# The Combination of Internet of Things Technology Based on Probability Model Network and Mass Education

Authors: Meiling Xu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the continuous development of the Internet of things, educational informatization has become a hot spot in the application of education. Internet of things technology, combined with various subject fields of education, can better achieve subject teaching objectives and teaching assistance. In the multimode teaching optimization model, weight distribution is a complex multiobjective decision-making problem. In this paper, a Bayesian network based on probability model is proposed, which is combined with the large entropy criterion to determine the comprehensive weight. The combination of Bayesian network and Bayesian statistics can make full use of the information of domain knowledge and sample data. Bayesian network uses arc to represent the dependence between variables and probability distribution table to represent the strength of dependence. It organically combines prior information with sample knowledge to promote the integration of prior knowledge and data, which is particularly effective when sample data is sparse or difficult to obtain. Bayesian network is used to associate objective attributes and influencing factors to self-study the target weight. By grasping the characteristics of information technology and the nature of university citizenship curriculum, this paper further analyzes the internal relationship between them, promotes the deep integration of the two, and improves the effectiveness of university citizenship curriculum teaching. By grasping the characteristics of information technology and the nature of the school civics curriculum, we promote a deeper integration of the two. It is my education that is more advanced.


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## Body

## 1. Introduction

Information technology into all areas of society influences the way people think, learn, and behave. The university’s Civic Studies course has the important task of studying, researching, and teaching Marxist theory and cultivating students’ core socialist values and ideals and beliefs. It is imperative that higher education Civics courses adapt to the general trend of education informatization and integrate information technology into course teaching [1, 2]. The traditional teaching methods of the university’s Civics and Political Science course can no longer meet the needs of students. Therefore, at present, it seems that the informatization of the teaching of Civics and Political Science has become an unstoppable trend. At present, many scholars have conducted research on this issue from different perspectives, providing a certain reference role for the latter [3, 4].Information technology is beginning to be used in all areas of education, providing convenient and effective teaching aid to better achieve subject teaching objectives [5]. It provides a convenient and effective teaching aid to better achieve the teaching objectives of the subject. The majority of teaching activities are now based on information technology, and the scope of its use in the scope of application of information technology in teaching is expanding [6]. At present, research on the integration of information technology and the teaching of Civic Studies in colleges and universities has been conducted at home and abroad, and certain progress has been made, which can serve as a reference for further research. However, the current research on the integration of the two has not been analyzed in-depth from the nature of the curriculum of the college Civics course, and there is no real in-depth integration between the teaching of the college Civics course and information technology in the content. In this regard, this paper starts from the dialectical relationship between form and content, goals, and means, grasps the characteristics of information technology and the nature of the curriculum of college Civics class, further analyzes the inner connection between the two, and promotes the deep integration of the two, so as to improve the effectiveness of the teaching of college Civics class.


