# Quality Evaluation for Physical Education Teaching in Colleges with Joint Neural Network

Authors: Yiliang Li
Journal: Security and Communication Networks
 (2022)

---

## Abstract

A new teaching curriculum for physical education in colleges has been proposed as part of the teaching reform. New developments in content, goals, and teaching methods have also occurred, and these have gradually become the focus of current physical education researchers. By developing a model for objectively, truly, comprehensively, and accurately evaluating college physical education teaching, it will be possible not only to improve the quality of physical education instruction and the effectiveness of faculty in achieving their goals but also to improve the quality of programs. This research work proposes a Joint Neural Network (JNN) composed of an Improved Support Vector Machine (ISVM) and improved Back Propagation (BP) network for evaluating physical education quality in colleges. When using a standard SVM to classify feature data, the parameters used have an impact on the SVM’s classification. This study introduces an improved SVM with hybrid optimization of PSO and GA algorithms, combining the properties of genetic algorithms and particle swarm optimization. As the existing BP network is trapped with a local optimum, this work proposes an optimized BP network algorithm using the bat algorithm. The improved SVM and improved BP are then combined to form a joint neural network for evaluating the quality of physical education in colleges. Comprehensive and systematic experiments validate the correctness and effectiveness of the algorithm proposed in this paper.


---

## Body

## 1. Introduction

Higher education has also entered a new age with the rapid expansion of society and the economy in the twenty-first century. Teaching resources have become increasingly competitive in the knowledge economy, while the scope of higher education has grown. Higher education has been subjected to greater scrutiny from all segments of society. There is a growing supply of top-level sports professionals, thanks to the progressive expansion of sports majors in colleges and universities in recent years. As a result, the disparity between the quality and quantity of physical education at colleges and universities has become increasingly apparent. Since the number of pupils has increased and educational resources have been constrained, the quality of education has deteriorated on a long-term basis. Students in colleges and universities are increasingly concerned about the lack of physical education teachers and facilities, as well as a lack of management in the physical education teaching field. In order to ensure the quality of physical education instruction, it is impossible to focus just on increasing the size and scope of the program. It has also been a challenge for colleges and universities to keep up with the school’s growth and increase in student numbers, which has resulted in a lack of investment in the quality of teaching. As a result, the growth of physical education in colleges and universities must focus on the development of the theoretical and practical methods of the physical education quality monitoring system [1–5].When it comes to curriculum reform and construction, improving education quality has always been a constant theme. College and university teaching quality monitoring and improvement is a necessary choice for higher education to be people-oriented and to develop extensively in harmony with other sectors, as well as sustainably in the long term. The key elements of college and university quality monitoring are the autonomy, quality, and limited resources available to each institution. For the long-term development of college teaching to be completed, it is essential that colleges and universities build and strengthen an internal quality monitoring system. This is in keeping with higher education policy. Physical education majors at general colleges and universities have become an important research issue on how to construct a teaching quality monitoring system that works in accordance with current college and university conditions, the educational law of progress, and the actual scenario. It is both important in theory and useful in practice [6–10]. A quality monitoring system for physical education in universities and colleges can also be referenced from this study [11–15].The original training and monitoring methods for physical education in colleges and universities have had a number of limitations. As a result, having a scientific method for assessing the quality of physical education instruction in colleges and universities is essential for the discipline’s long-term growth and development, as well as its ability to compete globally. As a result, this study draws on a variety of theoretical foundations to develop an evaluation system for the quality of physical education instruction that is scientific, standardized, reasonable, and effective. Monitoring college sports training units provides a theoretical and practical framework for reference. Efforts to ensure the long-term viability of physical education in colleges and universities are done by further investigating the management mode that is most suited for physical education in colleges and universities. Developing physical education standards in today’s colleges and universities, as well as the cultivation of outstanding sports talents who meet the needs of the time in order to promote the development of sports and speed up the development of a sports power, is of great theoretical significance and practical value.


