# Quality Evaluation Model of Education in Remote Learning and IoT Based on College Students’ Quality Cultivation

Authors: Yawen Huang; Jing Wang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The current model for evaluating ideological and political education is mostly based on a weighted evaluation of related educational parameters, like the structure of the school and the structure of the teachers. In this cycle, the impact of college students’ general development on the construction of their ideological and political education is not considered, which makes evaluations less exact by and large. To work on the exactness of the evaluation model, you ought to upgrade the construction of the evaluation model, with the quality of education for college students as the center advancement. Education quality evaluation calculation and model parameter output calculation are integrated. In terms of the quality of training for college students, simulation tests reveal that the proposed model has the lowest blunder esteem and the highest general evaluation precision. This makes it easier to decide what kind of ideological and political education is best.


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## Body

## 1. Introduction

Confidential colleges and colleges are a significant piece of China’s advanced education framework. They help modern education grow in ways that are both diverse and individualized. At the same time, the way that ideological and political courses are put together in private colleges has a direct effect on how college students think and what they value. So the way that ideological and political courses are put together in private colleges is very important.One of them is that the quality of ideological and political showing in the homeroom straightforwardly affects the improvement of gifts. The research contribution for the supposed “showing quality evaluation” is to assess the standard, effectiveness, and quality of showing exercises in light of the showing objectives and to gauge and pass judgment on the benefit of educating [1, 2]. The purpose of the purported “showing quality evaluation” is to evaluate the standard, efficacy, and quality of demonstration exercises in light of the demonstration objectives as well as to estimate and make a determination on the educational value. This paper takes the relevant parameters of high-quality cultivation and applies them to the correlation coefficient of the evaluation model. It then finds the best parameters for the model.In terms of teaching and learning, school administration, research and education, school buildings, and other areas, it appears that the Internet of Things (IoT) is revolutionizing education. There are several different categories that realistic IoT deployments in educational settings might fall under. IoT device data, for instance, might be utilized to improve management practices and educational pursuits. The effectiveness of education and evaluation might be increased by utilizing learner analytics, enhancing knowledge accessibility, and customizing learning through the use of linked devices. This section covers a variety of topics that can profit from the use of IoT [3]. A weighted evaluation of associated educational criteria, such as the structure of the school and the structure of the instructors, is the foundation of the present model for assessing ideological and political education. Evaluations are often less accurate in this cycle since the influence of college students’ overall growth on the formation of their ideological and political education is not taken into account. You should improve the assessment model’s structure in order to increase its accuracy, with the growth of college student education as the central focus, and the combination of model parameter outcome calculation with education quality rating calculations. Simulation tests show that the suggested model has the minimum error esteem and the best general evaluation precision when it comes to the caliber of instruction for college students. This makes the decision as regards the ideal ideological or political education. The research contribution for the supposed “showing quality evaluation” is to assess the standard, effectiveness, and quality of showing exercises in light of the showing objectives and to gauge and pass judgment on the benefit of educating. With regard to the demonstration objectives, the supposed “showing quality assessment” is aimed at assessing the standard, effectiveness, and quality of demonstration activities and also at predicting and making a decision on the educational value. This study applies the pertinent high-quality cultivation factors to the evaluation model’s correlation coefficient. Then, it determines which model parameters are the best.