## 2. Related Work

Since the 1990s, a large number of articles and papers on the integration of IT and the curriculum have been published in international conferences and in the relevant literature, but few have been published with any real theoretical depth. However, there are few studies with any real theoretical depth and even fewer articles that can give a comprehensive and profound discussion of the three issues mentioned above [7]. There are two important works that can discuss the theory and methods of integrating information technology and curriculum in a more systematic and complete way from the above three aspects. [8] is recognised by the international education community as the most authoritative and representative literature on the integration of information technology and curriculum in a more systematic and complete way. The three main integration models are described in detail in [9]. In order to help teachers solve the problem of how to effectively implement the integration of information technology and subject teaching, [10] puts forward specific steps and methods for effective integration and provides clear answers to the three major problems faced by the theory of integration of information technology and curriculum (the goal of integration, the connotation of integration, and the method of integration). Although the results are not ideal, they provide a good reference for future scholars to explore the integration of information technology and curriculum.Most scholars believe that the essence of the integration of information technology and the teaching of Civic Studies in universities is to change the traditional teaching teacher-centered teaching mode, giving rise to new teaching concepts and generating new teaching modes, rather than simply the essence of the integration of information technology with the teaching of Civics and Political Science is to change the traditional teacher-centered teaching model [11], to give rise to new teaching concepts and new teaching modes, rather than simply overlaying information technology with Civics and Political Science. [12] proposes that in the teaching of ideological and political science classes, we cannot use the relationship between “objectives and content” to deal with the “general approach” but must consider the nature and characteristics of ideological and political theory classes and analyze in depth the relationship between multimedia teaching and multimedia classes in ideological and political science classes. We must consider the nature and characteristics of ideological and political theory classes and analyze in depth the confused border between multimedia teaching and multimedia courseware and the theoretical and technical logic of the conflict between learning from technology and learning with technology. [13] specifically described several aspects, including the specific performance of application, the principles of application, and the ideas of application. They mentioned that it is necessary to fully follow the basic principles, fully clarify the ideas, change the ideology, fully apply information technology to ideological and political education, and enhance the education vitality and vitality.In [14], it is necessary to take the cultivation of students’ core literacy as the core, guide ideological and political education workers to break out of the traditional thinking stereotypes, establish “big data thinking,” and follow and grasp the “The law of communication,” innovative ideas, channels, and methods of publicity and education. [15] said that it needs to be divided into clear targets and needs to be implemented in steps and stages. Secondly, it is crucial to choose the right information technology tools for teaching, to make appropriate use of multimedia courseware, to use motion pictures to raise questions at the right time, to provoke students to think, and to deepen their understanding of knowledge receiving twice the result with half the effort.Some scholars have also conducted research on teaching of Civic Studies from three aspects: schools, teachers, and students. In [16] from the macroscopic and microscopic perspectives, it is proposed that the macroscopic level should be coordinated and planned to create multiparty cooperation, and the microscopic level should strengthen the professional information literacy mechanism of Civics teachers and deepen the sense of participation of students in Civics education. At the same time, it has a greater impact in establishing a whole-person, all-round, and whole-process ideological education and realizes the comprehensive integration of the real and virtual education environment [17]. [18] analyzed the impact of new media on college students from both positive and negative aspects, thus further from combining new media with ideological and political education work. [8] raised the issue of the current situation of the application of information technology; the author’s investigation found that in the current teaching of ideological and political theory courses in colleges and universities, there are certain misunderstandings about the application of information technology, there is a deviation in the understanding of the application of information technology, information technology is not organically combined with traditional forms of teaching, and the application of information technology is not ideal and puts forward the corresponding application of ideas; she proposed to establish a new concept of information technology teaching, the build advanced information technology teaching mode, and innovate information technology teaching methods. [19] proposed that the current problems in the application of information technology in ideological and political education mainly include the lack of self-reflection by teachers in the process of applying information technology.


## 3. Self-Learning of Weights Based on Bayesian Networks and the Great Entropy Criterion

### 3.1. Bayesian Network Construction

For a problem such as multimodal transport path optimization that requires the construction of a multiobjective optimization model, its multilevel, multiattribute characteristics can be expressed by constructing a Bayesian network that reflects the cause-and-effect relationship in the solution of this objective optimization problem due to the interrelatedness of its overall objective, subobjectives, and the influencing factors of each subobjective [20]. Figure 1 shows the Bayesian network diagram of a basic multilevel, multiattribute, multiobjective decision problem.Figure 1
Basic Bayesian network diagram.As can be seen in Figure1, the top and bottom layers of the Bayesian network graph are purely goals and attributes, and the remaining intermediate layers contain both attribute and coal layers. The intermediate layers are attribute values for the upper layer and goal values for the lower layer, and the straight lines between the different layers represent the causal relationships between them.The CPT (Canadian Pacific Telecommunications) in this paper is not the conditional probability of each attribute, but the initial weight and the attribute value of each attribute relative to the target. CPT value is a binary structure of a Bayesian network that represents the initial weight of an attribute relative to its upper-level evaluation metric and its own attribute value.


