# Evaluation of Political Classroom Teaching Quality in Universities Based on DA-BP Algorithm

Authors: Hui Guan; Dongxiang Zhu
Journal: Security and Communication Networks
 (2022)

---

## Abstract

In order to improve the evaluation accuracy of political classroom teaching quality in universities, a DA-BP-based evaluation method of political classroom teaching quality in universities is proposed. First of all, in constructing the teaching quality evaluation system of university political courses using the framework of behavior theory, the system consists of 4 primary indicators and 15 secondary indicators. Second, the university’s 2008–2013 annual teaching quality assessment scores for the teaching quality of political courses were used as the input data of the DA-BP network, and the 2013–2016 teaching evaluation scores were used as the algorithm’s test data. In order to test the prediction accuracy of the DA-BP algorithm, it is compared with the GA-BP algorithm, PSO-BP algorithm, and BP algorithm. The research results show that DA-BP has higher prediction accuracy than other algorithms and can better evaluate the teaching quality of political courses.


---

## Body

## 1. Introduction

China has strengthened the research on the evaluation of political education for nearly three decades, and a series of documents issued at important meetings of the Party Central Committee, The Central Committee of the Communist Party of China and the State Council issued a series of documents on strengthening the quality education of college students and improving their political awareness. Several opinions of the CPC Central Committee on Strengthening and Improving Political Work, etc., have all put forward clear requirements for the evaluation of political education. In terms of research results, the book Principles of Political Education, edited by Professors Qiu Weiguang and Zhang Yaocan, contains a chapter on the evaluation of political education, and the book Methodology of Political Education, edited by Professor Zheng Yongting, also puts forward a more comprehensive and targeted evaluation method. There are also many similar works on the evaluation of political education. In addition, there are more research results in this field in academic literature, such as Yu Manhua’s “The Construction of a New Mechanism of Political Education Evaluation in Universities,” Xiong Linwu’s “A Trial of Political Education Evaluation in Higher Education,” and Wang Maosheng’s “The Scientific Connotation and Characteristics of Political Education Evaluation”.On the basis of paying sufficient attention to the evaluation of political education, China has gradually begun to refine it, including the evaluation of political education work, the evaluation of political education courses, the evaluation of the timeliness of political education, etc. [1]. So far, there has been a lot of progress. In the evaluation of the quality of political theory courses, as early as the 1980s, the idea of quality assurance was explored by some scholars in China. It was explored by some scholars in China and further disseminated in the 1990s, but it has not yet formed a large impact. Wang Anping has analyzed and researched how to improve the quality of teaching in political science courses at a macrolevel, Zhang Aijun believes that theory should be linked to practice in teaching ideology, based on classroom teaching, enriching students’ theoretical knowledge reserves and strengthening the use of display, Zhang Sennian first proposed the concept of teaching quality assurance system for political theory courses in universities that was put forward for the first time by Zhang Sennian, and the significant role of the construction of this system in improving the teaching quality of political science courses in universities was demonstrated; Gao Haisheng innovatively introduced the teaching quality monitoring system after Zhang Sennian proposed the teaching quality assurance system, upgrading the static evaluation system to dynamic. Although there are more research results for the evaluation of teaching quality of political science courses in universities, they basically stay on qualitative evaluation, without quantitative evaluation, which would make the research lack credibility and persuasiveness. Therefore, this paper applies the dragonfly algorithm (DA) to optimize the parameters of the initial connection weights cj and ωij and thresholds ε and θj of the BP model, and proposes a DA-BP algorithm-based method for evaluating the quality of teaching in college political courses, which fills the gap of no quantitative research. From the result, we can know that the DA-BP algorithm can quickly and accurately evaluate the teaching quality, providing a new algorithm process for the subsequent evaluation of the teaching quality of college political courses.