## 2. Related Work

Xu et al. [16] constructed a relatively accurate index system, perfected the traditional AHP, and obtained the index weight. This overcomes the limitation that the weight of each indicator is too different, and it will not appear too small. And we obtain real data resources based on large samples to ensure the credibility of the data. Sun [17] used Bayesian classification to determine the evaluation system, explained the naive classifier, and listed classification examples. The data used in the experiment are the previously used empirical data, which shows that the classification performance is relatively good, the classification accuracy is relatively high, and the application of the Bayesian classification technology in teaching evaluation is feasible. The method adopted avoids the direct influence of human factors, and the technical reference provided for future teaching evaluation is reasonable. The weight determination method proposed by Hou [18] is AHP, which makes evaluation results more reasonable and scientific, and the practicality of the method is verified. Mathematical methods have very good properties, and proper selection in teaching evaluation can ensure the rationality of the results. It is very necessary to have a deep understanding for the specific situation for students in all aspects and to improve themselves in teaching. Ping [19] illustrated that the index system of college teacher evaluation is mainly carried out from four dimensions, and according to the research, a set of the measurement system with validity and reliability is obtained. Using this system, evaluation can obtain an index system with good index dimensions, it provides a practical and theoretical basis for the teaching evaluation system of colleges and universities.To avoid teaching evaluation work becoming only subjective and arbitrary, Chen [20] used the relevant evaluation methods in mathematics to calculate the weights scientifically and accurately, built a model reasonably, and developed a campus teaching evaluation system, through empirical analysis, to verify its reliability and to achieve the principle of design indicators. The constructed index system contains both objective and subjective factors. The two properties are skillfully combined to avoid the irrationality of the index system. Ge et al. [21] concluded that there are many influencing factors of teaching evaluation in colleges, including teachers, schools, and students. Therefore, the decision-making problem of teaching evaluation is obviously a complex nonlinear problem, and it is difficult to make a corresponding evaluation by establishing a traditional mathematical analytical formula. Because the advantage of the RReliefF algorithm is that it can solve nonlinear decision-making problems, and it is very effective to use it to simplify the evaluation system. Without the establishment for complex models, the established index system can be used as a reference for the scientific evaluation of teaching in colleges and universities. In order to solve some practical problems in the process of teaching evaluation in colleges and universities, Adriano et al. [22] determined the main factors that affect results for teaching evaluation. The index system to promote favorable development for teachers’ teaching quality is constructed, and the method of constructing the mathematical model is multifactor analysis. The index system obtained in this way is conducive to a more accurate evaluation of teaching.Li and She [23] achieved research results of teaching evaluation in various colleges, formulated comprehensive as well as simple evaluation principles, and established an index system with the teaching situation of colleges. This mainly included ten factors in four aspects. Appropriate evaluation methods are used for the calculation of weights; targeted empirical analysis is made on the selected samples; and finally the comprehensive evaluation results are obtained. Matt [24] showed that without giving teachers the opportunity to reflect and evaluate their teaching, it is difficult to develop their own competencies. This often leads to an over-reliance on the subjective judgment of the inspector observer. Zoe et al. [25] concluded that the evaluation of teaching in colleges is not objective, the difficulty coefficient of management evaluation is relatively large, and the scientific research achievements of teachers are difficult to quantify. In order to avoid these deficiencies, a practical indicator system has been developed. Reasonable evaluation methods were used to study the important coefficients of indicators, establish criteria, and build models. Through the research results, we can clarify our own position in teaching, find strengths and weaknesses, and then inspire each teacher to improve teaching in all aspects. Ranran [26] evaluated teaching by young teachers in colleges. Objective evaluation of teaching can be improved through the teaching quality of teachers in colleges. The index system is formulated, and two methods are used to construct a model for empirical analysis. Through the evaluation results, young teachers can not only understand their ranking in the evaluation but also see their shortcomings, summarize and think about them, formulate new strategies, and improve their teaching quality by implementing strategies.