### 3.2. AHP Method Weighting Interval Estimation Model

In the process of self-learning, the generation of random variables has a random nature; by using the method of interval estimation, the generation interval of random variables is modified to enhance the robustness of the self-learning process. In the AHP method, let the judgement matrixBn be generated according to the small changes of the judgement matrix Bn∗ in the ideal state, so the optimal value of Bn can be obtained through the set of weights in Bn∗. Define the set of optimal weights as Wi∗=ω1∗,ω2∗,⋯,ωn∗; according to the principle of pairwise comparison, aij∗ωj∗=ωi∗, it is possible to obtain
(1)ai1∗,ai2∗,⋯,ain∗×Wn∗=nωi∗.That is,(2)Bn∗×Wn∗=nWn∗.According to the EM method, we can obtain(3)Bn×Wn=βmaxWn,where βmax is defined as the maximum eigenvalue of the Bn matrix. When Bn is the optimal judgement matrix, βmax=n, in other cases. The difference between βmax>n. βmax and n represent the extent to which the judgment matrix Bn differs from the ideal state. Equations (2) and (3) give Bn∗Wn∗≤βmaxWn∗. Therefore, the weight interval estimation model can be obtained by converting it to a most-valued problem, i.e.,
(4)s.t.minωi∗,maxωi∗,0n∗Wn∗≤βmaxWn∗,0≤ωi∗≤1,∑i=1nωi∗=1.MATLAB calculates the weight estimation model to obtain a range of subjective weights for each target attribute of the APH (analytical hierarchy process) method, to determine the range for the random set in the self-learning process, and further to specify its weight value within the specified range of self-learning fluctuations [20].


### 3.3. Information Entropy and the Criterion of Great Entropy

Shannon was the first to apply the concept of entropy in information theory and further developed the idea of information entropy. The idea of information entropy is that when more information is available, the uncertainty generated decreases, so information entropy is used to define the resilience of a system state. The basic formula for information entropy is(5)HX=Hp1,p2,⋯,pn=−k∑i=1npilnpi,where pi is defined as the probability of occurrence of the i-th attribute, which in this paper can be interpreted as the weight of the i-th attribute, and Hp1,p2,⋯,pn is the information entropy function [21]. There are some differences in the definition of the formula for information entropy depending on the variables.Whenpi is an equal probability event,
(6)H=−k×n1nln1n=klnn.Whenpi is a continuous variable, the associated measure function cx is introduced and the derivation of the information entropy obtained is given by
(7)Hx=Hf=−∫fxlnfxcxdx.The criterion of great entropy was first proposed and applied to information theory by E. T. Jaynes, according to which the result that maximises the entropy value (or conditional entropy) under a known constraint should be chosen as the most objective result [22]. The maximum entropy value indicates the least subjective assumptions arising from the lack of objective data on parameters, and in the application of the criterion of great entropy in weighting studies, the subjective factors are least influential and more objective at this point.When the variable is a probability density function, the expression for the great entropy criterion is(8)maxH=−∫Rfxlnfxdx,t.X≥b,∫Rxifxdx=Mi,i=1,2,⋯,m=1,∫Rfxdx=1,where R is the set of variables, fx is the density function of the variables, Mi is the moment of origin corresponding to the i-th row, and b is the constraint of the variables.In this paper, the variables are the weights of the corresponding attribute values and the set of variables are discrete points [23]. Therefore, according to the great entropy criterion, we can derive the formula for the great entropy of the weight:
(9)maxE=−∑k=1mωklnωks.t.∑j∈Niωij=10≤ωij≤1.


### 3.4. The Combination of Bayesian Great Entropy and Self-Learning

Let the random sample beX1,X2,⋯,Xm and its set to be M. ω is the initial weight value of the CPT attribute of a node in the Bayesian network. In a Bayesian network with a total objective for path optimisation, the sum of the values of the attributes of the same layer under each object is 1. The relationship between the ω values of the three different attributes under attribute 1 of the second layer should satisfy ω1+ω2+ω3=1. Α is defined as a node attribute in the self-learning optimization process, which represents a node in the Bayesian network above. PαXk represents the α node’s CPT under a random sample of Xk the prior probability value, which corresponds to the corresponding attribute value of the CPT of the Bayesian network in the self-learning process [25].The value ofω is assumed to be an equally likely event in order to facilitate better learning of the value of ω. The objective of the method is to maximise the value of PwM=∏k=1PwXk, PwM, and PwM representing the total value of the combined weights of the random sample sets generated in the constructed Bayesian network.PwXk represents the total value of the combined weights of the random samples Xk generated in the constructed Bayesian network. The weights are introduced into the self-learning process by using the AHP as the initial solution, and the gradient descent method is used to solve the problem using the greedy hill climbing method. By using the weights in each iteration as the gradient of the self-learning by lnPwM, the solution is finally converged to a local optimum that meets the conditions. The computational steps of self-learning are as follows:
(1)
Find the gradient. For each node in the Bayesian network on the attributes and weight indicators to carry out the gradient calculation of the weight of that node, as shown in the formula:(10)∂lnPwM∂ω=∑k=1MPαXkω(2)
Iteration is performed based on the derived gradient, whered is denoted as the learning rate for the multiple found gradient steps and d is a given constant, as shown in the following equation:(11)ω⟵ω+d×∂lnPwM∂ω(3)
The resulting iterated weights are newly normalized to ensure that the weight values of all attributes of the same layer under a node satisfy∑ωi=1(4)
The goal of the end of self-learning is to set the maximum value of the information entropy of the weights. When the weight is not the maximum value, the weight is used as the initial weight and then iterated again; when the maximum value is reached, the iteration is stopped and the output is the self-learning weight based on Bayesian and great entropy combined with subjective and objective methods