## 2. Dragonfly Algorithm

The dragonfly algorithm (DA) is a new metaheuristic algorithm designed to solve global optimization problems by simulating the foraging and migration behavior of the dragonfly population in nature. It has the characteristics of simple implementation, few optimization parameters, short convergence time, and so on. It is widely used in various fields to optimize different problems. In this study, due to the need to combine the DA with the neural network algorithm, the band algorithm needs to have the characteristics of fast convergence and fewer steps, so the DA can well meet this demand. The original thoughts of the dragonfly algorithm comes from the simulation of group behavior of dragonflies such as predation and avoidance (Figure1), and the flight (evolutionary) search mechanism can be expressed in five steps: separation, alignment, aggregation, food attraction, and dispersal of natural enemies [2].Figure 1
The main idea of the dragonfly algorithm for finding the best calculation.In the DA, individual dragonflies forage and seek advantage through five types of behavior: collision avoidance behavior, pairing behavior, aggregation behavior, foraging behavior, and enemy avoidance behavior, which can be shown in those following formulas: the position vector update strategy for collision avoidance behavior is given in equation (1) [3].(1)Si=−∑j=1NX−Xi,where X is the current position of the dragonfly individual; Xj is the position of the j th neighboring dragonfly individual, and N is the number of neighboring dragonfly individuals. The strategy for updating the position vector for pairing behavior is shown in equation (2).(2)Ai=−∑j=1NVjN,where Vj is the velocity of the jth neighboring dragonfly individual. The location vector update strategy for aggregation behavior is shown in equation (3).(3)Ci=−∑j=1NXjN−X.The location vector update strategy for foraging behavior is shown in equation (4).(4)Fi=X+−X,where X+ is the food source location (the current optimal solution). The location direction update strategy for enemy avoidance behavior is as in equation (5).(5)Ei=X−+X,where X− is the natural enemy location (the current worst solution). In summary of the five dragonfly group behaviors above, the step vector update strategy for individual dragonflies is shown in equation (6).(6)ΔXt+1=sSi+aAi+cCi+fFi+eEe+ωΔXt,where s, a, c, f, and e are the weights of the five dragonfly group behaviors, respectively; ω denotes the inertia weight; and t is the number of current iterations as shown in equation (7). The dragonfly position update strategy is as follows:(7)Xt+1=Xt+ΔXt+1.This study adapts the optimal solution-seeking mechanism of the dragonfly algorithm as follows:(1)
We introduce an optimization mechanism for the initial population of individuals. The initial population of dragonflies is randomly selected, and a feasible solution is obtained through the initial optimization calculation [4].(2)
We add a local reasonableness mechanism [5]. The reasonableness review operator is added to the dragonfly algorithm, which iterates through the computational dimensions of individual dragonflies, retaining the reasonable localities after the flight and eliminating the localities that are not reasonable after the flight, to speed up the algorithm’s computation rate [6].The computational flow of the adapted dragonfly algorithm consists of 12 steps [7].Step 1: The initialization of the calculation parameters, taking into account the short-term optimal scheduling model of the stepped power station, sets the number of dragonfly populationsN = 40 and the number of iterations MAXiter = 200.Step 2: The initial population is generated by using the reservoir operating level of the stepped power station as the decision variable, based on the upper and lower limits of the water level operating areaZMaxi,t, ZMini,t using Xr=ZMini,t+e∗Zmaxi,t−Zmini,t, and the POA algorithm is used to find the optimal solution for the selected dragonfly based on the principle of single-station optimization, ensuring that at least one dragonfly in the initial population is a feasible solution. The number of iterations is iter = 1, and the dragonfly dimension is r = 0.Step 3: We determine whether there is a neighbor of dragonflyXk. If there are neighbors, we go to step 4; otherwise, we go to step 5.Step 4: We calculate the population evolution according to equations (1–5): separation Sr, alignment Ar, aggregation Cr, food attraction Fr, and natural enemy dispersal Er. We calculate the step size of individual dragonfly position update according to equation (6).Step 5: The dragonfly has no neighbors, andXr=e∗Zmaxi,t−Zmini,t was used to randomize the flight of individual dragonflies to step 6.Step 6: If func2 > func1 means that the change in position of the dimension is beneficial to the objective function, then the position of the current dimension is updated andXtk=Xt+1k. If func2 < func1 means that the change in position of the current dimension is not beneficial to the objective function, then ΔXkt + 1 = 0. If func2 < func1, which means that the change in position of the current dimension is not beneficial to the objective function, then ΔXkt + 1 = 0.Step 7: Ifr = r + 1, we determine whether all the dimensions of the dragonfly have been updated, and if r = rmax, we go to step 8; otherwise, we go to step 4.Step 8: We update the position of an individual dragonflyXk, Xt+1=Xt+Xt+1.Step 9: We makek = k + 1 and proceed to the optimization of a new individual dragonfly. If k ≥ N, then all dragonfly individuals are updated and we go to step 11. If k < N, then we go to step 3 for a loop.Step 10: We find the best dragonfly in the current iteration, compare it with the food position, and update the food position according to whether it is better than the objective function value of the food position, let iter = iter + 1. If iter ≥ MAXiter, we go to step 11; otherwise, we go to step 3.Step 11: We return the food position of the current iteration as the best result, and the calculation is finished.