## 3. Materials and Methods

### 3.1. Support Vector Machine (SVM)

Based on statistical learning theory and the notion of structural risk minimization, support vector machine (SVM) is a supervised machine learning method. The generalizability and several distinctive advantages of SVM in handling small samples and nonlinear classification problems are well documented.By finding a hyperplane, the primary idea of SVM is to distribute classification data evenly on both sides of it. The blank area on both sides of the hyperplane must be the greatest in order to have a good generalization ability to the problem to be solved. Figure1 is a schematic diagram of SVM binary classification. The black and red dots in the figure represent two different types of data, respectively, and H represents the optimal classification hyperplane solved by SVM. The lines H1 and H2 parallel to the optimal hyperplane H are the lines that pass through the two types of sample points and are closest to H, and the distance between H1 and H2 is called the classification interval.Figure 1
SVM classification.When dealing with inseparable problems, SVM transforms the original attributexi into attribute kxi through the kernel function and maps the points on low-dimensional space into high-dimensional space. Transform a nonlinear inseparable problem into a linearly separable problem and then use the linearly separable steps to solve the optimal hyperplane. The objective function of nonlinear SVM can be transformed into(1)minfω=0.5ω2+C∑i=1lσi,yiωkxi+b≥1.The optimization problem is transformed into a dual form by introducing the Lagrangian algorithm [27]:(2)minfω=0.5min∑i=1l∑j=1lyiyjaiajmxi,xj−∑i=1lai,∑i=1lyiai=0.The inner product of a high-dimensional space is a function of the amount of computation required to solve the optimization problem, as shown in the preceding derivation. It is believed that the kernel function, which accepts input from the low-dimensional space and calculates the value of the high-dimensional space, can be found in the original space in order to escape the curse of dimensionality. The number of dimensions in the feature space is unlimited because RBF has only a few parameters. Since it has such good properties for function fitting and nonlinear approximation compared to other kernel functions, many people have turned to using it.It can be seen from the above analysis that different penalty parametersC and kernel parameters k have different effects on the classification performance of SVM, and appropriate C and k will make SVM show the best classification effect. The research believes that too large C will lead to the occurrence of over-fitting, resulting in poor generalization ability of the model. If C is too small, the algorithm will not be fully trained on the inherent characteristics of the sample, resulting in the effects of underlearning and low accuracy.


### 3.2. Genetic Algorithms and Particle Swarm Optimization

Inspired by evolution theory, species selection theory, and population genetic theory, a genetic algorithm (GA) is an algorithm that finds the optimal solution in a process. In the genetic algorithm, the potential solutions to the analyzed problems all exist in the form of chromosomes. Fitness is an important function in a genetic algorithm, and its value is used to analyze the pros and cons of dyeing. During the optimization process of the entire genetic algorithm, operators such as selection, crossover, and mutation are used to gradually form a new generation for potential solutions, and each generation of individuals’ shows better and better adaptability to the living environment. GA has remarkable features such as global solution space search, parallelism, and extensive adaptability. The global solution space search avoids getting stuck in the local optimal solution. Taking the population as the unit, each individual in the independent variable is searched in parallel during the operation of the algorithm, so there is a higher search efficiency. GA can directly manipulate structure objects, so it has wider adaptability.The steps of the genetic algorithm are as follows:(1)
Encoding: it abstractly maps the solution space of the problem to the structure of the gene string and usually uses binary codes to encode the solution space into a finite-length string consisting of 0 and 1.(2)
Initialize the population and generateN individuals in the possible problem solution space to form the initialized population.(3)
Calculate fitness function: the fitness function value is an index for evaluating chromosomes, and each encoded gene string calculates fitness according to the fitness function.(4)
Selection: the selection operation is based on the fitness value of step (3), and a new population is formed through selecting excellent individuals.(5)
Crossover: randomly select the same position of two gene strings and form new individuals through crossover combination.(6)
Mutation: inverting a certain bit in a gene. New individuals created by mutation are added to the parent. Variation mimics the phenomenon of genetic mutation in nature.The PSO algorithm belongs to the random search algorithm. The algorithm is inspired by regularity for bird flock foraging behavior. The particle swarm optimization algorithm abstracts individual birds into particles without volume and mass, and each particle in the algorithm is a possible solution. Similar to other swarm intelligence search algorithms, the particle swarm optimization algorithm also achieves the optimal position of the problem through the mutual communication between different particles in the swarm.Inn-dimensional space, m particles form a population, xi represents the position, vi represents the velocity, pi represents optimal position, and pg represents the optimal position. The updated formulas are as follows:(3)vi+1=ωvi+α1β1p−xi+α2β2g−xi,xi+1=xi+vi+1.The pipeline of PSO is illustrated in Figure2.Figure 2
The pipeline of PSO.


### 3.3. ISVM with GA and PSO

The SVM penalty parameterC and kernel parameter k are mainly realized by the manual experience method and repeated test parameters in practical applications. This section uses the global search ability of GA and the fast convergence ability of PSO and uses the SVM physical education quality evaluation model optimized by GA and PSO hybrid optimization. The flowchart of ISVM is illustrated in Figure 3.Figure 3
The pipeline of ISVM.


