# Quality Assessment of Physical Education Teaching in Colleges and Universities Based on Joint Neural Network

Authors: Chengwei Tan
Journal: Mobile Information Systems
 (2022)

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## Abstract

In order to improve the accuracy of physical education teaching quality evaluation in colleges and universities, a new JNE teaching quality evaluation model of physical education is proposed in colleges and universities. Firstly, a set of multi-index evaluation system of college physical education teaching quality based on analytic hierarchy process is constructed from the four dimensions of teaching content, teaching method, teaching attitude, and teaching effect. The score data are used as the input vector of joint neural network (JEN), and the quality level of college physical education (excellent, good, average, and poor) is used as the output vector of JNE, and a teaching quality evaluation model of college physical education courses based on the JNE model is established. Compared with PSO-JEN and JEN, it is observed that JNE has higher classification accuracy, specificity, and sensitivity for teaching quality evaluation of physical education courses in colleges and universities.


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## Body

## 1. Introduction

In recent years, due to the teaching reform in our country, a new teaching syllabus has been put forward for physical education in colleges and universities, and the content, objectives, and methods of teaching have also undergone new changes, which have gradually become the focus of current physical education workers, combined with the current physical education in colleges and universities in my country.The basic current situation of teaching, in order to further improve the teaching quality of teachers, gives full play to the subjective initiative of physical education teachers, establishes a systematic and scientific teaching quality management and evaluation system, and comprehensively improves the overall level of physical education in colleges and universities which is of great significance [1]. For a long time, the common evaluation method of physical education is to carry out comprehensive evaluation and statistical methods such as AHP after questionnaire survey [2–4]. It is easier to operate in the form of a questionnaire, but the design level of the questionnaire and the scope of the investigation have obvious effects on the results. The disadvantage of comprehensive evaluation analysis and AHP is that it is greatly influenced by the subjective emotion and experience of the analysts. Combined with the abovementioned various AHP evaluation and analysis results subject to subjective influences and other factors [5], combined with the current rapid development of big data and artificial intelligence, a more scientific, objective, and accurate method is adopted to evaluate the quality of physical education teaching, aiming at the joint neural network. The performance of the joint neural network (JEN) model is affected by its parameter selection. A teaching quality evaluation model for physical education in colleges and universities based on assignment method (AM) optimization is proposed.The rest of the study is organized as follows. Section2 is concerned about the multi-index evaluation system of physical education teaching quality. Section 3 focuses on the joint neural network, and Section 4 throws light on the evaluation model of college physical education teaching quality based on GoldSA-JEN. Section 5 is about experimental results and analysis. Similarly, Section 6 is the conclusion.


## 2. Multi-Index Evaluation System of Physical Education Teaching Quality

Establishing a scientific system of physical education quality evaluation indicators is the basic guarantee for objective evaluation of physical education quality. The common teaching quality evaluation system includes two categories: the comprehensive teaching content to construct the teaching index, and the comprehensive student’s academic achievement to construct the teaching evaluation index. The above two kinds of classic evaluation system indicators are based on the following basic principles: indicators should be feasible, representative, and independent. The main purpose of the principle of less should not be more is to make the selection of evaluation indicators reliable, operable, and scientific. Based on related literature [6–8], this study constructs a set of AHP-based multi-index evaluation system of college physical education teaching quality from four dimensions: teaching content, teaching method, teaching attitude, and teaching effect, as shown in Figure 1.Figure 1
Physical education quality evaluation index.