## 3. Evaluation Based on DA-BP

### 3.1. BP Neural Network

The BP neural networks are a widely used forward-looking neural network model to obtain the impact of changes on the analysis objectives when different influences are involved [8]. Increasing the number of layers can improve accuracy and reduce error, but it can also complicate the network and thus increase the training time for the network weights. Increasing the number of neurons can be used to improve the error accuracy, and the training effect is easier to observe and adjust than increasing the number of layers [9]. Since three-layer neural networks have good function approximation and the structure is easy to design and operable, increasing the number of neurons in the hidden layer is usually preferred. According to Kolmogorov’s theorem, a neural network with n input units, 2n+1 intermediate units, and m output units is capable of expressing any mapping [10]. Since the input layer needs to input the data samples to score each investigator and the number of each investigator, while the output layer needs to output only the annual course quality score, so, there is only one neuron. Therefore, the neural network built in this paper uses 2 input units, 5 intermediate units, and 1 output unit, the structure of which is schematically shown in Figure 2.Figure 2
BP network topology diagram.If the number of layers of the input layer and output layer of the neural network ism and 1, respectively, and the number of layers of its hidden layer is p, then the formula of the neural network can be described as shown in equation (8).(8)Xi+1=fXi=11+exp−∑j=1pcjbj+εj=1,2,…,p.The formal speciesf is the implied layer excitation function, ε is the threshold value of the output layer, and cj and bj are the connection weights from the implied layer to the output layer and the node outputs of the implied layer, respectively. [11].Therefore, the output expression of the hidden layer nodes of the BP neural network is shown in equation (9).(9)bj=11+exp−∑i=1mωij+θji=1,2,…,m,where ωij is the connection weight from the input layer to the hidden layer; θj is the threshold value of the nodes in the hidden layer.Since the prediction accuracy of the BP neural network is easily regulated by the initial weights, slight differences in the initial link weights may lead to very different simulation results.cj, ωijand thresholdsε, θjparameters are the relevant calculation coefficients of the prediction algorithm. In this paper, the DA is used to optimize the initial weights of the BP neural network and adjust its threshold.Standard BP was used in the fields of simulation and blockchain, which itself has a more rigorous derivation process and sufficient theoretical basis, especially in the research of nonlinear systems, and has been studied and used by a large number of scholars [12]. However, through a large number of nonlinear experimental studies, it is found that there are several shortcomings as follows [13]:(1)
Slow convergence of the learning process.Although there are many factors that affect the convergence speed of the algorithm, the generalization ability itself is the most import factor, for example, in the selection of parameters, which, if not chosen properly, can make the training count reach tens of thousands of times before the desired accuracy is achieved.(2)
Convergence to the minimum value point is not guaranteed.For the BP neural networks trained with gradient descent as a learning rule, the interrelated points are made up of various parabolic surfaces, all of which are extremely small individuals. Therefore, in the process of training the object under study using neural networks, it is often the case that local minima occur, and once encountered, the whole neural network will fall into a local minimum and will remain in such a state from which it cannot emerge [14].(3)
The network structure is difficult to determine.The structural parameters of the network include a large number of factors. Therefore, it is important to always be aware of the possibility of redundancy in building the network, and if too much of this problem occurs, the training time will significantly increase as a result.To compensate for these shortcomings, the DP algorithm is introduced, which can solve the problems of local optimum and full learning rate. [15] The process of the DA-BP-based algorithm for evaluating the quality of teaching in college political courses is summarized as follows:Step 1: We initialize the BP neural network model and determine the network structure. The number of layers, the type of transfer function and training function, and the number of nodes in each layer of the BP neural network are determined according to the data samples; the data are read and preprocessed, and the data are divided into the training set and the test set. [16].Step 2: The DA algorithm uses real number encoding, the connection weightscj and ωij and thresholds ε and θj are encoded as a whole, and the spatial search dimension of the algorithm is m. Assuming that the number of nodes in the input layer, implicit layer, and output layer is R, S1, and S2, respectively, the encoding length S can be expressed as equation (10) as follows:(10)S=RS+S1+S2+S1S2.Step 3: The DA is initialized with a population size ofN and a maximum number of iterations of T [17].Step 4: We randomly initialize the step vectorΔX and randomly generate the initial position X of the dragonfly individuals.Step 5: We make the current number of iterationst=1, input the training set into the BP neural network, evaluate the fitness of dragonflies according to the fitness formula (11) of dragonfly algorithm species, and rank the best solution for the current training [18], where the mean squared deviation is chosen as the fitness function, and its expression is(11)fitness=1k∑i=1kyi−yi^2.In the formula,yi and yi^ are the actual and expected outputs of the ith sample, respectively, and k is the number of samples.Step 6: We update the food source locationX+ (current optimal solution) and the natural enemy location X− (current worst solution), and update the five behavioral weights s,a,c,f,e and the inertia weight ω.Step 7: We updateS,A,C,E,F according to equations (3–7).Step 8: We update the step vector and position vector according to equations (8) and (9).Step 9: If the number of iterationst༞T, we save the most connected weights cj and ωij and thresholds ε and θj; otherwise, t=t+1 and we return to step 5.Step 10: We use the connection weightscj and ωij and thresholds ε and θj corresponding to the optimal solution and make predictions. [19].