### 3.4. Back Propagation (BP) Network

The BP network was developed on the basis of the single-layer network and is a neural network model trained according to the error back propagation algorithm. The typical model of the BP neural network is illustrated in Figure4 [28].Figure 4
The pipeline of the BP network.There are two stages in the training procedure in the BP neural network method. The first stage is the transmission of knowledge from one unit to the next. The weights and thresholds of each neuron are used to calculate the actual output of each neuron based on the input data. Error back propagation is the next step. After a predetermined number of repetitions, if the difference between the first stage’s output and the expected value is still too large, a backpropagation procedure employing the gradient descent method is invoked. When the output value is less than predicted, each connection weight is reduced in order to reduce the amount of mistakes. The mathematical expression is(4)δil=∂∂zil12y−hw,bx2=−yi−ailf′zil,∂E∂wijl=ailδil+1,∂E∂bil=δil+1,w′=w−α∂E∂w,b′=b−α∂E∂b.The algorithm steps of the BP neural network are as follows:(1)
Initialize the algorithm parameters, such as determining the number of input layer nodes, the number of hidden layer nodes, the number of output layer nodes, and other parameters.(2)
Calculation of output and calculation of external input and hidden layer weights and thresholds as hidden layer output. Calculate output according to hidden layer output and hidden layer and output layer weights and thresholds.(3)
Loss value calculation: the error of the model is calculated with the actual output and expected output of the network model.(4)
Update of weights and thresholds.(5)
If the requirements of the algorithm accuracy or the maximum number of iterations are not met, go back to step (2).The BP neural network has a strong nonlinear mapping ability. During the training process, the learning content is adaptively stored in the weights of the BP neural network, so it has a high self-learning ability. However, the BP algorithm has the defect of falling into local extreme values during training.


### 3.5. Bat Algorithm

BA is a novel swarm intelligence optimization algorithm inspired by the predation behavior of bats [29]. As a new research area in the realm of intelligent optimization algorithms, BA has emerged as an important new element in PSO, GA, and other algorithms. Optimization in engineering, multiobjective optimization, and pattern recognition are just a few of the areas where it has found widespread application.When bats begin to hunt for prey, pulse firing rate is low and loudness is greatest. In the process of flying to the prey, the loudness decay coefficient controls the loudness to decrease. Similar to the swarm intelligence algorithm, the bat algorithm also includes two processes of global search as well as local search. The algorithm updates the global search part in the optimization process by adjusting the pulse frequency and updating the local search part by the pulse firing rate and loudness.In the problem search space, each bat updates its position and velocity.(5)fi=fmin+fmax−fminα,(6)vit=vit−1+xit−1−x∗fi,(7)xit=xit−1+vit,where fi represents the pulse frequency, and fmin and fmax represent the maximum and minimum frequencies, respectively. α is a random number in [0, 1].The position update formulas for local search and global search are as follows:(8)xn=x∗+φAt¯,(9)xn=xit+φAt¯.The steps of the bat algorithm are as follows:(1)
Initialize the relevant parameters, for example, the number of algorithm iterations, the speed and position of individual bats, population size, pulse firing rate and pulse firing rate increase coefficient, pulse loudness, pulse loudness attenuation coefficient, and pulse frequency.(2)
Calculate the optimal position of the bat according to the fitness function and update the speed and position according to equations (5)–(7).(3)
Generate a random number on the interval [0, 1] if the bat’s pulse firing rate is greater than the random number. Then update the position of the bat according to formula (7). Otherwise, select the optimal bat individual and generate a local solution by equation (8) around this individual.(4)
Generate random numbers again on the interval [0, 1]. If the bat’s impulse loudness is greater than this random number and the fitness value is better than the optimal value, the bat updates the position. And increase the pulse emission rate according to the pulse emission rate increase coefficient and decrease the loudness according to the loudness attenuation coefficient.(5)
If the end condition is satisfied, output the result; otherwise, go to step (2).


### 3.6. IBP with BA Algorithm

The thresholds and weights of the traditional BP neural network algorithm are generally randomly generated, and it is often easy to fall into the local optimal solution. In order to solve this defect of traditional BP neural network algorithm, this paper proposes using BA to optimize the BP neural network model. The fitness function of BA is as follows:(10)fxi=∑m=1nQim−Tim2,where n is the number of training samples, Qim is the actual output of the BP neural network, and Tim is the expected output of the BP neural network.The flow chart of the BA optimizing BP neural network is shown in Figure5.Figure 5
IBP network algorithm flow chart.