### 3.5. Bayesian and Great Entropy-Based Weight Self-Learning Steps

Based on the previous step-by-step statement, a step-by-step summary of Bayesian and great entropy-based weight self-learning is shown in Figure2.
(1)
The subobjectives of the path optimization model are analyzed to construct a multilevel, multiobjective Bayesian network [26](2)
The constructed Bayesian network is analyzed by the AHP method to obtain the corresponding subjective weight values, i.e., the initial weight values of each attribute before the self-learning operation(3)
Generate a self-learning random sampleM whose set is X1,X2,⋯,Xm(4)
Input the great entropy principle equationE as the objective of iterative termination(5)
Iterative learning is performed according to the Bayesian and great entropy-based self-learning method proposed in this paper [27](6)
The iteration is terminated when the condition of the great entropy principle is satisfied. The outputωi is the final weight of the corresponding nodes in the Bayesian network based on the Bayesian and great entropy-based weight self-learning method for each node in the Bayesian networkFigure 2
Flow chart for self-learning of weights based on Bayesian and great entropy.


### 3.6. Example Analysis

A college Civics classroom wants to transport a batch of goods from Civics vocabulary O to Civics vocabulary E. The four civics vocabulary and transport options as shown in table1, 2 and 3.Table 1
Unit freight rates and transport schedules between the various Civics terms.
O-AA-BB-CC-DD-ERailway50/570/950/14112/20120/28Highway35/475/1195/1890/15110/20Aviation80/1100/2120/3130/4154/4Water transport45/660/12M/M75/16100/30Table 2
Unit transit costs and transit schedules between different modes of transport.
RailwayHighwayAviationWater transportRailway0/02/22/22/2Highway2/20/01/12/2Aviation2/2/1/0/01/1Water transport2/22/21/10/0Table 3
Table of demand for goods for each Civic glossary.
OABCDE0057008The ant colony algorithm developed by MATLAB is used to solve the problem. The ant colony size is 50, the pheromone importance is 1, the heuristic factor importance is 0.2, the pheromone evaporation is 0.1, and the maximum number of iterations is 1 000. The total transportation cost is 3,482 yuan, the total transportation time is 84 h, and the objective function change curve is shown in Figure3.Figure 3
Evolutionary curve of the ant colony algorithm.From the experimental results of the calculations, it can be seen that transportation cost and transportation time are originally two contradictory objectives, and how to make the two balanced depends on the enterprise’s decision. The solution in this paper is to seek to minimise the transport time of the enterprise under the objective of minimising the cost of the enterprise. Alternatively, one can set objective one asf1 and objective two as f2 and convert multiple objectives F=f1∗f2 into a single objective to solve for [29].Under the influence of course informatization, the new mode of “Internet+Education” has been gradually developed in all universities, and many new teaching modes have emerged, providing impetus for the widespread development of the teaching mode of “Internet+Consciousness and Politics Course.” Minister Chen Baosheng suggested that the ideological and political work of universities should be combined online and offline. Online is to make good use of technical means and network position to provide effective teaching resources to the educated. The role of various activity carriers should be given full play, so that they can give full play to their own dynamic role. This involves making good use of the network. This means making good use of the role of the Internet and the continuous enrichment of teaching resources through the Internet.As shown in Figure4, after the clustering of different Civics knowledge points, students can also get some online teaching resources and teaching tutorials through the Internet to complete a series of knowledge and theory learning; offline fully embodies the leading role of the Civics teacher, answering questions and solving problems encountered by the educated online learning, and no longer repeating the relevant theoretical knowledge. In this process, the teacher should analyze the problems encountered in detail, so as to truly realize the purpose of “internalising the knowledge in the heart and externalising it in action,” thus improving the effectiveness of teaching [12].Figure 4
Clustering of different Civics knowledge points.The characteristics and laws of the curriculum of higher education civics course determine that offline ideological and political education is necessary and important. After passing Bayesian estimation, offline ideological and political education is the first to grasp the teaching needs and promote online education on the basis of grasping the basic teaching requirements. As shown in Figure5, it can be known that the ideology class in colleges and universities has a strong ideological nature and students themselves are less receptive to more theoretical knowledge, so the transmission of much theoretical knowledge must be done with repeated explanations and careful guidance from teachers. When teaching, the teacher chooses the appropriate method of delivery according to the real needs of the recipient. For example, situational teaching methods and discussion methods can be used to communicate with students emotionally, so that students can consciously generate enthusiasm for learning the subject and help them deepen their understanding of it; teachers must learn to use the thinking of the Internet to carry out online ideological and political education.Figure 5
Different Bayesian estimates.