## 3.1. BP Neural Network

The BP neural networks are a widely used forward-looking neural network model to obtain the impact of changes on the analysis objectives when different influences are involved [8]. Increasing the number of layers can improve accuracy and reduce error, but it can also complicate the network and thus increase the training time for the network weights. Increasing the number of neurons can be used to improve the error accuracy, and the training effect is easier to observe and adjust than increasing the number of layers [9]. Since three-layer neural networks have good function approximation and the structure is easy to design and operable, increasing the number of neurons in the hidden layer is usually preferred. According to Kolmogorov’s theorem, a neural network with n input units, 2n+1 intermediate units, and m output units is capable of expressing any mapping [10]. Since the input layer needs to input the data samples to score each investigator and the number of each investigator, while the output layer needs to output only the annual course quality score, so, there is only one neuron. Therefore, the neural network built in this paper uses 2 input units, 5 intermediate units, and 1 output unit, the structure of which is schematically shown in Figure 2.Figure 2
BP network topology diagram.If the number of layers of the input layer and output layer of the neural network ism and 1, respectively, and the number of layers of its hidden layer is p, then the formula of the neural network can be described as shown in equation (8).(8)Xi+1=fXi=11+exp−∑j=1pcjbj+εj=1,2,…,p.The formal speciesf is the implied layer excitation function, ε is the threshold value of the output layer, and cj and bj are the connection weights from the implied layer to the output layer and the node outputs of the implied layer, respectively. [11].Therefore, the output expression of the hidden layer nodes of the BP neural network is shown in equation (9).(9)bj=11+exp−∑i=1mωij+θji=1,2,…,m,where ωij is the connection weight from the input layer to the hidden layer; θj is the threshold value of the nodes in the hidden layer.Since the prediction accuracy of the BP neural network is easily regulated by the initial weights, slight differences in the initial link weights may lead to very different simulation results.cj, ωijand thresholdsε, θjparameters are the relevant calculation coefficients of the prediction algorithm. In this paper, the DA is used to optimize the initial weights of the BP neural network and adjust its threshold.Standard BP was used in the fields of simulation and blockchain, which itself has a more rigorous derivation process and sufficient theoretical basis, especially in the research of nonlinear systems, and has been studied and used by a large number of scholars [12]. However, through a large number of nonlinear experimental studies, it is found that there are several shortcomings as follows [13]:(1)
Slow convergence of the learning process.Although there are many factors that affect the convergence speed of the algorithm, the generalization ability itself is the most import factor, for example, in the selection of parameters, which, if not chosen properly, can make the training count reach tens of thousands of times before the desired accuracy is achieved.(2)
Convergence to the minimum value point is not guaranteed.For the BP neural networks trained with gradient descent as a learning rule, the interrelated points are made up of various parabolic surfaces, all of which are extremely small individuals. Therefore, in the process of training the object under study using neural networks, it is often the case that local minima occur, and once encountered, the whole neural network will fall into a local minimum and will remain in such a state from which it cannot emerge [14].(3)
The network structure is difficult to determine.The structural parameters of the network include a large number of factors. Therefore, it is important to always be aware of the possibility of redundancy in building the network, and if too much of this problem occurs, the training time will significantly increase as a result.To compensate for these shortcomings, the DP algorithm is introduced, which can solve the problems of local optimum and full learning rate. [15] The process of the DA-BP-based algorithm for evaluating the quality of teaching in college political courses is summarized as follows:Step 1: We initialize the BP neural network model and determine the network structure. The number of layers, the type of transfer function and training function, and the number of nodes in each layer of the BP neural network are determined according to the data samples; the data are read and preprocessed, and the data are divided into the training set and the test set. [16].Step 2: The DA algorithm uses real number encoding, the connection weightscj and ωij and thresholds ε and θj are encoded as a whole, and the spatial search dimension of the algorithm is m. Assuming that the number of nodes in the input layer, implicit layer, and output layer is R, S1, and S2, respectively, the encoding length S can be expressed as equation (10) as follows:(10)S=RS+S1+S2+S1S2.Step 3: The DA is initialized with a population size ofN and a maximum number of iterations of T [17].Step 4: We randomly initialize the step vectorΔX and randomly generate the initial position X of the dragonfly individuals.Step 5: We make the current number of iterationst=1, input the training set into the BP neural network, evaluate the fitness of dragonflies according to the fitness formula (11) of dragonfly algorithm species, and rank the best solution for the current training [18], where the mean squared deviation is chosen as the fitness function, and its expression is(11)fitness=1k∑i=1kyi−yi^2.In the formula,yi and yi^ are the actual and expected outputs of the ith sample, respectively, and k is the number of samples.Step 6: We update the food source locationX+ (current optimal solution) and the natural enemy location X− (current worst solution), and update the five behavioral weights s,a,c,f,e and the inertia weight ω.Step 7: We updateS,A,C,E,F according to equations (3–7).Step 8: We update the step vector and position vector according to equations (8) and (9).Step 9: If the number of iterationst༞T, we save the most connected weights cj and ωij and thresholds ε and θj; otherwise, t=t+1 and we return to step 5.Step 10: We use the connection weightscj and ωij and thresholds ε and θj corresponding to the optimal solution and make predictions. [19].