### 3.7. Joint Neural Network

This work first improves the SVM algorithm and the BP network to construct ISVM and IBP network, respectively. After that, the two networks are combined to propose a joint neural network (JNN), as shown in Figure6.Figure 6
The pipeline of JNN.The characteristic dimension of the input data is 15, and the specific physical education evaluation indicators are illustrated in Table1. The output is the evaluation result of teaching quality, and this work is set to 10 different levels.Table 1
The evaluation indexes.
ItemIndexX1Course objectivesX2Course structureX3Teaching expensesX4Teaching facilitiesX5Course resourcesX6Human resourcesX7Community resourceX8Teaching methodX9Teaching contentX10Teaching processX11Second classroomX12Student physical healthX13Knowledge cognitionX14Technical skillsX15Emotional attitude


## 3.1. Support Vector Machine (SVM)

Based on statistical learning theory and the notion of structural risk minimization, support vector machine (SVM) is a supervised machine learning method. The generalizability and several distinctive advantages of SVM in handling small samples and nonlinear classification problems are well documented.By finding a hyperplane, the primary idea of SVM is to distribute classification data evenly on both sides of it. The blank area on both sides of the hyperplane must be the greatest in order to have a good generalization ability to the problem to be solved. Figure1 is a schematic diagram of SVM binary classification. The black and red dots in the figure represent two different types of data, respectively, and H represents the optimal classification hyperplane solved by SVM. The lines H1 and H2 parallel to the optimal hyperplane H are the lines that pass through the two types of sample points and are closest to H, and the distance between H1 and H2 is called the classification interval.Figure 1
SVM classification.When dealing with inseparable problems, SVM transforms the original attributexi into attribute kxi through the kernel function and maps the points on low-dimensional space into high-dimensional space. Transform a nonlinear inseparable problem into a linearly separable problem and then use the linearly separable steps to solve the optimal hyperplane. The objective function of nonlinear SVM can be transformed into(1)minfω=0.5ω2+C∑i=1lσi,yiωkxi+b≥1.The optimization problem is transformed into a dual form by introducing the Lagrangian algorithm [27]:(2)minfω=0.5min∑i=1l∑j=1lyiyjaiajmxi,xj−∑i=1lai,∑i=1lyiai=0.The inner product of a high-dimensional space is a function of the amount of computation required to solve the optimization problem, as shown in the preceding derivation. It is believed that the kernel function, which accepts input from the low-dimensional space and calculates the value of the high-dimensional space, can be found in the original space in order to escape the curse of dimensionality. The number of dimensions in the feature space is unlimited because RBF has only a few parameters. Since it has such good properties for function fitting and nonlinear approximation compared to other kernel functions, many people have turned to using it.It can be seen from the above analysis that different penalty parametersC and kernel parameters k have different effects on the classification performance of SVM, and appropriate C and k will make SVM show the best classification effect. The research believes that too large C will lead to the occurrence of over-fitting, resulting in poor generalization ability of the model. If C is too small, the algorithm will not be fully trained on the inherent characteristics of the sample, resulting in the effects of underlearning and low accuracy.


## 3.2. Genetic Algorithms and Particle Swarm Optimization

Inspired by evolution theory, species selection theory, and population genetic theory, a genetic algorithm (GA) is an algorithm that finds the optimal solution in a process. In the genetic algorithm, the potential solutions to the analyzed problems all exist in the form of chromosomes. Fitness is an important function in a genetic algorithm, and its value is used to analyze the pros and cons of dyeing. During the optimization process of the entire genetic algorithm, operators such as selection, crossover, and mutation are used to gradually form a new generation for potential solutions, and each generation of individuals’ shows better and better adaptability to the living environment. GA has remarkable features such as global solution space search, parallelism, and extensive adaptability. The global solution space search avoids getting stuck in the local optimal solution. Taking the population as the unit, each individual in the independent variable is searched in parallel during the operation of the algorithm, so there is a higher search efficiency. GA can directly manipulate structure objects, so it has wider adaptability.The steps of the genetic algorithm are as follows:(1)
Encoding: it abstractly maps the solution space of the problem to the structure of the gene string and usually uses binary codes to encode the solution space into a finite-length string consisting of 0 and 1.(2)
Initialize the population and generateN individuals in the possible problem solution space to form the initialized population.(3)
Calculate fitness function: the fitness function value is an index for evaluating chromosomes, and each encoded gene string calculates fitness according to the fitness function.(4)
Selection: the selection operation is based on the fitness value of step (3), and a new population is formed through selecting excellent individuals.(5)
Crossover: randomly select the same position of two gene strings and form new individuals through crossover combination.(6)
Mutation: inverting a certain bit in a gene. New individuals created by mutation are added to the parent. Variation mimics the phenomenon of genetic mutation in nature.The PSO algorithm belongs to the random search algorithm. The algorithm is inspired by regularity for bird flock foraging behavior. The particle swarm optimization algorithm abstracts individual birds into particles without volume and mass, and each particle in the algorithm is a possible solution. Similar to other swarm intelligence search algorithms, the particle swarm optimization algorithm also achieves the optimal position of the problem through the mutual communication between different particles in the swarm.Inn-dimensional space, m particles form a population, xi represents the position, vi represents the velocity, pi represents optimal position, and pg represents the optimal position. The updated formulas are as follows:(3)vi+1=ωvi+α1β1p−xi+α2β2g−xi,xi+1=xi+vi+1.The pipeline of PSO is illustrated in Figure2.Figure 2
The pipeline of PSO.