## 3. Joint Neural Network

JEN is generally composed of input layer, hidden layer, and output layer.The network structure is shown in Figure2.Figure 2
JEN network structure diagram.In Figure2, the input and output variables of JEN are X = (X1, X2, …, Xn) and Y = (Y1, Y2, …,Ym); JEN network is trained. The process can be described in detail as follows:(i)
Step 1: initialize the JEN network. The input variableX = (X1, X2, …, Xn) and output variable Y = (Y1, Y2, …,Ym) of JEN determine the number of input layer nodes, hidden node layers, and output node layers of JEN, respectively, use n, l, and m to represent, use Wij and Wjk to represent the weight between the input layer and the hidden layer and the weight between the hidden layer and the output layer, and initialize the weight value; at the same time, use a and b to represent the hidden layer.(ii)
Step 2: calculate the hidden layer output. Use formula (1) to obtain the output value H of the hidden layer:(1)Hj=f∑i=1nWijXi−aj,j=1,2,…,l,(iii)
Step 3: calculate the output layer output. Calculate the predicted valueO of JEN according to the output H of the hidden layer of JEN, the connection weight Wjk from the hidden layer to the neurons of the output layer, and the threshold b corresponding to the output layer:(2)OK=f∑j=1lHjWjk−bj,k=1,2,…,m.(iv)
Step 4: calculate the network errore:(3)e=Yk−Ok,k=1,2,…,m.(v)
Step 5: update the weights:(4)Wij=Wij+ηHj1−Hjxi∑k−1mWjkek,i=1,2,…,l,Wjk=Wjk+ηHjek,j=1,2,…,l;k=1,2,…,m.(vi)
Step 6: update the threshold:(5)aj=aj+ηHj1−Hj∑k−1mWjkek,j=1,2,…,l,bk=bk+ek,k=1,2,…,m.(vii)
Step 7: (algorithm termination condition): if the termination condition is met, the algorithm stops; otherwise, it returns to Step 2.


## 4. Evaluation Model of College Physical Education Teaching Quality Based on GoldSA-JEN

The overall process of the teaching quality evaluation model for physical education in colleges and universities based on the JEN model proposed in this study is shown in Figure3.Figure 3
Evaluation flowchart.First, the dataset obtained through the system for the evaluation of the teaching quality of physical education in colleges and universities is divided into training set and test set according to the ratio of 4 : 1. Then, for the training technical data, the weights and thresholds of the joint nerve optimized JEN model proposed in this study are used to establish a teaching quality evaluation model for physical education in colleges and universities based on the JEN model; finally, the optimal weights and thresholds obtained by joint nerve are optimized [9, 10]. Substitute into the JEN model for testing. The overall execution process is described in detail as follows:(i)
Step 1: read the quality evaluation data of physical education in colleges and universities, divide the dataset into training set and test set according to the ratio of 4 : 1, and then normalize the data:(6)xnew=La+x−xminxmax−xmin×Lb−La,where x represents the original data and xnew represents the normalized data; use xmin and xmax to refer to the minimum and maximum data in the original dataset. La represents the minimum value in the normalized dataset and Lb represents the maximum value of the normalized data, where La is set to -1 and Lb is set to 1.(ii)
Step 2: initialize the basic parameters of the joint nerve algorithm, the joint section ratio search initial valuesa and b, the maximum number of loop iterations Tmax, the population size N, and the search dimension D, determine the JEN network structure, and initialize the weights and thresholds.(iii)
Step 3: initialize the population individuals of the joint nerve algorithm according to formula (7) and use the JEN model to obtain weights and thresholds to mark the initial position of everyone in each population:(7)Vi=lbi+rand0,1×ubi−lbj,where ubi and llbi are the upper and lower search limits of the i individual, respectively, and vi is the initial value of the i individual.(iv)
Step 4: calculate the joint section coefficientsx1 and x2 according to formulas (8) and (9):(8)x1=a×1−τ+b×τ,(9)x2=a×τ+b×1−τ,where τ is the joint ratio coefficient and τ=5−1/2 in the text a=−π and b=π.(v)
Step 5: use formula (10) to calculate the best fitness value ACC of everyone in the population and keep the best individual Vbest:(10)maxACCC,g=∑k=1kacckK,s.tC∈Cmax,Cmin,g∈gmax,gmin,where ACC represents the average accuracy value obtained by K-fold cross-validation and acck is used to represent the accuracy value obtained after the k-fold calculation.(vi)
Step 6: update the individual position according to(11)Vit+1=Vtt×sinr1−r2×sinr1x1×Dtt−x2×Vtt,where Vit+1 and Vit are the t+1 and t iteration positions of the i individual, respectively, Dit is the optimal position for the t iteration of the i individual, and r1 and r2 are random numbers between [0, 2π] and [0, π].(vii)
Step 7: for the population individuals whose positions have been updated, calculate the fitness valueACCnew of the calculator and use this value to compare with the optimal fitness ACCbest of the previous generation; if there is a situation of ACCnew>ACCbest, the best fitness value needs to be updated to this iterates to get the latest fitness value; at the same time, record the latest position of the individual and, otherwise, continue to keep ACCbest without updating.(viii)
Step 8: determine whether the algorithm reaches the termination condition; if the current number of cyclest>Tmax, jump out of the operation and end and output the optimal position and the optimal fitness value, where the optimal position corresponds to the optimal weight and threshold of the JEN model. Otherwise, skip to Step 3 ∼ACCbestStep 7 for continuous optimization iterate.(xi)
Step 9: according to the output results of step 8, carry out the quality evaluation of physical education teaching in colleges and universities for the test dataset.