## 4. Empirical Analysis

This empirical study selected research data obtained from the ideology and political course teaching and research department of the School of Marxism through the end-of-year review of the course between 2008 and 2016 at a 985 university in a central province of China. At the end of each teaching term, the teaching evaluation team composed of senior teachers will score according to the students’ reaction after class, the teaching progress, and the results of the teachers’ assessment at the end of the year. Due to the large number of data samples, all samples have a total of 2550 groups of data. Limited by the length of the article, only some data samples are listed. The data are a true and detailed record of the teaching and learning of the school’s ideology course over a period of more than ten years. Before systematically analyzing the data, it is necessary to construct an index system for evaluating the teaching quality of the university’s political classroom. On the basis of the reference of previous relevant domestic studies and the teaching characteristics of this 985 university, the indexes for evaluating the teaching quality of the university’s political course are constructed into a comprehensive index system of four primary indicators and 15 secondary indicators, as shown in Figure3. The first-level indicators are as follows: adequacy of teaching preparation, effectiveness of teaching operation, strength of teaching management, and constructiveness of practical teaching. Among them, teaching preparation is the preliminary preparation and preparatory work for the formal start of teaching, which is the first important link to ensure the quality of teaching. The teaching operation stage refers to the operation of various factors in the whole teaching progress. Teaching management includes internal management and external monitoring, and is an organic combination of the two. Practical teaching, as the name implies, is practice oriented, and it usually refers to the use of practical teaching to enable students to learn theoretical knowledge in a practical and interesting atmosphere, through which the effect of heuristic teaching is achieved, and it is the students who more deeply grasp the theoretical knowledge learned in the classroom, while strengthening their own comprehensive ability and ideological quality. The adequacy of the teaching preparation is divided into the excellence of the teaching environment, the scientific nature of the teaching objectives, the rigor of the teacher selection, and the teacher’s preparation for the class. The effectiveness of the teaching operation is divided into the adequacy of the teaching content, the effectiveness of the teaching methods, the manipulation of teaching skills, and the realization of teaching results. The strength of teaching management is divided into the scientific formulation of teaching policies, the adequacy of teaching policy implementation, the comprehensiveness of teaching security work, and the implementation of teaching evaluation and supervision. The constructiveness of practical teaching is divided into the directivity of practical teaching, the relevance of practical teaching content, and the adequacy of practical teaching effect.Figure 3
Evaluation system of teaching quality of political classroom in higher education.This research uses the maximum value method to standardize the data, and according to the 1–9 scale method, the scores of each year’s political teaching quality evaluation indexes are constructed according to the relative order of merit of each evaluation index by the two-by-two comparison method as shown in Table1, and the total evaluation scores of political teaching in each year are shown in Table 2.Table 1
Scores of the indicators for evaluating the quality of teaching and learning in civic studies by year.
Indicator number20082009201020112012201320142015201610.01450.02470.03010.04170.04670.04790.05210.05460.060720.02640.03720.04190.05120.05270.05380.05690.06320.064930.02720.05300.05930.06470.06540.06740.07050.07360.077140.31130.30180.32970.33950.34560.35480.38740.40960.426550.24870.31850.38640.42450.43750.43960.44580.47800.486460.19210.21990.29900.31840.32880.34670.36710.38430.397670.28310.30170.32070.34550.36710.37890.38010.39660.409680.04610.05910.08420.10220.13460.24790.26330.31090.356790.01450.02660.03750.05150.05580.06710.07890.08040.1104100.19610.22400.30940.31050.32490.35830.37910.39030.4116110.25450.30990.31020.32330.34090.35020.37840.38020.3915120.01840.01900.02220.03280.04060.04530.04970.05210.0544130.09910.10510.25470.26740.28940.29060.34020.35790.3908140.18740.20640.30550.32110.35770.37820.39560.40010.4214150.28410.30110.34030.35320.37090.39210.40130.42060.4465Table 2
Cronbach’s alpha test value.