## 2. Ideological and Political Education Quality Evaluation Model Based on College Students’ Quality Cultivation

### 2.1. Calculation of Correlation Coefficient between Quality Training and Ideological and Political Education

According toα correlation coefficient calculation formula,
(1)ζik=miniminkXok−Xik+ρmaximaxkXok−XikXok−Xik+ρmaximaxkXok−Xiki=1,2,⋯,m;k=1,2,⋯,n.The corresponding correlation coefficientζiKi=1,2,⋯,7;k=1,2,⋯,12 of each index and the reference sequence are calculated to form a matrix ζ7×12. Each indicator refers to the ideological and political quality index system and satisfaction data corresponding to college students’ quality cultivation and education. Detailed parameters are shown in Table 1.Table 1
Ideological and political quality index system and satisfaction data of college students’ quality cultivation education.
Primary indicatorsSenior indexesThe weighted valuesX schoolX−1 schoolX−2 schoolX−3 schoolX−4 schoolX−5 schoolX−6 schoolX−7 schoolQuality training courses and activitiesNumber of quality training courses0.0263.0067.3270.3672.6474.6264.0068.3264.00Quality training course content0.0464.1967.4270.4673.5664.2661.6667.8461.66Quality training lecture0.0364.7868.6271.6273.2472.6568.7272.6274.62Quality training activities0.0768.1368.7272.6274.6268.4269.1270.6573.26Quality cultivation conditionsQuality training education teachers0.0270.0769.1270.6573.26701667.6172.6673.61Quality education system0.0367.7467.6172.6673.6169.2768.3374.6272.66Quality training books and materials0.0272.8368.3374.6272.6674.2664.2570.1674.06Quality training site and facility construction0.0270.4864.2570.1674.0673.2669.4272.3273.41Quality training atmosphere0.0267.7269.4272.3273.4174.9273.5174.1570.23Quality training channelAccess to information about education0.0467.7170.1674.5274.6568.5274.6573.6276.42Ease of access to educational services0.0567.1368.4373.0673.8374.3573.2675.4272.44Feedback quality training education demand channels0.0569.3167.4272.1174.6569.4374.1675.3468.36Quality training corresponds to ideological and political educationHow to establish consciousness0.1564.0068.3273.5174.1570.2373.5174.1570.23Knowledge of quality trainingEvaluation of the impact of ideological and political education0.0861.6667.8474.6573.6276.4273.2675.4272.44The ability to think and act ideologically and politically is assessed0.1262.2469.2473.2675.4272.4474.1675.3468.36Assessment of ideological and political preferences0.1669.8968.1674.1675.3468.3673.2675.4272.44Ideological and political evaluations of high-quality achievements0.1057.5467.3674.6174.1673.8664.0068.3273.51College quality training corresponds to ideological and political education resultsThe theory of college quality-oriented education is with relation to political and ideological education0.0468.1366.4274.5673.2668.1366.4274.5674.65College quality education practice education, both ideological and political0.0470.4868.2374.8175.2670.4868.2374.8173.26College quality training education managementEducation, both intellectual and political0.0468.7267.3173.4674.1668.7267.3173.4674.16Political and ideological achievements of college quality training education0.0772.2568.4375.2375.2268.1366.4274.5673.51


### 2.2. Evaluation Model Objective and Related Parameter Definition Calculation

#### 2.2.1. The Objective of the Evaluation Model Is Determined

To construct the target matrix of the evaluation model, it is assumed that there arem-th objects and n-th evaluation indicators, and the initial evaluation matrix X=xijmn is constructed, and xij represents the value under the i-th object indicator j.
(2)X=x11⋯x1nx21⋯x2nxm1⋯xmn.The initial matrix is quantified by index, and the matrixR is obtained. Among this, rij=xij/∑i=1mxij,j=1,2,3⋯,n.
(3)R=r11⋯r1nr21⋯r2nrm1⋯rmn.Considering that the entropy value itself is a measure of uncertainty [4, 5], fij is used to represent the uncertainty of the i-th index of the i-th project, and the entropy S of the evaluation is expressed by
(4)Sj=−k×∑i=1mfijlnfij,j=1,2,⋯,i.In formula (4), the values of fij=rij/∑i=1mrij, k=1/lnm, and k are greater than 0, and the definition of entropy weight is expressed by
(5)wj=1−sj∑j=1nej,0≤wj≤1,∑j=1nwj=1.It can be seen from formulas (4) and (5) that the more the information, the more definite the entropy weight and the smaller the entropy [6, 7]. The determination principle of the target entropy of the model is evaluated in detail, as shown in Figure 1.Figure 1
Determination principle of objective entropy of the evaluation model.


