# Evaluation of College Basketball Teaching Based on Deep Learning

Authors: Yulan Fu
Journal: Mobile Information Systems
 (2022)

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## Abstract

Physical education at college is the final step of school physical education, and it is critical to a student's educational experience. There are numerous issues such as the integration of existing teaching resources, combining the characteristics of basketball teaching to improve teaching quality, and the use of basketball as a means of lifelong physical exercise faced by college basketball teachers. To address these challenges, this research attempts to integrate the backpropagation (BP) neural network technique with its enhanced deep learning algorithm by adjusting the learning rate, adding more momentum items, increasing the excitation function, and other methods. A teacher’s effectiveness as a teacher is examined using numerous variables utilizing the suggested BP neural network assessment model for measuring teaching quality. In addition, the specific implementation of the improved BP neural network algorithm includes the determination of the number of BP neural network layers, the input data preparation method, and the number of neurons per layer. Finally, the simulation experiment evaluates the teacher’s teaching quality using the upgraded BP neural network.


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## Body

## 1. Introduction

At present, the interest in sports has increased rapidly. This growing interest can be linked to technological breakthroughs made by teams and individuals over a few decades to obtain a competitive advantage. According to [1], statistics have always played a part in sport, but the application of predictive analytics has grown in recent years. Because it is difficult to get useful insights from raw data, the amount of data produced for each game creates a big data problem. In this regard, with the implementation of the national “Educational Action Plan for the 21st Century” along with the continuous development of CUBA to promote “basketball culture,” various media have vigorously reported on NBA, CBA, and other high-level basketball games at home and abroad. Basketball, which integrates the characteristics of competition, teamwork, gameplay, fun, and fitness, is very popular among college students. Due to these, more and more college students have a great interest in basketball and actively choose basketball courses. On the other hand, college basketball teaching continuously reforms, taps development potential, and improves teaching quality and effect of basketball teaching. Besides, it enables college students to learn knowledge and skills of basketball by enhancing their physical fitness and improving their health. They regard basketball as a way to stay physically fit for the rest of their lives, and they are devoted to it [2, 3]. Due to these factors, basketball has become the key research area for many scholars.In this connection, the authors of [4] pointed out in the “Government Work Report”: “Higher education should focus on improving quality, speed up education and teaching reform, relatively stabilize the scale of enrollment, strengthen the construction of high-level disciplines and universities, and innovate talent training models.” Improve the talent training program's framework and work to develop a significant number of top performers. As a result, regular colleges and universities provide excellent basketball optional courses to help college students improve their physical and mental health. They encourage the popularization and growth of basketball in colleges and universities, as well as the implementation of the “National Student Physical Health Standard” and the Sunshine Sports Projects [5]. Many issues, such as higher educational reforms, the existing state of college basketball elective courses, and future growth prospects, have been the focus of attention in the new condition of further extending physical education. Classroom teaching is the primary means by which colleges and universities attain their educational aims, and teachers are the primary source of classroom teaching. Teachers are evaluated mostly based on the quality of their classroom training. The quality of a teacher’s work has a direct impact on the development of a student's talents. School administrators can better understand teaching work and improve teaching quality by evaluating the quality of instructors’ lessons. As a result, assessing teaching quality is an essential part of educational administration [6, 7]. Therefore, the classroom teaching quality evaluation can determine if instructors’ instructional procedures are organized under educational regulations and concepts. Measuring the disparity between the true qualities of teacher teaching is an essential technique to gauge teacher teaching quality. This is the extent to which the goal must be met. If the gap is too large, effective judgments can be made to discover problems in the teaching process in time and sum up lessons and experiences.Because of the above, an effective method and necessary means of teaching quality management in colleges and universities, a comprehensive evaluation system is very important for the expansion of college teacher management theory [8]. The following functions of the improvement of the college classroom teaching evaluation system and the enhancement of college classroom teaching quality are extremely important: (1) The teaching process is a “process of preaching, teaching, and solving puzzles.” The evaluation of teaching quality can provide timely feedback on students’ problems in the learning process and help teachers find and solve problems in the teaching process in time. It allows teachers to enhance their teaching approaches and overall teaching quality. (2) Teaching quality evaluation’s objectivity and impartiality have a great persuading capacity. Teachers’ enthusiasm for teaching work may be generated by feedback on teaching material and horizontal comparisons between teachers, and they can continue to develop energy and drive in the competition. (3) Teaching quality evaluation results are conducive to teachers’ self-inspection and improvement of teaching methods, thereby guiding the scientific decision-making of teaching administrative leadership and improving the efficiency of running schools. (4) Teachers’ teaching work is primarily evaluated for quality through objective and fair comprehensive scoring and rating. The findings can accurately represent a teacher’s teaching quality and level, allowing the teaching management department to promote and evaluate teachers based on their performance. The continuous improvement of the teaching evaluation mechanism can promote the standardization, institutionalization, and scientific development of teaching work. (5) The objectivity and fairness of teaching quality evaluation can enable the teaching supervision department to supervise the normal progress of various teaching work and teaching quality. Teaching evaluation results give a solid foundation for analyzing the accuracy and reasonableness of teaching arrangements. In this paper, I proposed a new algorithm based on the BP neural network to overcome the shortcomings of the BP algorithm and improve the performance of the BP network. In addition, I have developed a model for evaluating teaching quality that is based on the upgraded BP neural network. The major contribution of this work is as follows:(1)
Firstly, this research work highlights issues such as the integration of existing teaching resources, combining the characteristics of basketball teaching to improve teaching quality, and the use of basketball as a means of lifelong physical exercise faced by college basketball teachers.(2)
Secondly, to address these challenges, this research attempts to integrate the backpropagation (BP) neural network technique with its enhanced deep learning algorithm by adjusting the learning rate, adding more momentum items, increasing the excitation function, and other methods.(3)
Finally, the properties of the teacher’s teaching quality evaluation technique based on the upgraded BP neural network are investigated through simulation experiments. The fast convergence and excellent learning ability of the enhanced BP method suggested in this research distinguish it from other similar algorithms.The rest of this paper is organized as follows: Section2 explores the work of numerous scholars who have studied basketball sports and its integration. Section 3 is based on the proposed methodology and improved BP neural network algorithm. Section 4 explains the experimental works for the evaluation of college basketball teaching based on deep learning and their results. Section 5 is the last concluding section of my paper.