YearCronbach’s alpha value20080.70720090.75020100.86820110.83620120.67020130.78520140.73120150.63220160.836According to the statistical principle, the reliability and validity of the data obtained from the survey are tested to verify the credibility and effectiveness of the statistical data. Cronbach’s alpha value is selected as the reliability-check index, and the KMO value and Bartlett test value are selected as the validity test index. The test results of reliability and validity are shown in Tables3 and 4.Table 3
KMO value and the Bartlett sphericity test value.
Kaiser–Meyer–Olkin metric0.954Bartlett’s sphericity test0.00Table 4
Overall evaluation scores for teaching political course by year.
Year200820092010201120122013201420152016Score2.40352.60843.33113.44753.65863.91884.06644.35244.5561According to the principle of the reliability test, Cronbach’s alpha value must be greater than 0.60 to pass the reliability test, so all indicators in this survey meet the reliability requirements. In terms of validity test, the author mainly tests the structural validity of the data. First, the KMO value and Bartlett sphericity value of the data are calculated to test whether they are suitable for validity test. When the KMO value is greater than 0.5 and the Bartlett sphericity test value is 0, the data are suitable for validity test. The KMO value and the Bartlett sphericity test value of the data are shown in Table4.It can be seen that the KMO value of the data is greater than 0.5, and the Bartlett sphericity test value is 0, indicating that the data validity meets the standard.The results of the overall evaluation scores were obtained by the following equation:(12)OS=∑i=1nSi•Ti,where OS denotes the overall score, i denotes the number of indicators, Si denotes the score for each indicator, and Ti denotes the weight of each indicator.In order to test the goodness of the evaluation results of the quality of teaching in higher education political classroom, there are two evaluation indicators, of which the RMSE is the most commonly used error analysis algorithm, followed by the indicator correlation coefficientR. The formulas are expressed as equations (13) and (14).(13)RMSE=1n∑k=1nxk−xk^2,(14)R=∑k=1nxkxk^∑k=1nxk2·1∑k=1nxk2,where xk and xk^ denote the actual and predicted values of the kth sample, respectively, and n denotes the number of samples. RMSE is mainly used to measure the dispersion of the model, and R is mainly used to illustrate the degree of correlation between predicted and actual values.According to the literature, the quality of teaching in the political classroom was classified into five levels, namely, very good, better, average, poor, and very poor, and their evaluation levels are divided as shown in Table5.Table 5
Classification of evaluation levels.
Rating rangeEvaluation results[4.1, 5)Very good[3.3, 4.1)Better[2.5, 3.3)Fair[1.7, 2.5)Poor[1, 1.7)Very poorAs can be seen from Table5, with the passage of time, the teaching supervision has become more and more perfect, and the teaching quality has also been improved accordingly, which is more in line with the facts and further verifies the authenticity of the data.The collected data were scored by experts, and a total of 10 sets of data were obtained. The data were divided into two parts, the first 6 sets of data were used as the training set to build the DA-BP classroom teaching evaluation model, and the last 3 sets of data were used as the test set to test the correctness of the DA-BP classroom teaching evaluation model. The structure of the BP neural network is 2-5-1, the set learning rate is 0.1, the training accuracy is 10-6, and the maximum training times of the BP neural network are 20000. The hidden layer function adopts the sigmoid function, which is characterized by its differentiability, continuity of itself and derivative function, nonlinear characteristics, and fast convergence, so it is selected as the hidden layer excitation function. The prediction result of the algorithm is shown in Figure4; as can be seen from the figure, the DA-BP algorithm more accurately predicted the actual score after 2014.Figure 4
DA-BP political course teaching quality evaluation results.After completing the DA-BP algorithm through training to more accurately predict the teaching quality ratings of a university’s political course from 2014 to 2016, the algorithm was used to continue to test the prediction accuracy of the algorithm and to continue to simulate the teaching quality ratings of the university’s political course after 2016. The statistical data after 2016 also come from the university. Due to the significant role of the long-term year-end comprehensive evaluation of teaching quality in improving teaching quality, the university will maintain the year-end evaluation until 2021. In this case, it is necessary to choose the simulation prediction method of the DA-BP algorithm, and there are three traditional prediction methods. [20].