#### 2.2.2. Definition of Related Evaluation Quantity of the Target Model

The structure diagram of the definition of the relevant evaluation quantity of the target model is shown in Figure2. Based on the model calculated above, the weight solved by formula (5) is substituted into formula (3) to form the weighted target matrix:
(6)v=vij=w1r11⋯wnr1nw2r21⋯wnr2nw3rm1⋯w3rmn=v11⋯v1nv21⋯v2nvm1⋯vmn.Figure 2
Structure of the definition of relevant evaluation quantity of the target model.The relevant evaluation quantity is defined according to the weight value, and the relevant definition parameters are as follows:(7)di−=∑j=1nvij−vj−2,i=1,2,⋯,m.Definition 1.
Positive ideal solution:(8)v+=maxv1j,v2j,⋯,vmj.Definition 2.
Negative ideal solution:(9)v−=minv1j,v2j,⋯,vmj.
In formulas (8) and (9), the j-th attribute value vj+ of positive ideal solution v+ and the j-th attribute value vj− of negative ideal solution v− are presented.Definition 3.
The distance between the evaluation object and the positive and negative ideal solution:(10)di+=∑j=1nvij−vj+2,i=1,2,⋯,m,Definition 4.
Relative closeness between the evaluation index and the ideal solution:(11)ci=di+di++di−.In formula (11), 0≤ci≤1, i=1,2,3,⋯,m. The structure of the definition of relevant evaluation quantity of the target model is shown in Figure 2.


### 2.3. Ideological and Political Quality Evaluation Quantity Calculation

According to the above target model and its related parameter definition [8, 9], its definition quantity is put into the weight of different indicators at all levels in Table 1, namely, WXX−1, WX−11X−2, WX−12X−2, WX−13X−2, WX−14X−2, and WX−15X−2. Using
(12)R=ri1×m=r1,r2,⋯,rm,the correlation degree of each primary indicator is calculated:(13)RX−11=WX−11X−2×EX−11X−2T,RX−12=WX−12X−2×EX−12X−2T,RX−13=WX−13X−2×EX−13X−2T,RX−14=WX−14X−2×EX−14X−2T,RX−15=WX−15X−2×EX−15X−2T.In the formula,RX−11X−2, RX−12X−2, RX−13X−2, RX−14X−2, and RX−15X−2 are the matrix composed of corresponding data in the table of related values. Finally, the correlation degree of the target layer and index R is calculated: RX=r1,r2,r3,r4,r5=WXX−1RX−11,RX−12,RX−13,RX−14,RX−15.According to the degree of correlation inRX, the order of correlation degree of ideological and political quality under the quality-oriented education of college students in eight universities can be determined as follows.


### 2.4. Integrated Output of Structural Parameters of the Ideological and Political Education Quality Evaluation Model Based on College Students’ Quality Cultivation

The model evaluation indicator system is determined according to the analytic hierarchy process [10] and the 1-9 scale method [11, 12] proposed by Sati, so as to establish the integration matrix X−1 [13, 14]. Finally, a comprehensive evaluation is conducted on indicators at all levels. The following takes teaching ability X−1, an advanced indicator of an efficient teaching quality, and ideological and political teaching X−14 as an example for integrated evaluation.According to the above index data [15, 16] of quality cultivation related to ideological and political quality evaluation, a single-factor evaluation is carried out for thinking quality X−141, and an evaluation quantification matrix D=99,79,79,39 [17, 18] is established, corresponding to excellent,good,medium,andpoor. X−141 is voted, and the results show that 65% of people vote excellent, 25% vote good, and 10% vote medium, and the evaluation matrix is obtained: X−141=0.55,0.15,0.07,0.Similarly,X−142=0.42,0.16,0.10,0; the evaluation matrix composed of the two advanced indicators of X−14 can be obtained comprehensively [19–21]:
(14)RX−14=0.550.150.0700.420.160.100.The conversion score is(15)VX−14=RX−14•ST=0.550.150.0700.420.160.100.The numerical vector isV⟶=80.575.5 [20, 21].Combined with the integration matrixQX−14, the output coefficient of the integration quantitative evaluation model X−14 can be obtained as follows:
(16)JX−14=V¯X−14•QX−14=80.575.5⋅0.0040.004=1.562.The output process of model evaluation structure parameter integration is shown in Figure3.Figure 3
Block diagram of output flow of model evaluation structure parameter integration.


