Research on Intelligent Informatization Management System of Ideological and Political Education for Student Groups in Higher Educational Institutions

In today’s society, information technology plays an indispensable role in all fields, and with the rapid development of the Internet, information technology has penetrated into people’s daily life. At the same time, ideological and political education has ushered in new opportunities and challenges at the stage of higher education in China [1-4]. Informatization teaching can make the ideological and political class more vivid and flexible, as well as improve students’ participation and learning effect. There are also some problems in informatization teaching, which hinder the development of informatization teaching of ideological and political class in colleges and universities [5-8]. Therefore, it is self-evident that it is important to better integrate information technology and ideological and political education teaching in depth, and to build an intelligent informatization management system for ideological and political education in colleges and universities [9-12].

The intelligent informatization management system of students’ ideological and political education is a platform for comprehensive, systematic and scientific management of students’ ideological and political education by using informatization technology [13-14]. This system covers many aspects such as students’ basic information, ideological and political education courses, practical activities, assessment and evaluation, and realizes the whole process management of students’ ideological and political education. Through this system, schools can manage and supervise students’ ideological and political education more conveniently [15-18]. Teachers can release course materials related to ideological and political education, assign homework, and organize discussions in the system, as well as learn about students’ learning and feedback in real time in order to adjust the teaching content and methods in a timely manner [19-21]. Students, on the other hand, can study the ideological and political education courses, submit assignments, and participate in discussions anytime and anywhere through the system, and also have a better understanding of their learning progress and performance [22].

Based on the combination of intelligent background, this topic will carry out specific research on the construction of intelligent informatization system for ideological and political education of student groups in colleges and universities, focusing on the intelligent service demand of informatization management. According to the demand analysis of the management system, this paper designs the information management cloud service model of ideological and political education of college student groups according to the idea of SOA, and develops the intelligent informatization management system based on cloud computing technology. Finally, the effect of the developed intelligent information management system is examined through system performance testing, combined with hierarchical analysis methods and fuzzy comprehensive evaluation methods.

2

Intelligent information management system for ideological and political education

2.1

Demand Analysis of Intelligent Information Management System

The information management system of ideological and political education for students in colleges and universities is a software information system with complicated functions and huge business, which mainly involves the management of student data, the management of basic data information of teaching affairs, the management of course information, the management of course selection and scheduling, the management of grade information and the management of students’ evaluation of teachers and other major businesses. The demand analysis of student data management is shown in Figure 1. Due to the high principle and technical requirements of student management, coupled with the complexity and triviality of organizing work, it is difficult for many academic staff to get rid of their daily work. In terms of functional requirements, information management includes maintenance of students’ information organization, information maintenance, maintenance of dissimilar situations, information protection, management of students’ photos and rewards and punishments, and formation of reports and documents for data reporting based on students’ data. Therefore, in the information management system, the management of students is centered around the information status of students in order to ensure effective real-time management of student group data information.

From the needs of ideological and political education teaching scheduling management, scheduling information is the most basic and important work in many teaching links, and its task is very complex. To solve the scheduling work of ideological and political education, it is necessary to solve the relationship between the curriculum, classrooms, students, teachers, time and other constraints, i.e., to ensure the reasonable and effective allocation of teaching resources. Its essence is how to use the existing human, financial, material, time and space and other educational resources of colleges and universities to achieve high efficiency and optimization of teaching services, in order to create favorable conditions for the scheduling of colleges and universities, so as to create a harmonious and objective environment for the smooth and orderly development of educational activities.

The management of teaching evaluation information is based on the students as the main body, while the teacher is guided by reasonable guidance to carry out skillful teaching actions. Therefore, the ideological and political education of students listening to the class should be based on active teaching choices and feedback supervision, with a focus on passive acceptance of knowledge. As regards the university itself, it needs to ensure that the teaching process of teachers is in line with the teaching task and has good teaching quality. Ideological and political education teaching is an important means of evaluating teaching through information evaluation. The main focus is on the established teaching objectives and syllabus, and modern information technology is utilized to conduct a comprehensive assessment and evaluation of teaching activities. The evaluation of teaching activities, feedback, and regulation has a more important guiding significance, not only to improve the quality of teaching, but also to strengthen the role of teaching incentives and supervision.