### 4.1. Single-Step Forecast

Atk = 1, each subsequent prediction step is based on the result of the previous prediction step and is a one-step prediction. The process is as follows: with a total of m predicted values and input parameters Tn,Tn+1,…,Tn+m in the prediction model, each input parameter is entered into the prediction model separately and the result is the predicted value of Tn+m+1 at the next point, which is repeated in this way to predict the value of Tn+m+2. The value of Tn+1,Tn+2,…Tn+m+1 is used as input data to obtain the value of Tn+m+2.


### 4.2. Multistep Prediction

Whenk > 1, the previously collected data, we tentatively setmhere, are input into the network together; then, the relevant values of the subsequent nodesTn+m+1,Tn+m+2,…,Tn+m+k, multistep prediction under the background of big data output, and the prediction result will have a large error, and the reason is mainly because of iteration and the related error in the previous step. It will continue to accumulate in the results of the next step. Yes, the error will expand step by step. When the error reaches a certain level, there may be a problem that the training network cannot achieve convergence, and the ideal optimal solution cannot be obtained.


### 4.3. Rolling Forecast

The rolling forecast is based on a single-step forecast and then predicts the follow-up after inputting the expected value. It is assumed that the observation sequence of the specific algorithm isTn,Tn+1,…,Tn+m, and the output value is the observation value of Tn+m+1. Then, we use Tn+m+1 and Tn+1,Tn+2,…Tn+m as input data to evaluate and estimate Tn+m+2, get the output observations Tn+m+2, and then take Tn+m+2 and Tn+2,Tn+3,…,Tn+m+1 as the input number Tn+m+3 for estimation continuously in this way.Iterative feedback adjustment can realize the predictive analysis of research objects in the future.Rolling forecasting is a combination method with a lot of advantages and strong practical significance. Therefore, this paper adopts the rolling forecast for analysis and processing. During the simulation, MATLAB software was used to call the function, and the simulation function selects the sim function. The learning rate, the number of learning times, and the number of hidden layers are set to 0.005, 10,000, and 3 according to the size of the sample data. The network is adjusted by the input of sample data. The network has been adjusted many times. When the learning rate is adjusted to 0.0001, the number of learning times is 30,000, the number of hidden layers is 5, and the number of nodes in each layer is 480, 320, 160, 160, and 320..The simulation results and error analysis are shown in Figures5 and 6.Figure 5
DA-BP political course teaching quality evaluation for 2017–2021.Figure 6
Prediction error.The figure indicates that the absolute error of the predicted value of the DA-BP algorithm is always controlled within 0.15, which is a reasonable error. In order to further check the rationality of the error, the relative error value obtained by dividing the absolute error value by the actual value is subjected to statistical analysis, resulting in Figure7.Figure 7
Relative error.It can be seen from Figure7 that the relative error of the prediction result of the DA-BP algorithm fluctuates between 0.2% and 27%, and the maximum relative error does not exceed 27%. According to the statistical law, it conforms to the prediction accuracy and can be used for simulation.To test the accuracy and effectiveness of the DA-BP model, DA-BP was compared with several different improved algorithms. The DA-BP algorithm was reconstructed with Python language. The logsigmoid function was selected as the hidden layer excitation function. The learning rate was set to 0.01, and the maximum number of iterations was set to 20000. The survey data from 2017 to 2020 were used as the training dataset input model for training. The parameters of other algorithms were chosen as follows: particle swarm optimization (PSO) algorithm parameters: maximum number of iterationsT = 100, population size N = 10, learning factor C1 = C2 = 2, and search interval [−1, 1]; genetic algorithm (GA) parameters: population size N = 10, maximum number of iterations T = 100, crossover probability PC = 0.7, and variation probability Pm = 0.1; BP neural network parameters: input layer nodes are 25, hidden layer nodes are 50, output layer node is 1, maximum training number is 1000, transfer function of hidden layer is logsig, the output layer transfer function is purelin, the training function is trainlm, the learning rate is 0.01, and the training error target is 0.001. The comparison results of different algorithms are shown in Figure 8 and Table 6.Figure 8
Results of teaching quality evaluation of different algorithms for political courses.Table 6
Comparison of evaluation results of different algorithms.
MethodTraining setTesting setRMSERRMSERDA-BP0.00900.98710.03940.9801GA-BP0.01910.97310.04050.9694PSO-BP0.01850.96460.04170.9583BP0.01630.95280.04280.9437As can be seen from Figure8, except the DA-BP algorithm, the GA-BP algorithm is the most accurate in prediction. However, the minimum absolute error of the prediction result of the GA-BP algorithm is 0.20, and the minimum relative error is 30%, both higher than the DA-BP algorithm.From Figure8 and Table 6, it can be seen that the DA-BP algorithm outperforms the remaining three algorithms in terms of prediction accuracy, where the RMSE value calculated according to the DA-BP algorithm is the lowest and the correlation coefficient R value is the largest, which indicates that the predicted teaching quality evaluation value of the political course by the DA-BP algorithm is very close to its actual value and the DA-BP algorithm has the highest prediction accuracy. Second, it is not difficult to find that both the GA-BP and PSO-BP algorithms yielded better prediction accuracy than the BP neural network algorithm, which may be due to the fact that the first two algorithms both optimize the BP algorithm to some extent, thus improving the accuracy.


## 4.1. Single-Step Forecast

Atk = 1, each subsequent prediction step is based on the result of the previous prediction step and is a one-step prediction. The process is as follows: with a total of m predicted values and input parameters Tn,Tn+1,…,Tn+m in the prediction model, each input parameter is entered into the prediction model separately and the result is the predicted value of Tn+m+1 at the next point, which is repeated in this way to predict the value of Tn+m+2. The value of Tn+1,Tn+2,…Tn+m+1 is used as input data to obtain the value of Tn+m+2.


## 4.2. Multistep Prediction

Whenk > 1, the previously collected data, we tentatively setmhere, are input into the network together; then, the relevant values of the subsequent nodesTn+m+1,Tn+m+2,…,Tn+m+k, multistep prediction under the background of big data output, and the prediction result will have a large error, and the reason is mainly because of iteration and the related error in the previous step. It will continue to accumulate in the results of the next step. Yes, the error will expand step by step. When the error reaches a certain level, there may be a problem that the training network cannot achieve convergence, and the ideal optimal solution cannot be obtained.