## 3.1. Bayesian Network Construction

For a problem such as multimodal transport path optimization that requires the construction of a multiobjective optimization model, its multilevel, multiattribute characteristics can be expressed by constructing a Bayesian network that reflects the cause-and-effect relationship in the solution of this objective optimization problem due to the interrelatedness of its overall objective, subobjectives, and the influencing factors of each subobjective [20]. Figure 1 shows the Bayesian network diagram of a basic multilevel, multiattribute, multiobjective decision problem.Figure 1
Basic Bayesian network diagram.As can be seen in Figure1, the top and bottom layers of the Bayesian network graph are purely goals and attributes, and the remaining intermediate layers contain both attribute and coal layers. The intermediate layers are attribute values for the upper layer and goal values for the lower layer, and the straight lines between the different layers represent the causal relationships between them.The CPT (Canadian Pacific Telecommunications) in this paper is not the conditional probability of each attribute, but the initial weight and the attribute value of each attribute relative to the target. CPT value is a binary structure of a Bayesian network that represents the initial weight of an attribute relative to its upper-level evaluation metric and its own attribute value.


## 3.2. AHP Method Weighting Interval Estimation Model

In the process of self-learning, the generation of random variables has a random nature; by using the method of interval estimation, the generation interval of random variables is modified to enhance the robustness of the self-learning process. In the AHP method, let the judgement matrixBn be generated according to the small changes of the judgement matrix Bn∗ in the ideal state, so the optimal value of Bn can be obtained through the set of weights in Bn∗. Define the set of optimal weights as Wi∗=ω1∗,ω2∗,⋯,ωn∗; according to the principle of pairwise comparison, aij∗ωj∗=ωi∗, it is possible to obtain
(1)ai1∗,ai2∗,⋯,ain∗×Wn∗=nωi∗.That is,(2)Bn∗×Wn∗=nWn∗.According to the EM method, we can obtain(3)Bn×Wn=βmaxWn,where βmax is defined as the maximum eigenvalue of the Bn matrix. When Bn is the optimal judgement matrix, βmax=n, in other cases. The difference between βmax>n. βmax and n represent the extent to which the judgment matrix Bn differs from the ideal state. Equations (2) and (3) give Bn∗Wn∗≤βmaxWn∗. Therefore, the weight interval estimation model can be obtained by converting it to a most-valued problem, i.e.,
(4)s.t.minωi∗,maxωi∗,0n∗Wn∗≤βmaxWn∗,0≤ωi∗≤1,∑i=1nωi∗=1.MATLAB calculates the weight estimation model to obtain a range of subjective weights for each target attribute of the APH (analytical hierarchy process) method, to determine the range for the random set in the self-learning process, and further to specify its weight value within the specified range of self-learning fluctuations [20].