2.2

Intelligent information management system construction

2.2.1

SOA-based information management service model

SOA is a method of designing software systems, not a programming language or a specific technology for implementing applications. Different developers have different steps and methods for software development. SOA enables developers to adopt a unified architectural approach to software development under certain environments, providing a conceptual framework structure for solving application service development problems. The core element of a SOA implementation is “services”, which are provided and used by a specific set of entities, also known as the three SOA roles, namely service providers, service consumers and service agents. Services in a SOA environment have some of the basic characteristics as described above, where services are identified with a network address and are accessed by looking up the network address. This design will focus on how the business is mapped and how business processes are mapped to application services. The three roles in SOA (service provider, service broker, and service requester) have three basic behaviors between them, namely publish, lookup, and bind, through which services are provided, by the service provider publishing information about the services to the service broker, which provides the registration and lookup interfaces for the services, and the service requester lookups the required services through the service broker and binds them to the these services provided by the service provider.

The information management cloud service model for ideological and political education of college student groups is designed according to the idea of SOA, and a unified description specification is adopted to generate standardized, reusable and loosely coupled management services. The SOA-based information management service framework is shown in Figure 2. In this paper, we design a framework model of SOA-based information management cloud services, which mainly consists of cloud resource providers, cloud application service providers, cloud service management providers, and users. Where the cloud application service provider corresponds to the service provider in the SOA model, the cloud service manager corresponds to the service agent in the SOA model, and the user corresponds to the service consumer in the SOA model.

2.2.2

Intelligent information management system construction based on cloud computing technology

Intelligent informatization management system is shown in Figure 3. The overall framework architecture of intelligent informatization management system based on cloud computing technology is based on the standard cloud architecture, which is divided into four layers, namely resource layer, data layer, application layer and user layer from the bottom up, and adopts a modularized design, so that each system can work independently as well as interact with each other in a collaborative manner.

It mainly integrates servers, networks, storage devices, a unified data information center and machine rooms, and provides support for the upper layers of the basic system through the virtualization platform in the form of elastic resource management. Based on the characteristics of cloud computing technology, the system is designed with a shared resource pool consisting of high-performance X86 servers and platform management servers. The platform management server installs the virtualization management system required for the resource pool, and provides resource pool control and scheduling services in addition to virtualization services.

Hardware resources include servers, storage devices, network devices, etc. in the data center. The smallest unit of virtualization management is the virtual machine, which is capable of completing functions such as load management, resource deployment, resource monitoring, security management, and data management. In addition, it can also use the interface provided by the virtualization platform to manage virtual hardware resources. Resource layer services mainly include image management, system management, user management, system monitoring, etc. These services correspond to the functions provided by the virtualization management layer, so that the user can obtain the interfaces of various resources in the resource layer.

It consists of basic data of colleges and universities, enrollment information of colleges and universities, and user information database, etc. The basic data of colleges and universities includes formatted profiles of colleges and universities, information about teachers, and professional settings. College information contains various data related to college enrollment in recent years, including score information, enrollment plan information and so on. The user information is associated with the university departments and candidate registration information in this system. The structure is master-slave, distributed file system cluster has a NameNode and multiple Data Nodes, NameNode manages the file system metadata, Data Node stores the actual data, the client contacts the NameNode to get the metadata of the file, and the real file read/write operation is to interact with the Data Node.

The application layer is a collection of application software in the cloud, which are built on top of the resource layer and data layer, and are provided to users through the Internet to provide different application services for different objects. The layer system contains two parts: a front-end application and a back-end management system. The front-end application mainly focuses on user-oriented customizable business functions, including enrollment planning, enrollment publicity management, data statistical analysis, enrollment data reporting, new student check-in management, and other parts. The back-end management is mainly divided into three major parts: cloud resource management, platform management, and business management. Cloud resource management mainly supervises and allocates all kinds of servers, storage, and network resources of the computing center in a unified way. Platform management includes unified user management, basic data management, and terminal management. Business management encompasses business rights, business configuration, docking management, and statistical analysis functions.

The user layer provides user registration, identity verification and authorization services and interfaces, and the authorization information will be used as the passport to call the services of the lower layer in order to guarantee the security of the system and data.