## 3.3. ISVM with GA and PSO

The SVM penalty parameterC and kernel parameter k are mainly realized by the manual experience method and repeated test parameters in practical applications. This section uses the global search ability of GA and the fast convergence ability of PSO and uses the SVM physical education quality evaluation model optimized by GA and PSO hybrid optimization. The flowchart of ISVM is illustrated in Figure 3.Figure 3
The pipeline of ISVM.


## 3.4. Back Propagation (BP) Network

The BP network was developed on the basis of the single-layer network and is a neural network model trained according to the error back propagation algorithm. The typical model of the BP neural network is illustrated in Figure4 [28].Figure 4
The pipeline of the BP network.There are two stages in the training procedure in the BP neural network method. The first stage is the transmission of knowledge from one unit to the next. The weights and thresholds of each neuron are used to calculate the actual output of each neuron based on the input data. Error back propagation is the next step. After a predetermined number of repetitions, if the difference between the first stage’s output and the expected value is still too large, a backpropagation procedure employing the gradient descent method is invoked. When the output value is less than predicted, each connection weight is reduced in order to reduce the amount of mistakes. The mathematical expression is(4)δil=∂∂zil12y−hw,bx2=−yi−ailf′zil,∂E∂wijl=ailδil+1,∂E∂bil=δil+1,w′=w−α∂E∂w,b′=b−α∂E∂b.The algorithm steps of the BP neural network are as follows:(1)
Initialize the algorithm parameters, such as determining the number of input layer nodes, the number of hidden layer nodes, the number of output layer nodes, and other parameters.(2)
Calculation of output and calculation of external input and hidden layer weights and thresholds as hidden layer output. Calculate output according to hidden layer output and hidden layer and output layer weights and thresholds.(3)
Loss value calculation: the error of the model is calculated with the actual output and expected output of the network model.(4)
Update of weights and thresholds.(5)
If the requirements of the algorithm accuracy or the maximum number of iterations are not met, go back to step (2).The BP neural network has a strong nonlinear mapping ability. During the training process, the learning content is adaptively stored in the weights of the BP neural network, so it has a high self-learning ability. However, the BP algorithm has the defect of falling into local extreme values during training.


## 3.5. Bat Algorithm

BA is a novel swarm intelligence optimization algorithm inspired by the predation behavior of bats [29]. As a new research area in the realm of intelligent optimization algorithms, BA has emerged as an important new element in PSO, GA, and other algorithms. Optimization in engineering, multiobjective optimization, and pattern recognition are just a few of the areas where it has found widespread application.When bats begin to hunt for prey, pulse firing rate is low and loudness is greatest. In the process of flying to the prey, the loudness decay coefficient controls the loudness to decrease. Similar to the swarm intelligence algorithm, the bat algorithm also includes two processes of global search as well as local search. The algorithm updates the global search part in the optimization process by adjusting the pulse frequency and updating the local search part by the pulse firing rate and loudness.In the problem search space, each bat updates its position and velocity.(5)fi=fmin+fmax−fminα,(6)vit=vit−1+xit−1−x∗fi,(7)xit=xit−1+vit,where fi represents the pulse frequency, and fmin and fmax represent the maximum and minimum frequencies, respectively. α is a random number in [0, 1].The position update formulas for local search and global search are as follows:(8)xn=x∗+φAt¯,(9)xn=xit+φAt¯.The steps of the bat algorithm are as follows:(1)
Initialize the relevant parameters, for example, the number of algorithm iterations, the speed and position of individual bats, population size, pulse firing rate and pulse firing rate increase coefficient, pulse loudness, pulse loudness attenuation coefficient, and pulse frequency.(2)
Calculate the optimal position of the bat according to the fitness function and update the speed and position according to equations (5)–(7).(3)
Generate a random number on the interval [0, 1] if the bat’s pulse firing rate is greater than the random number. Then update the position of the bat according to formula (7). Otherwise, select the optimal bat individual and generate a local solution by equation (8) around this individual.(4)
Generate random numbers again on the interval [0, 1]. If the bat’s impulse loudness is greater than this random number and the fitness value is better than the optimal value, the bat updates the position. And increase the pulse emission rate according to the pulse emission rate increase coefficient and decrease the loudness according to the loudness attenuation coefficient.(5)
If the end condition is satisfied, output the result; otherwise, go to step (2).