## 2.1. Calculation of Correlation Coefficient between Quality Training and Ideological and Political Education

According toα correlation coefficient calculation formula,
(1)ζik=miniminkXok−Xik+ρmaximaxkXok−XikXok−Xik+ρmaximaxkXok−Xiki=1,2,⋯,m;k=1,2,⋯,n.The corresponding correlation coefficientζiKi=1,2,⋯,7;k=1,2,⋯,12 of each index and the reference sequence are calculated to form a matrix ζ7×12. Each indicator refers to the ideological and political quality index system and satisfaction data corresponding to college students’ quality cultivation and education. Detailed parameters are shown in Table 1.Table 1
Ideological and political quality index system and satisfaction data of college students’ quality cultivation education.
Primary indicatorsSenior indexesThe weighted valuesX schoolX−1 schoolX−2 schoolX−3 schoolX−4 schoolX−5 schoolX−6 schoolX−7 schoolQuality training courses and activitiesNumber of quality training courses0.0263.0067.3270.3672.6474.6264.0068.3264.00Quality training course content0.0464.1967.4270.4673.5664.2661.6667.8461.66Quality training lecture0.0364.7868.6271.6273.2472.6568.7272.6274.62Quality training activities0.0768.1368.7272.6274.6268.4269.1270.6573.26Quality cultivation conditionsQuality training education teachers0.0270.0769.1270.6573.26701667.6172.6673.61Quality education system0.0367.7467.6172.6673.6169.2768.3374.6272.66Quality training books and materials0.0272.8368.3374.6272.6674.2664.2570.1674.06Quality training site and facility construction0.0270.4864.2570.1674.0673.2669.4272.3273.41Quality training atmosphere0.0267.7269.4272.3273.4174.9273.5174.1570.23Quality training channelAccess to information about education0.0467.7170.1674.5274.6568.5274.6573.6276.42Ease of access to educational services0.0567.1368.4373.0673.8374.3573.2675.4272.44Feedback quality training education demand channels0.0569.3167.4272.1174.6569.4374.1675.3468.36Quality training corresponds to ideological and political educationHow to establish consciousness0.1564.0068.3273.5174.1570.2373.5174.1570.23Knowledge of quality trainingEvaluation of the impact of ideological and political education0.0861.6667.8474.6573.6276.4273.2675.4272.44The ability to think and act ideologically and politically is assessed0.1262.2469.2473.2675.4272.4474.1675.3468.36Assessment of ideological and political preferences0.1669.8968.1674.1675.3468.3673.2675.4272.44Ideological and political evaluations of high-quality achievements0.1057.5467.3674.6174.1673.8664.0068.3273.51College quality training corresponds to ideological and political education resultsThe theory of college quality-oriented education is with relation to political and ideological education0.0468.1366.4274.5673.2668.1366.4274.5674.65College quality education practice education, both ideological and political0.0470.4868.2374.8175.2670.4868.2374.8173.26College quality training education managementEducation, both intellectual and political0.0468.7267.3173.4674.1668.7267.3173.4674.16Political and ideological achievements of college quality training education0.0772.2568.4375.2375.2268.1366.4274.5673.51


