarifies how such distinct notions have been classified. Tij is depicted in
(11)Θj=∑i=1mTijandΘj−i=∑p≠imTij×∑UNτk2abτ,(12)pji=pijSjaji×∑UNτk2abτ=Kji≥KjiθjiTij,(13)φ=∑j=1nTji≤F0+∑j=1npji≤U0,(14)L=φ+∑i=1Tλei∑j=1nTji−F0+∑j=1nλti∑j=1npji−U0.Its education must be oriented on communist ideology but also methodologyL=φ+λei, with the purpose of liberating people from current framework constraints and constructing ӘL/ӘTij a set of regulations as the fundamental goal the political and intellectual learning. These methodologies are represented in the Equation (13) and Equation (14). The final political and intellectual learning goal is signified in.
(15)ƏLƏTij=∑T=1Kρeρe+ρt.Kji∑Tij−∑i=1j=1ρtρe+ρt.Kjiθj−1∑pjiTij2+∑i=1j∈KλeiKji−λeiKji−λtiKjiθj−iTij2=0.


## 4. Result and Discussion

The binary search algorithm∫n0n0+N0∀n0≥0 would be implemented in wireless communication technology and assessments of ∂1 classroom training optimization approaches. Researchers would particularly detail the graph’s creation strategy as shown in Figure 2. In binary search, living environments of the students were employed to classify data and dataset both teaching and learning education in IPE. It achieves results for teaching male accuracy (66.23%) and female correctness (65.23%) (58.32%). Another one receives results for learning male accuracy (76.42%) and female accuracy (76.54%) (71.45%). The inquiry of political and ideological education relies on the deep learning approach to determine the institutions during the academic year (refer Figure 3).Figure 3
Analysis for performance in residential environment for using binary search algorithms with artificial intelligence and machine learning in wireless network environment.To classify the existing approach for education, the dataset in men’s and women’s residential environments is used in learning in IPE. It is obtaining results for male accuracy (64.34%) and female accuracy (64.98%) in teaching education (57.89%). Another one receiving results for learning male accuracy (79.87%) and female correctness (75.87%).psinθ/s is the perception of tasks in educational institutions, political, and ideological education which has become muddled. k represents the mean of the vector’s direction, while λf0 represents the standard deviation to retrieve in Figure 4. The investigation’s political and social orientation focuses on determining. It compares the results of existing work with binary search algorithms using AI learning to obtain the exact outcome for binary search Algorithms to deep learning. It compares the existing results analysis again for binary search algorithms with machine learning (refer Table 1).Figure 4
Analysis ideological and political education environment for IPE in colleges and universities using AI with WN technology.Table 1
AI-learned binary search algorithms for respondent classification men and women aspects of the residential environment.
ParametersClassification for men and womenResidential environment countPercentage (%)GendersMen16985.15Women18489.42Residential of the environmentTeaching12946Learning18582EducationSchool36295.7515–22 years9517.73ActivitiesExtracurricular activities31665.4The report’s population was made up of 382 kids from two of the largest schools and colleges in a nonprobabilistic manner. The general populace is woman, between both the periods of 15 to 22, and also is engaged in elementary level (see Table1). Nonetheless, 382 (90.43%) of such students are men, while 169 (85.15%) were women, 184 (89.42%) were with a learning, and 129 (46%) were with a teaching, while 185 (82%) have been in education, with the rest 316 (65.4%) in specialist education (refer Table 1).In this study, we looked into students’ perceptions of online learning, their ability to assimilate information, and their use of educational learning platforms in this regard. A moderately structured questionnaire was used to conduct an online survey. 382 students from academic institutions contributed data. The median, standard error, variance of mean, and variance std. error variation values are specified in the performance monitoring for the impartial measuring test for school, and the evaluation for the that performance is dependent on these data. The analysis on political and ideological education is represented by the Binary search Algorithms to quantify using level of learning by the students using the parameterUNτk2abτ+∂2k2,∀n0≥0. The existence of fpn in such a phrase is decides the level of learning progress by the students and the results are illustrated in Figure 5.Figure 5
Performance analysis for measurements of impartiality school education assessment.Given the struggles, students believe that traditional face-to-face technique is the ideal way to carry out the complete teaching and that the platform would be utilized as a supplement to aid the educational process. Thus, 69.12% of students prefer face language teaching/learning, 58.31% prefer a combination of physical and digital classes, and 19.83% choose web-based learning/teaching (refer Table2).Table 2
Impartial measurements test of school education.