## 5. Experimental Results and Analysis

### 5.1. Data Sources and Evaluation Indicators

In order to verify the effect of JEN on physical education quality evaluation, the experimental environment uses Matlab2020(a) software, the memory corresponding to the PC is 32G, the main frequency selected by the central processor is 2.6 GHz, and the CPU uses Intel(R)Core (TM) i5-7200 4-core, operating system. Select Windows 10 Pro. The data come from the XX college teaching reform project “Research on College Physical Education Teaching Mode,” and a total of 3000 sets of data are collected. In order to evaluate theeACC classification accuracy rate, Specificity value and the accuracy evaluation indicators of algorithms such as Sensitivity are mentioned as follows:(12)ACC=TP+TNTP+FP+FN+TN,(13)Specificity=TNFP+TN,(14)Sensitivity=TPTP+FN,where TP and TN, respectively, represent the number of samples that are correctly classified and the number of samples that are wrongly classified into other levels in the teaching quality evaluation of physical education courses in colleges and universities. FP and FN, respectively, represent the number of samples and false positives that are misclassified at other levels in the teaching quality evaluation of physical education courses in colleges and universities. In order to verify the effect of JEN on the quality evaluation of physical education in colleges and universities and compare the recognition effects of JEN, PSO-JEN, and JEN, the parameters of the algorithm are firstly set, as shown in Table 1.Table 1
Parameter settings.
AlgorithmParameter settingsJoint nervePopulation sizeM = 10 and evolutionary generation T = 50PSOPopulation sizeM = 10, evolutionary generation T = 50, and learning factor c1 = c2 = 2BPNNThe number of neurons in the input layerN1 = 12, the number of neurons in the hidden layer N2 = 24, and the number of neurons in the output layer N3 = 1


### 5.2. Analysis of Results

The evaluation score data of the secondary indicators of physical education teaching quality evaluation of all grades in 15 colleges and universities are selected as the input matrix vector of JEN, and the physical education quality level (excellent, good, average, and poor) of colleges and universities is used as the output vector of JEN (physical education quality evaluation JEN model). Secondly, use joint nerve to optimize the weights and thresholds of the JEN model and to establish the JEN teaching quality evaluation model of physical education in colleges and universities.Figure4 is a comparison chart of the convergence speed of different algorithms, in which JEN has a faster convergence speed and starts to converge when the number of iterations is 5. The evaluation results of joint nerve-EN, JEN, and BPNN are shown in Table 2.Figure 4
Convergence speed comparison chart: (a) PSO-BPNN; (b) joint nerve-BPNN.
(a)(b)Table 2
Evaluation results.
MethodsACC (%)Specificity (%)Sensitivity (%)JEN94.2694.7295.32PSO-BPNN91.592.1593.10BPNN91.1191.5290.46It can be seen from Table2 that the classification accuracy ACC of JEN is 95.62%, which is better than 92.85% of PSO-JEN and 90.76% of JEN. Specificity of JEN was 95.38%, which was better than 93.10% of PSO-JEN and 92.33% of JEN. The sensitivity of JEN was 97.46%, which was better than 94.34% of PSO-JEN and 91.15% of JEN. The larger the three indicators of ACC, Specificity, and Sensitivity, the better the algorithm effect. In terms of classification accuracy, compared with PSO-JEN and JEN, it is improved by 2.77% and 4.86%, respectively. In terms of specificity, compared with PSO-JEN and JEN, the improvement was 2.28% and 3.05%, respectively. In terms of Sensitivity index, compared with PSO-JEN and JEN, it is increased by 3.12% and 6.31%, respectively. From the comparison results of the three evaluation indicators of ACC, Specificity, and Sensitivity, JEN has higher classification accuracy, specificity, and sensitivity in evaluating the quality of physical education in colleges and universities.