## 2.2. Evaluation Model Objective and Related Parameter Definition Calculation

### 2.2.1. The Objective of the Evaluation Model Is Determined

To construct the target matrix of the evaluation model, it is assumed that there arem-th objects and n-th evaluation indicators, and the initial evaluation matrix X=xijmn is constructed, and xij represents the value under the i-th object indicator j.
(2)X=x11⋯x1nx21⋯x2nxm1⋯xmn.The initial matrix is quantified by index, and the matrixR is obtained. Among this, rij=xij/∑i=1mxij,j=1,2,3⋯,n.
(3)R=r11⋯r1nr21⋯r2nrm1⋯rmn.Considering that the entropy value itself is a measure of uncertainty [4, 5], fij is used to represent the uncertainty of the i-th index of the i-th project, and the entropy S of the evaluation is expressed by
(4)Sj=−k×∑i=1mfijlnfij,j=1,2,⋯,i.In formula (4), the values of fij=rij/∑i=1mrij, k=1/lnm, and k are greater than 0, and the definition of entropy weight is expressed by
(5)wj=1−sj∑j=1nej,0≤wj≤1,∑j=1nwj=1.It can be seen from formulas (4) and (5) that the more the information, the more definite the entropy weight and the smaller the entropy [6, 7]. The determination principle of the target entropy of the model is evaluated in detail, as shown in Figure 1.Figure 1
Determination principle of objective entropy of the evaluation model.


### 2.2.2. Definition of Related Evaluation Quantity of the Target Model

The structure diagram of the definition of the relevant evaluation quantity of the target model is shown in Figure2. Based on the model calculated above, the weight solved by formula (5) is substituted into formula (3) to form the weighted target matrix:
(6)v=vij=w1r11⋯wnr1nw2r21⋯wnr2nw3rm1⋯w3rmn=v11⋯v1nv21⋯v2nvm1⋯vmn.Figure 2
Structure of the definition of relevant evaluation quantity of the target model.The relevant evaluation quantity is defined according to the weight value, and the relevant definition parameters are as follows:(7)di−=∑j=1nvij−vj−2,i=1,2,⋯,m.Definition 1.
Positive ideal solution:(8)v+=maxv1j,v2j,⋯,vmj.Definition 2.
Negative ideal solution:(9)v−=minv1j,v2j,⋯,vmj.
In formulas (8) and (9), the j-th attribute value vj+ of positive ideal solution v+ and the j-th attribute value vj− of negative ideal solution v− are presented.Definition 3.
The distance between the evaluation object and the positive and negative ideal solution:(10)di+=∑j=1nvij−vj+2,i=1,2,⋯,m,Definition 4.
Relative closeness between the evaluation index and the ideal solution:(11)ci=di+di++di−.In formula (11), 0≤ci≤1, i=1,2,3,⋯,m. The structure of the definition of relevant evaluation quantity of the target model is shown in Figure 2.


## 2.2.1. The Objective of the Evaluation Model Is Determined

To construct the target matrix of the evaluation model, it is assumed that there arem-th objects and n-th evaluation indicators, and the initial evaluation matrix X=xijmn is constructed, and xij represents the value under the i-th object indicator j.
(2)X=x11⋯x1nx21⋯x2nxm1⋯xmn.The initial matrix is quantified by index, and the matrixR is obtained. Among this, rij=xij/∑i=1mxij,j=1,2,3⋯,n.
(3)R=r11⋯r1nr21⋯r2nrm1⋯rmn.Considering that the entropy value itself is a measure of uncertainty [4, 5], fij is used to represent the uncertainty of the i-th index of the i-th project, and the entropy S of the evaluation is expressed by
(4)Sj=−k×∑i=1mfijlnfij,j=1,2,⋯,i.In formula (4), the values of fij=rij/∑i=1mrij, k=1/lnm, and k are greater than 0, and the definition of entropy weight is expressed by
(5)wj=1−sj∑j=1nej,0≤wj≤1,∑j=1nwj=1.It can be seen from formulas (4) and (5) that the more the information, the more definite the entropy weight and the smaller the entropy [6, 7]. The determination principle of the target entropy of the model is evaluated in detail, as shown in Figure 1.Figure 1
Determination principle of objective entropy of the evaluation model.