## 4.3. Rolling Forecast

The rolling forecast is based on a single-step forecast and then predicts the follow-up after inputting the expected value. It is assumed that the observation sequence of the specific algorithm isTn,Tn+1,…,Tn+m, and the output value is the observation value of Tn+m+1. Then, we use Tn+m+1 and Tn+1,Tn+2,…Tn+m as input data to evaluate and estimate Tn+m+2, get the output observations Tn+m+2, and then take Tn+m+2 and Tn+2,Tn+3,…,Tn+m+1 as the input number Tn+m+3 for estimation continuously in this way.Iterative feedback adjustment can realize the predictive analysis of research objects in the future.Rolling forecasting is a combination method with a lot of advantages and strong practical significance. Therefore, this paper adopts the rolling forecast for analysis and processing. During the simulation, MATLAB software was used to call the function, and the simulation function selects the sim function. The learning rate, the number of learning times, and the number of hidden layers are set to 0.005, 10,000, and 3 according to the size of the sample data. The network is adjusted by the input of sample data. The network has been adjusted many times. When the learning rate is adjusted to 0.0001, the number of learning times is 30,000, the number of hidden layers is 5, and the number of nodes in each layer is 480, 320, 160, 160, and 320..The simulation results and error analysis are shown in Figures5 and 6.Figure 5
DA-BP political course teaching quality evaluation for 2017–2021.Figure 6
Prediction error.The figure indicates that the absolute error of the predicted value of the DA-BP algorithm is always controlled within 0.15, which is a reasonable error. In order to further check the rationality of the error, the relative error value obtained by dividing the absolute error value by the actual value is subjected to statistical analysis, resulting in Figure7.Figure 7
Relative error.It can be seen from Figure7 that the relative error of the prediction result of the DA-BP algorithm fluctuates between 0.2% and 27%, and the maximum relative error does not exceed 27%. According to the statistical law, it conforms to the prediction accuracy and can be used for simulation.To test the accuracy and effectiveness of the DA-BP model, DA-BP was compared with several different improved algorithms. The DA-BP algorithm was reconstructed with Python language. The logsigmoid function was selected as the hidden layer excitation function. The learning rate was set to 0.01, and the maximum number of iterations was set to 20000. The survey data from 2017 to 2020 were used as the training dataset input model for training. The parameters of other algorithms were chosen as follows: particle swarm optimization (PSO) algorithm parameters: maximum number of iterationsT = 100, population size N = 10, learning factor C1 = C2 = 2, and search interval [−1, 1]; genetic algorithm (GA) parameters: population size N = 10, maximum number of iterations T = 100, crossover probability PC = 0.7, and variation probability Pm = 0.1; BP neural network parameters: input layer nodes are 25, hidden layer nodes are 50, output layer node is 1, maximum training number is 1000, transfer function of hidden layer is logsig, the output layer transfer function is purelin, the training function is trainlm, the learning rate is 0.01, and the training error target is 0.001. The comparison results of different algorithms are shown in Figure 8 and Table 6.Figure 8
Results of teaching quality evaluation of different algorithms for political courses.Table 6
Comparison of evaluation results of different algorithms.
MethodTraining setTesting setRMSERRMSERDA-BP0.00900.98710.03940.9801GA-BP0.01910.97310.04050.9694PSO-BP0.01850.96460.04170.9583BP0.01630.95280.04280.9437As can be seen from Figure8, except the DA-BP algorithm, the GA-BP algorithm is the most accurate in prediction. However, the minimum absolute error of the prediction result of the GA-BP algorithm is 0.20, and the minimum relative error is 30%, both higher than the DA-BP algorithm.From Figure8 and Table 6, it can be seen that the DA-BP algorithm outperforms the remaining three algorithms in terms of prediction accuracy, where the RMSE value calculated according to the DA-BP algorithm is the lowest and the correlation coefficient R value is the largest, which indicates that the predicted teaching quality evaluation value of the political course by the DA-BP algorithm is very close to its actual value and the DA-BP algorithm has the highest prediction accuracy. Second, it is not difficult to find that both the GA-BP and PSO-BP algorithms yielded better prediction accuracy than the BP neural network algorithm, which may be due to the fact that the first two algorithms both optimize the BP algorithm to some extent, thus improving the accuracy.


## 5. Conclusions

The DA-BP algorithm can more accurately evaluate the teaching quality of previous political courses, and more importantly, can accurately predict and evaluate the future teaching quality, thus putting forward the following countermeasures and suggestions: (1) to strengthen the importance of college leaders and increase supervision; (2) to innovate and improve the teaching mode and teaching methods; (3) to fully utilize the enthusiasm of student subjects; (4) to strengthen student guidance in conjunction with the actual situation; (5) to clarify the purpose of teaching evaluation and evaluation indicators and the evaluation system; and (6) to strengthen case teaching to improve students’ understanding and perception. This paper proposes a DA-BP-based method for evaluating the quality of teaching in college political courses in order to improve the accuracy of teaching quality evaluation in college political courses. Based on the behavioral theoretical framework and the actual teaching situation of the university, an evaluation system for the teaching quality of college political classroom teaching is constructed, the expert evaluation scores of 15 evaluation indicators related to the teaching quality of college politics classroom teaching are input into the DA-BP algorithm model, and the model outputs after training and predicts teaching quality scores. The other methods mentioned in this paper can also simulate to a certain extent, but the accuracy is not as good as the DA-BP algorithm. The teaching quality evaluation of university political classroom under the DA-BP algorithm model can be used as a reference method to provide some teaching institutions. Since the founding of New China, the state has invested a lot of energy in the political education of college students. The implementation of good political education is related to everyone, and it is imperative to have good and accurate teaching quality evaluation standards. The country hopes that every college student can receive a good and correct political education, because they are the future of the country, and they shoulder the heavy responsibility of building the country. If there is a problem with the ideology and cognition of college students, they will no longer have a sense of their national identity. If we have a sense of identity, then this will be a disaster for this country. Fortunately, various experts, scholars, and universities have gradually attached importance to political education, and at the same time, an evaluation system for the quality of education is indispensable. This paper provides a method reference for the evaluation of the quality of political teaching of college students in the future.



---

*Source: 3391881-2022-09-22.xml*