3

Analysis of the effectiveness of the intelligent information management system

3.1

Performance Test of Intelligent Informatization Management System

System testing is mainly used to test whether the functions of the system are realized normally, which is an important part of system development. System testing provides a solid foundation for the system’s normal operation and provides a certain reference for improving and upgrading it. In terms of system performance, the whole system performance and load capacity was effectively tested, including CPU utilization rate, system response time, etc. The hardware used is CPU, which is the most advanced and most powerful. The hardware used is a dual-core, 2.0GHz or higher, x86-compatible processor with 4GB of RAM and 200MB of local storage as cache. During this performance test, 500 users operated the system simultaneously for one and a half hours. The results of the CPU utilization test of the system are shown in Figure 4. In 90 minutes, the actual CPU utilization rate of the whole system is between 10% and 60%, and the actual average CPU utilization rate is 37.09%, which shows that the developed and designed system fully meets the actual requirements of users.

The test results of the system response time are shown in Figure 5. In order to further verify the performance of the system in this paper, the traditional information management system is used for comparison. When the number of users is within 400, the response time difference between the two systems is not large, but the intelligent information management system in this paper has a small advantage. After the number of users exceeds 500, the response time of this paper’s system stays within 1.0~2.5s, while the response time of the traditional system is between 1.6~3.4s. It can be found that the load of the traditional system has obviously increased and the response time delay of the system is serious, which further validates the performance of the system in this paper.

3.2

Evaluation of Intelligent Informatization Management System

3.2.1

Evaluation method based on AHP-fuzzy comprehensive evaluation

Hierarchical analysis method (AHP) has been applied in many industries and has received widespread attention and recognition due to its flexible structure, simple construction, and organic unity of qualitative and quantitative features. It can solve some fuzzy decision-making problems that are difficult to quantify. This method can not only quantify the decision maker’s qualitative empirical judgment, but also the human thought process hierarchical, through the various levels of the relevant factors for pairwise comparison, layer by layer to verify the results of the comparison, so as to make it more scientific and reliable. Through the hierarchical analysis method, the problem is subdivided into the objective level, the criterion level, and the program level.

Fuzzy comprehensive evaluation method (FCE), is based on fuzzy mathematical affiliation theory, the use of mathematical analysis and evaluation, the qualitative analysis results into quantitative assessment results, based on the hierarchical analysis method, through mathematical operations, the quantification of the various factors affecting the best preferred option, so as to effectively overcome the fuzzy and complex decision-making problem of multiple indicators or difficult to quantify. The fuzzy comprehensive evaluation method is adopted to evaluate the system, utilizing the theory of affiliation degree in fuzzy mathematics, combining quantitative and qualitative analysis research organically, and making a concrete analysis of the more abstract system effect problem.

The specific steps for using hierarchical analysis to establish the evaluation system are as follows:

Step 1, establish the recursive hierarchy of the problem.

Split a complex problem into multiple components and further divide it into several different levels based on the logical association and subordination between these elements. The hierarchical way of thinking is formed by determining the position each of these elements occupies in the total system according to certain criteria and making them interrelated to form a whole. In general, the hierarchical structure can be divided into three parts: the goal level, i.e., the decision-making goal level

Step 2, constructing a two-by-two comparison judgment matrix.

This method can also be used in other multi-factor comprehensive evaluation. That is, to assess and compare the relative importance of any two factors at the same level, the scale method is used to rate the experts, and the judgment matrix is constructed accordingly.

Step 3, the weight calculation and consistency test of each program level factor under the single criterion.

Specifically, the factors of the scheme layer are compared two by two, the judgment matrix is constructed, its eigenvalues are calculated, and then the importance weight ranking of the scheme layer relative to the corresponding single criterion layer is derived through normalization. It is worth noting that a consistency test is required to ensure the consistency and rationality of the judgment matrix. The mainstream methods for calculating the eigenvectors of judgment matrices are the sum-product method and the square-root method, and the sum-product method is used here for calculation.