## 2.2.2. Definition of Related Evaluation Quantity of the Target Model

The structure diagram of the definition of the relevant evaluation quantity of the target model is shown in Figure2. Based on the model calculated above, the weight solved by formula (5) is substituted into formula (3) to form the weighted target matrix:
(6)v=vij=w1r11⋯wnr1nw2r21⋯wnr2nw3rm1⋯w3rmn=v11⋯v1nv21⋯v2nvm1⋯vmn.Figure 2
Structure of the definition of relevant evaluation quantity of the target model.The relevant evaluation quantity is defined according to the weight value, and the relevant definition parameters are as follows:(7)di−=∑j=1nvij−vj−2,i=1,2,⋯,m.Definition 1.
Positive ideal solution:(8)v+=maxv1j,v2j,⋯,vmj.Definition 2.
Negative ideal solution:(9)v−=minv1j,v2j,⋯,vmj.
In formulas (8) and (9), the j-th attribute value vj+ of positive ideal solution v+ and the j-th attribute value vj− of negative ideal solution v− are presented.Definition 3.
The distance between the evaluation object and the positive and negative ideal solution:(10)di+=∑j=1nvij−vj+2,i=1,2,⋯,m,Definition 4.
Relative closeness between the evaluation index and the ideal solution:(11)ci=di+di++di−.In formula (11), 0≤ci≤1, i=1,2,3,⋯,m. The structure of the definition of relevant evaluation quantity of the target model is shown in Figure 2.


## 2.3. Ideological and Political Quality Evaluation Quantity Calculation

According to the above target model and its related parameter definition [8, 9], its definition quantity is put into the weight of different indicators at all levels in Table 1, namely, WXX−1, WX−11X−2, WX−12X−2, WX−13X−2, WX−14X−2, and WX−15X−2. Using
(12)R=ri1×m=r1,r2,⋯,rm,the correlation degree of each primary indicator is calculated:(13)RX−11=WX−11X−2×EX−11X−2T,RX−12=WX−12X−2×EX−12X−2T,RX−13=WX−13X−2×EX−13X−2T,RX−14=WX−14X−2×EX−14X−2T,RX−15=WX−15X−2×EX−15X−2T.In the formula,RX−11X−2, RX−12X−2, RX−13X−2, RX−14X−2, and RX−15X−2 are the matrix composed of corresponding data in the table of related values. Finally, the correlation degree of the target layer and index R is calculated: RX=r1,r2,r3,r4,r5=WXX−1RX−11,RX−12,RX−13,RX−14,RX−15.According to the degree of correlation inRX, the order of correlation degree of ideological and political quality under the quality-oriented education of college students in eight universities can be determined as follows.


## 2.4. Integrated Output of Structural Parameters of the Ideological and Political Education Quality Evaluation Model Based on College Students’ Quality Cultivation

The model evaluation indicator system is determined according to the analytic hierarchy process [10] and the 1-9 scale method [11, 12] proposed by Sati, so as to establish the integration matrix X−1 [13, 14]. Finally, a comprehensive evaluation is conducted on indicators at all levels. The following takes teaching ability X−1, an advanced indicator of an efficient teaching quality, and ideological and political teaching X−14 as an example for integrated evaluation.According to the above index data [15, 16] of quality cultivation related to ideological and political quality evaluation, a single-factor evaluation is carried out for thinking quality X−141, and an evaluation quantification matrix D=99,79,79,39 [17, 18] is established, corresponding to excellent,good,medium,andpoor. X−141 is voted, and the results show that 65% of people vote excellent, 25% vote good, and 10% vote medium, and the evaluation matrix is obtained: X−141=0.55,0.15,0.07,0.Similarly,X−142=0.42,0.16,0.10,0; the evaluation matrix composed of the two advanced indicators of X−14 can be obtained comprehensively [19–21]:
(14)RX−14=0.550.150.0700.420.160.100.The conversion score is(15)VX−14=RX−14•ST=0.550.150.0700.420.160.100.The numerical vector isV⟶=80.575.5 [20, 21].Combined with the integration matrixQX−14, the output coefficient of the integration quantitative evaluation model X−14 can be obtained as follows:
(16)JX−14=V¯X−14•QX−14=80.575.5⋅0.0040.004=1.562.The output process of model evaluation structure parameter integration is shown in Figure3.Figure 3
Block diagram of output flow of model evaluation structure parameter integration.