## 3.3. Information Entropy and the Criterion of Great Entropy

Shannon was the first to apply the concept of entropy in information theory and further developed the idea of information entropy. The idea of information entropy is that when more information is available, the uncertainty generated decreases, so information entropy is used to define the resilience of a system state. The basic formula for information entropy is(5)HX=Hp1,p2,⋯,pn=−k∑i=1npilnpi,where pi is defined as the probability of occurrence of the i-th attribute, which in this paper can be interpreted as the weight of the i-th attribute, and Hp1,p2,⋯,pn is the information entropy function [21]. There are some differences in the definition of the formula for information entropy depending on the variables.Whenpi is an equal probability event,
(6)H=−k×n1nln1n=klnn.Whenpi is a continuous variable, the associated measure function cx is introduced and the derivation of the information entropy obtained is given by
(7)Hx=Hf=−∫fxlnfxcxdx.The criterion of great entropy was first proposed and applied to information theory by E. T. Jaynes, according to which the result that maximises the entropy value (or conditional entropy) under a known constraint should be chosen as the most objective result [22]. The maximum entropy value indicates the least subjective assumptions arising from the lack of objective data on parameters, and in the application of the criterion of great entropy in weighting studies, the subjective factors are least influential and more objective at this point.When the variable is a probability density function, the expression for the great entropy criterion is(8)maxH=−∫Rfxlnfxdx,t.X≥b,∫Rxifxdx=Mi,i=1,2,⋯,m=1,∫Rfxdx=1,where R is the set of variables, fx is the density function of the variables, Mi is the moment of origin corresponding to the i-th row, and b is the constraint of the variables.In this paper, the variables are the weights of the corresponding attribute values and the set of variables are discrete points [23]. Therefore, according to the great entropy criterion, we can derive the formula for the great entropy of the weight:
(9)maxE=−∑k=1mωklnωks.t.∑j∈Niωij=10≤ωij≤1.


## 3.4. The Combination of Bayesian Great Entropy and Self-Learning

Let the random sample beX1,X2,⋯,Xm and its set to be M. ω is the initial weight value of the CPT attribute of a node in the Bayesian network. In a Bayesian network with a total objective for path optimisation, the sum of the values of the attributes of the same layer under each object is 1. The relationship between the ω values of the three different attributes under attribute 1 of the second layer should satisfy ω1+ω2+ω3=1. Α is defined as a node attribute in the self-learning optimization process, which represents a node in the Bayesian network above. PαXk represents the α node’s CPT under a random sample of Xk the prior probability value, which corresponds to the corresponding attribute value of the CPT of the Bayesian network in the self-learning process [25].The value ofω is assumed to be an equally likely event in order to facilitate better learning of the value of ω. The objective of the method is to maximise the value of PwM=∏k=1PwXk, PwM, and PwM representing the total value of the combined weights of the random sample sets generated in the constructed Bayesian network.PwXk represents the total value of the combined weights of the random samples Xk generated in the constructed Bayesian network. The weights are introduced into the self-learning process by using the AHP as the initial solution, and the gradient descent method is used to solve the problem using the greedy hill climbing method. By using the weights in each iteration as the gradient of the self-learning by lnPwM, the solution is finally converged to a local optimum that meets the conditions. The computational steps of self-learning are as follows:
(1)
Find the gradient. For each node in the Bayesian network on the attributes and weight indicators to carry out the gradient calculation of the weight of that node, as shown in the formula:(10)∂lnPwM∂ω=∑k=1MPαXkω(2)
Iteration is performed based on the derived gradient, whered is denoted as the learning rate for the multiple found gradient steps and d is a given constant, as shown in the following equation:(11)ω⟵ω+d×∂lnPwM∂ω(3)
The resulting iterated weights are newly normalized to ensure that the weight values of all attributes of the same layer under a node satisfy∑ωi=1(4)
The goal of the end of self-learning is to set the maximum value of the information entropy of the weights. When the weight is not the maximum value, the weight is used as the initial weight and then iterated again; when the maximum value is reached, the iteration is stopped and the output is the self-learning weight based on Bayesian and great entropy combined with subjective and objective methods


## 3.5. Bayesian and Great Entropy-Based Weight Self-Learning Steps

Based on the previous step-by-step statement, a step-by-step summary of Bayesian and great entropy-based weight self-learning is shown in Figure2.
(1)
The subobjectives of the path optimization model are analyzed to construct a multilevel, multiobjective Bayesian network [26](2)
The constructed Bayesian network is analyzed by the AHP method to obtain the corresponding subjective weight values, i.e., the initial weight values of each attribute before the self-learning operation(3)
Generate a self-learning random sampleM whose set is X1,X2,⋯,Xm(4)
Input the great entropy principle equationE as the objective of iterative termination(5)
Iterative learning is performed according to the Bayesian and great entropy-based self-learning method proposed in this paper [27](6)
The iteration is terminated when the condition of the great entropy principle is satisfied. The outputωi is the final weight of the corresponding nodes in the Bayesian network based on the Bayesian and great entropy-based weight self-learning method for each node in the Bayesian networkFigure 2
Flow chart for self-learning of weights based on Bayesian and great entropy.