## 2. Related Work

These days, the global development trend and reform features of education allow us to perceive lifelong education, education for all, education modernization, education democratization, and other educational ideas. These have become international educational concepts and trends that have positively and effectively affected and encouraged countries throughout the world [9, 10]. College physical education, as the highest level of school sports, has a natural link with the countries and regions’ political and economic development levels in the process of continual reform and development. It, like higher education, plays an essential part in the creation of overall national strength. University sports in various nations exhibit diverse features due to differences in national conditions and public opinion. In recent years, many scholars in China have conducted research and discussions on this new type of teaching evaluation for the optional courses of physical education in ordinary colleges and universities [11]. However, there are relatively few studies on the optional basketball courses. The research on the teaching of basketball optional courses is mainly focused on the new situation. This paper puts forward ideas and suggestions on the purpose, guiding ideology, teaching materials, and methods of basketball elective teaching, and the standardization and optimization of technical and tactical teaching.Basketball is a sport that college students love very much. It has the characteristics of a large number of students participating and a wide range of popularization. It plays an extremely important role in enhancing the physical fitness of college students [12, 13], forming sports habits, and successfully passing the test “National Student Physical Health Standard.” Basketball optional courses have a pivotal position in college physical education. The development of basketball and the continuous improvement of technology and tactics have made it a highly competitive sport, but its unique fitness and entertainment are even more popular among college students. Thus, it is critical to secure the teaching quality of college basketball classrooms, promote college basketball teaching level, and establish a good foundation for college students’ lifelong sports by developing a scientific and fair assessment method of college basketball teaching quality. At this stage, the process of the teaching quality evaluation system of each university is roughly divided into setting evaluation indicators, collecting evaluation data, analyzing and processing data, and finally drawing evaluation decision rules. Therefore, data collection and processing play an extremely important role, and there are also some problems: (1) Huge amount of data collection: in the classroom teaching evaluation process, to objectively, fairly, and fully grasp the students’ understanding of the teacher’s teaching process, participate in the evaluation. The number of students and the evaluation of content items are relatively large, and huge data information needs to be collected. (2) Data incompleteness and uncertainty: in the teaching quality evaluation, due to human factors, students will have uneven evaluation results during the evaluation process, and the evaluation data obtained in this way are incomplete and uncertain. (3) Students’ subjective reasons: students’ preference or prejudice toward teachers leads to more personal subjective factors in the evaluation. (4) Objective reasons in the process of system operation: there are errors in the process of system operation. For example, some students fill in part of the data and submit it, and some students make mistakes in filling in, resulting in data loss or data distortion.Currently, there are a variety of approaches used to assess the quality of instruction, including expert assessment, analytic hierarchy process (AHP), fuzzy comprehensive evaluation, SOLO classifications, gray correlation, neural network model evaluation, and others. These methodologies evaluate classroom teaching quality from several viewpoints and range, while also performing qualitative analysis of the evaluation results. However, the majority of these methodologies have failed to successfully evaluate college basketball teaching. Therefore, this paper attempts to integrate the backpropagation (BP) neural network technique with its enhanced deep learning algorithm by adjusting the learning rate, adding more momentum items, increasing the excitation function, and other methods. A teacher’s effectiveness as a teacher is examined using numerous variables utilizing the suggested BP neural network assessment model for measuring teaching quality. In addition, the specific implementation of the improved BP neural network algorithm includes the determination of the number of BP neural network layers, the input data preparation method, and the number of neurons per layer.