First, each column of the judgment matrix A is normalized by Eq. to obtain ${\bar{α}}_{x y}$ : (1) ${\bar{α}}_{x y} = \frac{α_{x y}}{\sum_{p = 1}^{n} α_{k y}}, x = 1, 2, \dots, n, y = 1, 2, 3, \dots, n$

Next, ${\bar{α}}_{x y}$ is summed to obtain matrix W_xy according to Eq: (2) $W_{x y} = \sum_{x = 1}^{n} {\bar{α}}_{x y}; x = 1, 2, \dots, n$

Third, W_xy is normalized to obtain W_x, the weight, by applying Eq: (3) $W_{x} = \frac{W_{x y}}{W_{y}}$ (4) $λ_{\max} = \frac{1}{n} \sum_{x = 1}^{n} \frac{{(A_{w})}_{x}}{W_{x}}$

Fourth, the maximum eigenvalue λ_max is calculated by applying Eq. and the consistency test is performed to calculate the CR: (5) $C R = \frac{C I}{R I}, C I = \frac{λ_{\max} - n}{n - 1}$

Specifically, the factors of the scheme layer are compared two by two, the judgment matrix is constructed, its eigenvalues are calculated, and then the importance weight ranking of the scheme layer relative to the corresponding single criterion layer is derived through normalization. It is worth noting that a consistency test is required to ensure the togetherness and rationality of the judgment matrix. The mainstream methods for calculating the eigenvectors of judgment matrices include the sum-product method and the square-root method, and the sum-product method is used here for calculation. When CR<0.1, the judgment matrix A has good consistency.

Step 4: Calculate and rank the total weights of the factors of the program layer with respect to the target layer.

Considering the importance weight of each factor of the guideline layer to the decision-making layer as a₁, a₂, a₃.....a_n, the importance ranking of each factor of the program layer to the corresponding guideline layer is W_1y, W_2y, W_3y.....W_xy, and the importance ranking of each factor of the program layer to the target layer (total ranking weight) is as follows: (6) $\sum_{y = 1}^{x} α_{y} W_{x y}$

The detailed operational steps of the fuzzy comprehensive evaluation method are as follows:

Step 1, establish factor set and rubric set.

Factor set U = {u₁, u₂, ⋯u_n} in this paper represents n evaluation indicators in the internal control of H universities. Comment set V = {v₁, v₂, ⋯v_m} is the 5 evaluation indexes set for the evaluation results, which are “excellent”, “good”, “medium”, “pass” and “poor”.

In step 2, the evaluation matrix and the weight vectors of the evaluation factors are determined.

For each evaluation factor u_i, a one-factor evaluation is implemented: $r_{i} = (r_{i 1}, r_{i 2^{'}} \dots, r_{i m})$ , i.e., r_ij(0 ≤ r_ij ≤ 1) represents the judgment made by v_j on factor u_i, resulting in a one-factor evaluation matrix R: (7) $R = {(r_{i j})}_{n \times m} = [\begin{matrix} r_{11} & r_{12} & r_{13} & \dots & r_{1 m} \\ r_{21} & r_{22} & r_{23} & \dots & r_{2 m} \\ r_{31} & r_{32} & r_{33} & \dots & r_{3 m} \\ ⋮ & ⋮ & ⋮ & ⋱ & ⋮ \\ r_{n 1} & r_{n 2} & r_{n 3} & \dots & r_{n m} \end{matrix}]$

In this paper, we will define the weights of different evaluation indicators in the effectiveness of internal control according to the different degrees of influence of each evaluation factor on the evaluation object, and utilize the hierarchical analysis method to determine the weights of each indicator. The weight factor is to assign a total value to the information represented by all evaluation factors and make it computable, so that the weight can reflect the importance of the whole system or some elements to the overall performance. Based on relevant classification criteria such as objectives and criteria, the assessment factors related to the object of study were categorized into multiple levels and grades. This was done to clarify the importance of each assessment indicator at the objective level and to determine the weight of each assessment indicator in the internal control strategy in this article.

In step 3, a fuzzy evaluation is performed by synthesizing a vector based on the evaluation matrix and weights.

Based on the set of weights W = (w₁, w₂⋯, w_i) and the evaluation matrix R = (r_ij)_n×m, A = W*R is derived, vector synthesis for each layer is performed, and the set of rubrics is set up so that a composite score can be derived about each indicator. Thus, the evaluation object is evaluated.

3.2.2

Evaluation Indicators of Intelligent Informatization Management System

In order to comprehensively and objectively evaluate the “Intelligent Information Management System”, this paper selects the direct users of the software as the target of the questionnaire survey, including the maintenance personnel who provide technical support for the software and the users who are responsible for filling in the reports. This chapter analyzes from the perspective of maintenance technicians. The questionnaire survey was directed to 12 people, collectively referred to as maintenance technicians, and was processed using the method of expert assessment, and the system evaluation indexes were obtained after expert questioning. The 12 maintenance technicians who participated in the questionnaire survey as mentioned before were invited to score, and the weights were analyzed using SPSS software to derive the evaluation index system and index layer weights. The evaluation index system of intelligent informatization system is shown in Table 1, in which the system quality factor has the highest weight (0.5371), followed by the organizational management factor (0.2970), benefit management factor (0.0924), and user management factor (0.0735).