## 3.6. IBP with BA Algorithm

The thresholds and weights of the traditional BP neural network algorithm are generally randomly generated, and it is often easy to fall into the local optimal solution. In order to solve this defect of traditional BP neural network algorithm, this paper proposes using BA to optimize the BP neural network model. The fitness function of BA is as follows:(10)fxi=∑m=1nQim−Tim2,where n is the number of training samples, Qim is the actual output of the BP neural network, and Tim is the expected output of the BP neural network.The flow chart of the BA optimizing BP neural network is shown in Figure5.Figure 5
IBP network algorithm flow chart.


## 3.7. Joint Neural Network

This work first improves the SVM algorithm and the BP network to construct ISVM and IBP network, respectively. After that, the two networks are combined to propose a joint neural network (JNN), as shown in Figure6.Figure 6
The pipeline of JNN.The characteristic dimension of the input data is 15, and the specific physical education evaluation indicators are illustrated in Table1. The output is the evaluation result of teaching quality, and this work is set to 10 different levels.Table 1
The evaluation indexes.
ItemIndexX1Course objectivesX2Course structureX3Teaching expensesX4Teaching facilitiesX5Course resourcesX6Human resourcesX7Community resourceX8Teaching methodX9Teaching contentX10Teaching processX11Second classroomX12Student physical healthX13Knowledge cognitionX14Technical skillsX15Emotional attitude


## 4. Experiment and Discussion

### 4.1. Dataset and Evaluation Metrics

This work uses two self-made physical education quality assessment datasets, PEA and PEB, which were collected from different universities. The number of samples included in each dataset is different, and the specific sample distribution is shown in Table2. Precision and recall rates are utilized as the evaluation metrics in this work.Table 2
The detailed information of datasets.
DatasetTraining setTest setTotalPEA6891350210393PEB8732413312865


### 4.2. Comparison with Other Methods

To verify the effectiveness of the JNN method proposed in this work, it is compared with other neural network methods. The methods involved in the comparison include logistic regression (LR), SVM, and BP. The experimental results are shown in Table3.Table 3
Comparison with other methods.
MethodPEAPEBPrecisionRecallPrecisionRecallLR85.883.888.785.2SVM90.886.292.189.6BP92.789.994.191.2JNN94.591.496.393.6Obviously, compared with other methods, the JNN method proposed in this work can achieve the best performance. Compared with the best-performing BP network, JNN achieves 1.8% precision hints and 1.5% recall improvements on the PEA dataset, respectively. A 2.2% precision improvement and a 2.4% recall improvement were obtained on the PEB dataset, respectively. This verifies the validity and correctness of the JNN.


### 4.3. Evaluation on Training Loss

In neural networks, the loss value of the network is an important evaluation metric. It can judge whether a network has reached a convergence state, and network convergence is the basis for subsequent network testing. Therefore, this work analyzes the error during network training, and the experimental results are shown in Figure7.Figure 7
Evaluation on training loss.Obviously, as the number of iterations increases, the loss of the network gradually decreases. When the training epoch reaches 30, the loss value of the network remains basically the same. This shows that the network has reached a convergence state at this time, which also verifies the correctness of the JNN network.


### 4.4. Evaluation on Network Joint

As mentioned above, the JNN network designed in this work is obtained by combining two neural networks, ISVM and IBP. To verify the effectiveness of this joint neural network strategy, this work conducts comparative experiments to compare the performance of physical education evaluation when using a single ISVM and a single IBP network, respectively. The experimental results are shown in Figure8.Figure 8
Evaluation on network joint.Obviously, the evaluation performance corresponding to a single ISVM or a single IBP network is lower than that of the JNN network. The best performance improvement can only be obtained by combining the two neural networks, which further illustrates the correctness of the JNN network designed in this work. Combining the two networks can effectively complement information, thereby improving the evaluation accuracy.