## 3. Methodology and Improved BP Neural Network Algorithm

### 3.1. BP Neural Network and Its Algorithm

Since Hebb's introduction of learning principles in the 1940s, several artificial neural network learning algorithms have been proposed. In the book “Parallel Distributed Processing,” published in 1986, a team of scientists led by McCelland and Rumelhart described the error backpropagation technique of multilayer perceptron’s with a nonlinear continuous transformation function. The multilayer network concept proposed by Minsky was researched and presented. BP networks are multilayer views that are taught utilizing error backpropagation methods.


#### 3.1.1. BP Neural Network Model

In the history of the development of artificial neural networks, the BP algorithm has played a significant role in the arrival of the second research climax of artificial neural networks. In a sense, the emergence of the BP algorithm has ended the history of multilayer networks without training algorithms and is considered a training method for multilevel network systems. Furthermore, it has a strong mathematics base; therefore, changing its connection rights is compelling. After being published in 1986, the BP algorithm quickly became the most extensively used multilevel network training method, and it played a significant role in the popularization and deployment of artificial neural networks. It is made up of two processes: one for forwarding information propagation and the other for error propagation, which is known as the BP neural network. Each neuron receives the external input information and transmits it to the next higher-layer neuron. It is the responsibility of the middle layer to perform internal information processing activities and transform information. Here, the hidden layer can be used from where the data are sent to the output layer at the end. Put an understanding into action by completing the forward propagation process. Whenever the actual output differs from what was predicted, the error is propagated backward. Error propagates from the output layer back to the hidden layer and then to the input layer. At this point, the output layer has been affected. The process of neural network learning involves the repetitive forward and reverse propagation of information and errors. It is time to stop learning when the specified number of learning sessions has elapsed or when the output error has been decreased to the specified levels.On other hand, the BP neural network is a multilayered feedforward neural network that enables the achievement using the error backpropagation technique. The 3-layer network structure built on the BP algorithm consists of neurons in the input, hidden, and output layers. Among these layers, the input layer is in charge of receiving and delivering external data, while the hidden layer is in charge of data processing and the output layer is responsible for the outcomes of information. During the processing of the neural network, the information is sent from the input layer to the output layer via the hidden layer. If the current output matches the predicted output, the training is complete. Otherwise, the error is returned to the network. Fresh weights and threshold values for each layer are obtained and conveyed to the input layer one after another computing the error signal of the outcome and expectation in reverse, as per the original connection path. Neural network memory training refers to the forwarding of information and the frequent change of error backpropagation. Convergence and the related stable weights are obtained when the network's global error is smaller than the given error value and learning ceases. Figure1 depicts the flowchart of the BP neural network algorithm [14, 15].Figure 1
Flowchart of the BP neural network algorithm.


#### 3.1.2. BP Neural Network Algorithm

One of the most extensively used neural network algorithms is the backpropagation (BP) neural network method, which is a multilayer feedforward network trained using the error backpropagation technique. BP neural network is a supervised network learning method, and its input is a sample calculated using(1)p1,t1,p2,t2,…,pq,tq,where pp<i<q is the input of the network and the corresponding target output is tip<i<q′.The output of the network is the output of the last layer of neurons, which can be obtained using(2)a=aM.Then, the actual output is compared with the target output. In order to obtain the mean square error, the function described in (3) should be used.(3)Fx=Ee2=Et−a2.In order to achieve the best performance of the BP network, the network parameters in the algorithm need to be adjusted to minimize the mean square error, so (3) is also called the performance index.Then, use the approximate steepest descent method to update the bias and weights:(4)Wmk+1=Wmk−asmam−1T,(5)bmk+1=bmk−asm.In equations (4) and (5), after the kth network training, the weight matrix of the mth layer is W, and b is the bias value of the mth layer. It needs to go through the kth network training and the m−1 layer. The output vector of is a, s is an index describing the output error of the m layer, that is, the sensitivity index of the mth layer.In order to enable the network to meet specific application requirements, the following steps are required: continuous input of sample vectors, according to the actual output and standard output errors, adjust the training weights through corresponding rules, and finally make the performance index meet the specified requirements. This process usually needs to be repeated.