ParametersN number of studentsMeanS. D.p learning and teaching educationt test of mean equivalenceStd. error differenceMean differenceOnline environment3723.903.560.001-2.630.75-0.65Online established learning/teaching4344.652.970.010-3.460.44-0.39Platform practice4173.212.840.0012.470.330.17Platform effectiveness3864.233.520.001-1.370.35-0.53Its overall performance of such a process of teaching and learning is employed in the E-learning framework functioning a supplement these educational process. Thus, 94.32% of students in an overall quality analysis would advocate employing binary search algorithms through the final score for the teaching/learning upon that dataset. Alternative factor, i.e., a mix of offline and online classes, considered for the analysis shows 84.23% would encourage this model of learning. However, binary search algorithms give the best performance and would suggest online-based learning/teaching learner’s progress as indicated byEinn The distinction in learning progress lets the understanding level of the student by the teacher which is given in UNτk2abτ The results of the analysis are given in Figure 6.Figure 6
Performance analysis of online environmental for ideological and political education in colleges and universities using AI and WN technology.The performance of the teaching approach is evaluated, only with E-learning framework serving as a supplement here to educational process. Thus, based on the total achievement of 95%, first, second, and final class honor teaching/learning utilizing binary search algorithms was recommended for the dataset. For the performance analysis with the existing system, 76% would select a combination of both digital and text based contents. Binary search algorithms seem to be the best possible result that provides easy accessibility with the online learning/teaching. The existing ideological and methodological concepts that are integrated−k2πpsinθ/λf0 are incapable of answering the e−k demands of the university’s intellectual and political student growth which is represented in Figure 7. Our findings on the disadvantages of online courses revealed consistent total grade intake. If teachers failed to be updated with the strategies to keep the students engaged, there is a lack of data related to grade of the students (see Table 3). Aside from technological improvements, environmental disruptive innovations including such noise from family members or neighbors, and a lack of suitable working space, have an impact on the amount of time pupils can concentrate when learning online.Figure 7
Performance analysis for time based frequency of online and offline classes using binary search algorithm.Table 3
Result analysis median scores and differences political and ideological teaching statistics.
StudentBinary search algorithmsPerformance analysis (%)Time (s)FrequencyAccuracy (%)Teaching recognition3.70.82396Learning recognition30.74582Learning context2.40.86778Effect of class room2.80.76167The binary search methods with optimization strategy are used not only to analyze AI and machine learning data, but also to extract a most important information using knowledge by analyzing and modifying vital information. In spite of the fact that processing of data is typically more sophisticated and also the level of available information is relatively large, machine learning systems struggle properly in characterizing patterns in the data. Students believe that education is the most important aim, but education also includes guidance, evolution, recognition, understanding, dissemination, and cooperation that all help students achieve their goals. The use ofΘj illustrates the large information and data sharing. In IP learning, much information will be gathered and used as in ∑i=1mTijandΘj−i order and the results of the analysis is given in Figure 8. The attainment of students’ efficiency framework is focused on behavioral product evaluation. The performance analyzes the median score and the variations in political and philosophical teaching statistics. The results indicate the best performance for such binary search algorithms utilizing optimization using AI and WN approaches (refer Table 3).Figure 8
Performance evaluation and comparison for existing algorithm to analysis for statistics on political and ideological education using AI with WN technology.


## 5. Conclusions

All industries are embracing artificial intelligence due to the present technological advancement. One example is the education industry, which employs AI techniques like machine learning to help students better understand university courses. Artificial intelligence and machine learning in a wireless network environment will be studied in this article regarding the ideological and political education at colleges and universities. The term “ideology” can be applied to both a system of principles that govern politics and a common viewpoint held by many people. The proposed study technique has been implemented to determine the perception of college and universities towards ideological and political education. Machine learning is a type of artificial intelligence that uses computer algorithms and statistical models to evaluate data, and it may learn and adjust itself using the data. Because machine learning does not require human intervention, it has a significant advantage over traditional methods. The suggested system incorporates artificial intelligence and machine learning into political philosophy and education. The efficiency and benefits of machine learning techniques will change the educational sector. These findings show how artificial intelligence and machine learning approaches can improve the teaching and learning of ideological or political education at colleges and universities.



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