## 3. Simulation Experiment

### 3.1. Data Screening

When evaluating the importance of indicators, it is very easy to have the problem of inconsistent evaluation directions. For example, when A1 is more important than A2 and A2 is more important than A3, if A3 is judged to be more important than A1, there will be a contradiction. Therefore, it is necessary to conduct a consistency test for the maximum characteristic root and eigenvector comparison. If it passes, the eigenvector is the weight vector. The integration output error test can be carried out in the next step [22–24].


### 3.2. Experimental Data Collation

The experimental weight index data obtained by experimental screening are shown in Table2, and the simulation test import operation is carried out for them.Table 2
Index evaluation weights.
IndicatorsXThe weight ofA in XX−110.4102X−120.1200X−130.2810X−140.0712A10.150 8———0.120 3A20.331 7———0.071 8A30.101 7———0.110 1A4—0.231 2——0.101 2A5—0.150 1——0.028 7A6—0.621 0——0.040 1A7——0.128 1—0.045 2A8——0.351 2—0.081 6A9——0.508 5—0.051 0A10———0.174 80.031 8A11———0.371 20.071 3A12———0.183 10.073 4


### 3.3. Experimental Data Calculation

Assuming that the importance of each indicator is judged and the weight value ofA to X is obtained as shown in Table 2, the consistency test of indicator weight can be calculated as follows. When AR<0.1, this means that the consistency test results of each indicator meet the requirements for target X in mathematics.
(17)AIX=∑i=1nωAIi=0.0062,(18)RIX=∑i=1nwRIi=0.2152,(19)ARX=AIXRIX=0.01083≺0.10.In the calculation process, the fluctuation curve of the weight index corresponding to the targetX is shown in Figure 4.Figure 4
Fluctuation curve of the weight index corresponding to targetX.
(a)
The fluctuation curve of the weight indexAIX corresponding to the target X(b)
The fluctuation curve of the weight indexRIX corresponding to the target X(c)
The fluctuation curve of the weight indexARX corresponding to the target X


### 3.4. Integrate Output Error Testing

As per Reference [10] says, the quality of ideological and political showing in the homeroom can be separated into five grades: awesome, great, general, poor, and exceptionally poor. The grade classification is shown in Table 3.Table 3
Classification of evaluation grades.
Corresponding to the scoreEvaluation3.1,4Very good2.3,3.1Good1.5,2.3General0.7,1.5Poor0,0.7Very poorThe collected data were scored by experts, and a total of 10 sets of data were obtained. The data is divided into two parts. The first six groups of data are used as training sets to establish the FS-MQ classroom teaching evaluation model. The validation population size isN=10, and the maximum iteration times are T=100.The evaluation error data in Table4 can be obtained by combining the data in Figure 4 and Table 3.Table 4
Comparison of evaluation errors of ideological and political education quality.
Comparative model quantityComparative evaluation sampleTest evaluation sampleTZDRTTZDRTHA-MQ0.007 80.884 50.026 20.878 2HA-MQ0.005 10.867 50.028 10.860 0QDP-MQ0.005 70.865 20.034 80.853 1MQ0.006 50.854 10.036 10.834 5From the overall evaluation result of ideological and political teaching, the evaluation result of FS-MQ is better than that of HA-MQ, QDP-MQ, and MQ. When comparing evaluation samples and test evaluation samples, the TZDR of FS-MQ is the lowest and the correlation coefficientT is the highest [25, 26]. This means that the HA-MQ model’s ideological and political showing evaluation value is the closest to the real ideological and political educating evaluation value. The prediction effect is the best (2>FS‐MQ), and the evaluation accuracy of FS-MQ, GA-BP, and QDP-MQ is better than that of MQ.