Table 1.

Intelligent information system evaluation index system

Target layer A	Standard layer B	Weighting	Index layer C	Weighting
Evaluation of intelligent management systemA
	Organizational management factorB1	0.2970	Leadership attentionC1	0.0981
			Top designC2	0.5139
			Overall implementationC3	0.388
	System quality factorB2	0.5371	System compatibilityC4	0.0589
			Functional perfectionC5	0.4167
			Module scienceC6	0.4001
			Content updateC7	0.1243
	User management factorB3	0.0735	Business trainingC8	0.1572
			Thought knowledgeC9	0.1851
			Interaction and communicationC10	0.6577
	Benefit management factorB4	0.0924	Work efficiencyC11	0.653
			Marginal costC12	0.273
			BrandC13	0.074

3.2.3

Fuzzy evaluation results of intelligent informationization management system

Twelve maintenance technicians as mentioned earlier were invited to score. Fuzzy comprehensive evaluation was performed using SPSS software. The evaluation levels were in the order of “very good (5), good (4), fair (3), poor (2), and very poor (1)”. The scoring of maintenance technicians was based on a five-point scale. The final results are obtained by summarizing the scores of 10 maintenance technicians on the operational evaluation of the data sharing and fusion subsystem and conducting fuzzy evaluation using SPSS software. The fuzzy evaluation results of the intelligent informatization management system are shown in Table 2. According to the calculation results, the scores of the indexes of organizational management factor, system quality factor, user management factor and benefit management factor are 3.394, 3.325, 3.610 and 3.479, which are distributed between average and better, and the overall comprehensive score of the intelligent informatization management system constructed in this paper is 3.395, which is in the range of The overall comprehensive score of the intelligent informationization management system constructed in this paper is 3.395, which is in the range of general and better, reflecting that the overall effect of the system in this paper is in the range of “general” and “better”.

Table 2.

System fuzzy evaluation results

Index	5	4	3	2	1	Score
Organizational management factor B1	0.114	0.350	0.363	0.175	0.000	3.394
System quality factor B2	0.095	0.362	0.334	0.221	0.001	3.325
User management factor B3	0.172	0.402	0.299	0.135	0.000	3.610
Benefit management factor B4	0.095	0.431	0.337	0.143	0.001	3.479
Comprehensive evaluation	0.106	0.370	0.335	0.195	0.000	3.395

4

Conclusion

While the cause of higher education is developing rapidly, the university state pays more attention to the information management of ideological and political education of student groups. For this reason, this paper analyzes the demand for an intelligent information management system, constructs an information management service model based on SOA, and proposes an intelligent information management system based on cloud computing. In the performance test of the system, the actual CPU utilization rate of the developed intelligent information management system is between 10% and 60%, and the average CPU utilization rate is 37.09%. The response time of the system is kept within 1.0~2.5s, which is reduced by 0.6~0.9s compared with the response time of the traditional informatization management system.In order to objectively assess the effect of the system, through the AHP-fuzzy comprehensive evaluation method, the comprehensive evaluation result of the effect of the system is 3.395 points, and the scores of each index are above 3 points, which are in the general and better. The research results further verify the performance of the intelligent information management system, and provide a path for the optimization of the information management of colleges and universities, as well as provide ideas for the information management of the ideological and political education of the student body in colleges and universities.

Lingua:: Inglese

Frequenza di pubblicazione:: 1 volte all'anno
Argomenti della rivista:: Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro

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Research on Intelligent Informatization Management System of Ideological and Political Education for Student Groups in Higher Educational Institutions

Danqiong Wang

Pubblicato online: 24 mar 2025

Ricevuto: 19 ott 2024

Accettato: 02 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0763

Parole chiaveSOA, Cloud computing, AHP, Fuzzy comprehensive evaluation, Intelligent informatization management system

© 2025 Danqiong Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
SOA, Cloud computing, AHP, Fuzzy comprehensive evaluation, Intelligent informatization management system