### 4.5. Evaluation on Improvement Strategy

As mentioned earlier, this work improves the SVM algorithm and the BP algorithm. Specifically, genetic algorithm and particle swarm optimization are used to optimize SVM, and the bat algorithm is used to optimize BP. In order to verify the effectiveness and correctness of this improved strategy, this work conducts comparative experiments to compare the network performance without the optimization strategy. The experimental results are shown in Figure9.Figure 9
Evaluation of improvement strategy.Obviously, when SVM and BP are optimized separately, the performance of the network will be improved. However, a single optimization strategy cannot achieve the best performance, and the highest evaluation accuracy can be obtained only when SVM and BP are optimized at the same time. This further demonstrates the effectiveness and correctness of the optimization strategy proposed in this work.


## 4.1. Dataset and Evaluation Metrics

This work uses two self-made physical education quality assessment datasets, PEA and PEB, which were collected from different universities. The number of samples included in each dataset is different, and the specific sample distribution is shown in Table2. Precision and recall rates are utilized as the evaluation metrics in this work.Table 2
The detailed information of datasets.
DatasetTraining setTest setTotalPEA6891350210393PEB8732413312865


## 4.2. Comparison with Other Methods

To verify the effectiveness of the JNN method proposed in this work, it is compared with other neural network methods. The methods involved in the comparison include logistic regression (LR), SVM, and BP. The experimental results are shown in Table3.Table 3
Comparison with other methods.
MethodPEAPEBPrecisionRecallPrecisionRecallLR85.883.888.785.2SVM90.886.292.189.6BP92.789.994.191.2JNN94.591.496.393.6Obviously, compared with other methods, the JNN method proposed in this work can achieve the best performance. Compared with the best-performing BP network, JNN achieves 1.8% precision hints and 1.5% recall improvements on the PEA dataset, respectively. A 2.2% precision improvement and a 2.4% recall improvement were obtained on the PEB dataset, respectively. This verifies the validity and correctness of the JNN.


## 4.3. Evaluation on Training Loss

In neural networks, the loss value of the network is an important evaluation metric. It can judge whether a network has reached a convergence state, and network convergence is the basis for subsequent network testing. Therefore, this work analyzes the error during network training, and the experimental results are shown in Figure7.Figure 7
Evaluation on training loss.Obviously, as the number of iterations increases, the loss of the network gradually decreases. When the training epoch reaches 30, the loss value of the network remains basically the same. This shows that the network has reached a convergence state at this time, which also verifies the correctness of the JNN network.


## 4.4. Evaluation on Network Joint

As mentioned above, the JNN network designed in this work is obtained by combining two neural networks, ISVM and IBP. To verify the effectiveness of this joint neural network strategy, this work conducts comparative experiments to compare the performance of physical education evaluation when using a single ISVM and a single IBP network, respectively. The experimental results are shown in Figure8.Figure 8
Evaluation on network joint.Obviously, the evaluation performance corresponding to a single ISVM or a single IBP network is lower than that of the JNN network. The best performance improvement can only be obtained by combining the two neural networks, which further illustrates the correctness of the JNN network designed in this work. Combining the two networks can effectively complement information, thereby improving the evaluation accuracy.


## 4.5. Evaluation on Improvement Strategy

As mentioned earlier, this work improves the SVM algorithm and the BP algorithm. Specifically, genetic algorithm and particle swarm optimization are used to optimize SVM, and the bat algorithm is used to optimize BP. In order to verify the effectiveness and correctness of this improved strategy, this work conducts comparative experiments to compare the network performance without the optimization strategy. The experimental results are shown in Figure9.Figure 9
Evaluation of improvement strategy.Obviously, when SVM and BP are optimized separately, the performance of the network will be improved. However, a single optimization strategy cannot achieve the best performance, and the highest evaluation accuracy can be obtained only when SVM and BP are optimized at the same time. This further demonstrates the effectiveness and correctness of the optimization strategy proposed in this work.


## 5. Conclusion

The quality of the physical education curriculum is an important part of college and university instruction. This research suggested a Joint Neural Network (JNN) made up of improved SVM and an improved BP network to evaluate the quality of physical education in universities. Using traditional SVM classification of feature data, the classification is influenced by multiple parameter selections made during the classification process. The improved SVM uses hybrid optimization of particle swarm optimization algorithms and a genetic algorithm that integrates the characteristics of both algorithms. A set of detailed and systematic experiments validated the correctness and effectiveness of the proposed mechanism. Apart from JNN, SVM, and BP algorithms, state-of-the-art rule-based [29] and hybrid reasoning and recommendation algorithms [30] can also play an important role in the physical education of students. In the future extension of this research work, the same issue is planned to be tackled using rule-based and hybrid reasoning methodologies.



---

*Source: 2039146-2022-04-28.xml*