## 3.6. Example Analysis

A college Civics classroom wants to transport a batch of goods from Civics vocabulary O to Civics vocabulary E. The four civics vocabulary and transport options as shown in table1, 2 and 3.Table 1
Unit freight rates and transport schedules between the various Civics terms.
O-AA-BB-CC-DD-ERailway50/570/950/14112/20120/28Highway35/475/1195/1890/15110/20Aviation80/1100/2120/3130/4154/4Water transport45/660/12M/M75/16100/30Table 2
Unit transit costs and transit schedules between different modes of transport.
RailwayHighwayAviationWater transportRailway0/02/22/22/2Highway2/20/01/12/2Aviation2/2/1/0/01/1Water transport2/22/21/10/0Table 3
Table of demand for goods for each Civic glossary.
OABCDE0057008The ant colony algorithm developed by MATLAB is used to solve the problem. The ant colony size is 50, the pheromone importance is 1, the heuristic factor importance is 0.2, the pheromone evaporation is 0.1, and the maximum number of iterations is 1 000. The total transportation cost is 3,482 yuan, the total transportation time is 84 h, and the objective function change curve is shown in Figure3.Figure 3
Evolutionary curve of the ant colony algorithm.From the experimental results of the calculations, it can be seen that transportation cost and transportation time are originally two contradictory objectives, and how to make the two balanced depends on the enterprise’s decision. The solution in this paper is to seek to minimise the transport time of the enterprise under the objective of minimising the cost of the enterprise. Alternatively, one can set objective one asf1 and objective two as f2 and convert multiple objectives F=f1∗f2 into a single objective to solve for [29].Under the influence of course informatization, the new mode of “Internet+Education” has been gradually developed in all universities, and many new teaching modes have emerged, providing impetus for the widespread development of the teaching mode of “Internet+Consciousness and Politics Course.” Minister Chen Baosheng suggested that the ideological and political work of universities should be combined online and offline. Online is to make good use of technical means and network position to provide effective teaching resources to the educated. The role of various activity carriers should be given full play, so that they can give full play to their own dynamic role. This involves making good use of the network. This means making good use of the role of the Internet and the continuous enrichment of teaching resources through the Internet.As shown in Figure4, after the clustering of different Civics knowledge points, students can also get some online teaching resources and teaching tutorials through the Internet to complete a series of knowledge and theory learning; offline fully embodies the leading role of the Civics teacher, answering questions and solving problems encountered by the educated online learning, and no longer repeating the relevant theoretical knowledge. In this process, the teacher should analyze the problems encountered in detail, so as to truly realize the purpose of “internalising the knowledge in the heart and externalising it in action,” thus improving the effectiveness of teaching [12].Figure 4
Clustering of different Civics knowledge points.The characteristics and laws of the curriculum of higher education civics course determine that offline ideological and political education is necessary and important. After passing Bayesian estimation, offline ideological and political education is the first to grasp the teaching needs and promote online education on the basis of grasping the basic teaching requirements. As shown in Figure5, it can be known that the ideology class in colleges and universities has a strong ideological nature and students themselves are less receptive to more theoretical knowledge, so the transmission of much theoretical knowledge must be done with repeated explanations and careful guidance from teachers. When teaching, the teacher chooses the appropriate method of delivery according to the real needs of the recipient. For example, situational teaching methods and discussion methods can be used to communicate with students emotionally, so that students can consciously generate enthusiasm for learning the subject and help them deepen their understanding of it; teachers must learn to use the thinking of the Internet to carry out online ideological and political education.Figure 5
Different Bayesian estimates.


## 4. Conclusions

With the continuous development of information technology, informationisation in education has become an unstoppable trend. Information technology has begun to be applied in various subject areas of education, providing convenient and effective teaching aid to better achieve subject teaching objectives. In this paper, we propose a comprehensive weight determination method combining subjective and objective with subjective assignment and then self-learning combined with Bayesian networks and great entropy criterion. By grasping the characteristics of information technology and the nature of the curriculum of the university’s Civics course, the in-depth integration of the two is promoted.



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*Source: 2782473-2022-05-12.xml*