## 5.1. Data Sources and Evaluation Indicators

In order to verify the effect of JEN on physical education quality evaluation, the experimental environment uses Matlab2020(a) software, the memory corresponding to the PC is 32G, the main frequency selected by the central processor is 2.6 GHz, and the CPU uses Intel(R)Core (TM) i5-7200 4-core, operating system. Select Windows 10 Pro. The data come from the XX college teaching reform project “Research on College Physical Education Teaching Mode,” and a total of 3000 sets of data are collected. In order to evaluate theeACC classification accuracy rate, Specificity value and the accuracy evaluation indicators of algorithms such as Sensitivity are mentioned as follows:(12)ACC=TP+TNTP+FP+FN+TN,(13)Specificity=TNFP+TN,(14)Sensitivity=TPTP+FN,where TP and TN, respectively, represent the number of samples that are correctly classified and the number of samples that are wrongly classified into other levels in the teaching quality evaluation of physical education courses in colleges and universities. FP and FN, respectively, represent the number of samples and false positives that are misclassified at other levels in the teaching quality evaluation of physical education courses in colleges and universities. In order to verify the effect of JEN on the quality evaluation of physical education in colleges and universities and compare the recognition effects of JEN, PSO-JEN, and JEN, the parameters of the algorithm are firstly set, as shown in Table 1.Table 1
Parameter settings.
AlgorithmParameter settingsJoint nervePopulation sizeM = 10 and evolutionary generation T = 50PSOPopulation sizeM = 10, evolutionary generation T = 50, and learning factor c1 = c2 = 2BPNNThe number of neurons in the input layerN1 = 12, the number of neurons in the hidden layer N2 = 24, and the number of neurons in the output layer N3 = 1


## 5.2. Analysis of Results

The evaluation score data of the secondary indicators of physical education teaching quality evaluation of all grades in 15 colleges and universities are selected as the input matrix vector of JEN, and the physical education quality level (excellent, good, average, and poor) of colleges and universities is used as the output vector of JEN (physical education quality evaluation JEN model). Secondly, use joint nerve to optimize the weights and thresholds of the JEN model and to establish the JEN teaching quality evaluation model of physical education in colleges and universities.Figure4 is a comparison chart of the convergence speed of different algorithms, in which JEN has a faster convergence speed and starts to converge when the number of iterations is 5. The evaluation results of joint nerve-EN, JEN, and BPNN are shown in Table 2.Figure 4
Convergence speed comparison chart: (a) PSO-BPNN; (b) joint nerve-BPNN.
(a)(b)Table 2
Evaluation results.
MethodsACC (%)Specificity (%)Sensitivity (%)JEN94.2694.7295.32PSO-BPNN91.592.1593.10BPNN91.1191.5290.46It can be seen from Table2 that the classification accuracy ACC of JEN is 95.62%, which is better than 92.85% of PSO-JEN and 90.76% of JEN. Specificity of JEN was 95.38%, which was better than 93.10% of PSO-JEN and 92.33% of JEN. The sensitivity of JEN was 97.46%, which was better than 94.34% of PSO-JEN and 91.15% of JEN. The larger the three indicators of ACC, Specificity, and Sensitivity, the better the algorithm effect. In terms of classification accuracy, compared with PSO-JEN and JEN, it is improved by 2.77% and 4.86%, respectively. In terms of specificity, compared with PSO-JEN and JEN, the improvement was 2.28% and 3.05%, respectively. In terms of Sensitivity index, compared with PSO-JEN and JEN, it is increased by 3.12% and 6.31%, respectively. From the comparison results of the three evaluation indicators of ACC, Specificity, and Sensitivity, JEN has higher classification accuracy, specificity, and sensitivity in evaluating the quality of physical education in colleges and universities.


## 6. Conclusion

This study proposes a teaching quality evaluation model for physical education subjects in colleges and universities based on the combined neural network optimization BPNN model. Compared with the joint neural network, PSO-BPNN and BPNN, in the classification accuracy, specificity, and sensitivity of the three performance evaluation indicators, the joint neural network model has the best effect on the evaluation of the quality of college physical education, so it can be used for physical education courses in colleges and universities. Teaching quality evaluation provides new ideas and approaches. However, due to the variety of indicators that affect the quality of physical education in colleges and universities, this study only studies the impact of 4 first-level indicators and 12 second-level indicators on the quality evaluation of physical education in colleges and universities.


### 6.1. Future Work

More indicators will be studied in the future. The impact of quality evaluation to improve the accuracy and applicability of the model.


## 6.1. Future Work

More indicators will be studied in the future. The impact of quality evaluation to improve the accuracy and applicability of the model.



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