## 3.1. Data Screening

When evaluating the importance of indicators, it is very easy to have the problem of inconsistent evaluation directions. For example, when A1 is more important than A2 and A2 is more important than A3, if A3 is judged to be more important than A1, there will be a contradiction. Therefore, it is necessary to conduct a consistency test for the maximum characteristic root and eigenvector comparison. If it passes, the eigenvector is the weight vector. The integration output error test can be carried out in the next step [22–24].


## 3.2. Experimental Data Collation

The experimental weight index data obtained by experimental screening are shown in Table2, and the simulation test import operation is carried out for them.Table 2
Index evaluation weights.
IndicatorsXThe weight ofA in XX−110.4102X−120.1200X−130.2810X−140.0712A10.150 8———0.120 3A20.331 7———0.071 8A30.101 7———0.110 1A4—0.231 2——0.101 2A5—0.150 1——0.028 7A6—0.621 0——0.040 1A7——0.128 1—0.045 2A8——0.351 2—0.081 6A9——0.508 5—0.051 0A10———0.174 80.031 8A11———0.371 20.071 3A12———0.183 10.073 4


## 3.3. Experimental Data Calculation

Assuming that the importance of each indicator is judged and the weight value ofA to X is obtained as shown in Table 2, the consistency test of indicator weight can be calculated as follows. When AR<0.1, this means that the consistency test results of each indicator meet the requirements for target X in mathematics.
(17)AIX=∑i=1nωAIi=0.0062,(18)RIX=∑i=1nwRIi=0.2152,(19)ARX=AIXRIX=0.01083≺0.10.In the calculation process, the fluctuation curve of the weight index corresponding to the targetX is shown in Figure 4.Figure 4
Fluctuation curve of the weight index corresponding to targetX.
(a)
The fluctuation curve of the weight indexAIX corresponding to the target X(b)
The fluctuation curve of the weight indexRIX corresponding to the target X(c)
The fluctuation curve of the weight indexARX corresponding to the target X


## 3.4. Integrate Output Error Testing

As per Reference [10] says, the quality of ideological and political showing in the homeroom can be separated into five grades: awesome, great, general, poor, and exceptionally poor. The grade classification is shown in Table 3.Table 3
Classification of evaluation grades.
Corresponding to the scoreEvaluation3.1,4Very good2.3,3.1Good1.5,2.3General0.7,1.5Poor0,0.7Very poorThe collected data were scored by experts, and a total of 10 sets of data were obtained. The data is divided into two parts. The first six groups of data are used as training sets to establish the FS-MQ classroom teaching evaluation model. The validation population size isN=10, and the maximum iteration times are T=100.The evaluation error data in Table4 can be obtained by combining the data in Figure 4 and Table 3.Table 4
Comparison of evaluation errors of ideological and political education quality.
Comparative model quantityComparative evaluation sampleTest evaluation sampleTZDRTTZDRTHA-MQ0.007 80.884 50.026 20.878 2HA-MQ0.005 10.867 50.028 10.860 0QDP-MQ0.005 70.865 20.034 80.853 1MQ0.006 50.854 10.036 10.834 5From the overall evaluation result of ideological and political teaching, the evaluation result of FS-MQ is better than that of HA-MQ, QDP-MQ, and MQ. When comparing evaluation samples and test evaluation samples, the TZDR of FS-MQ is the lowest and the correlation coefficientT is the highest [25, 26]. This means that the HA-MQ model’s ideological and political showing evaluation value is the closest to the real ideological and political educating evaluation value. The prediction effect is the best (2>FS‐MQ), and the evaluation accuracy of FS-MQ, GA-BP, and QDP-MQ is better than that of MQ.


## 4. Conclusion

The ideological education evaluation model has good teaching evaluation. To provide a more accurate evaluation of the educational quality of teaching, to solve the evaluation data quality problem between the ideological education evaluation error and put forward based on the education to cultivate college students. Through the simulation experiments, the evaluation of the accuracy of the model is studied. Later on, the focus will be on the most effective way for improving ideological and political education and quality training, and the model will be refined even more.



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*Source: 1171771-2022-09-05.xml*