### 3.2. Improvement of BP Neural Network Algorithm

#### 3.2.1. Limitations of BP Neural Network Algorithm

According to mathematical theory, the BP neural networks are well suited for handling problems involving intricate internal mechanisms and capable of implementing any complex nonlinear mapping. It is possible to automatically derive “fair” solution rules using the BP neural network. After learning a collection of examples and accurate replies, the BP neural network completes this process. It follows that the brain’s BP neural network is capable of generalization.Experiments have proven that this BP neural network method is particularly successful in dealing with a wide range of challenges. Since its introduction, the BP neural network has been widely employed. While the BP neural network has various advantages and prospective applications, it is not without limitations. The BP neural network has numerous significant problems, including a very slow training speed, difficulty with local minima on a high-dimensional surface, and an algorithm convergence issue. These final two faults in particular have the capacity to eliminate the network. A neural network’s learning process reduces the network’s overall error to a minimum. The BP network’s convergence mechanism has two flaws: slow convergence and the presence of “local minima.” This occurrence will occur during learning new material. The discrepancy between the actual result and the expected output persists even after numerous repetitions of learning. This slower rate of error reduction leads to even more static changes, with the network finally converging to a tiny local region. The phenomena are the outcomes of the minimum point. The sigmoid function is employed as an independent variable in this equation to induce nonlinearity. This results in a large number of local minimum points in the weight space formed by the sigmoid function, as well as many local minimum points.It is impossible to reach the global minimum when training with the gradient descent technique to apply the BP algorithm. The following issues are likely to arise:The BP algorithm has a slow convergence rate and a low learning efficiency. This might take several hours or more for more challenging problems that necessitate training the BP algorithm.When training the BP algorithm, it may fall into a small valley area due to the paraboloid’s uneven surface, and this small valley area generates an extremely low local value. Put a stop to any training that would try to stray from the local minimum.The number of features characterizing each input sample must be the same, and the newly added sample will alter the previously taught network.


#### 3.2.2. Improved BP Neural Network Algorithm Steps

The BP network and its many patterns are used in the majority of neural network models in the use of artificial neural networks. This is not to say that the BP network is flawless; there will always be flaws in its algorithm, for instance, falling into the least part of the local part during the training process, the convergence speed being rather slow, the network having more duplication and the samples newly added affecting the samples learned, and so on. To address these flaws, the researcher has proposed several better techniques [16]. Its enhanced approaches are broadly grouped into three categories: improving the speed of neural network training, improving training accuracy, and avoiding slipping into the lowest value of the local portion. The added momentum approach and the variable learning rate technique are two of the most common strategies [17]. Figure 2 explains my suggested framework for college basketball teaching deep learning using deep learning.Figure 2
Framework for college basketball teaching deep learning using deep learning.According to my proposed scheme, the basketball data are collected from the players and stored in a database. From here, the data are cleaned and noise and raw data are removed. Consequently, features are generated from these data with the help of feature generation techniques. When the features are generated successfully, the best features are then selected and assigned to deep learning, from where two possibilities are obtained either top game strategy prescription or predict the short output of the basketball.First, a three-layer neural network needs to be set up, for which the following describes the training process of the improved BP network:The number of units in the input layer =NThe number of units in the hidden layer =LThe number of units in the output layer =MThe input vectorw of the network is X the actual output vector of the network = YThe output vector of the intermediate layer =HThe target vector of the training group is represented by the output vectorDThe weight from the output layer uniti to the hidden layer unit p = VThe weight from the hidden layer unitj to the output layer unit K = Wo and j are the threshold values of the output unit and the hidden unit, respectivelyAccording to the previous description, the output of each unit in the middle layer is obtained by(6)hj=f∑i=0N−1Vijxi+ϕj.Then, the output of each unit of the output layer is calculated using(7)yk=f∑j=0L−1Wjkhj+θk.Here, fx using the sigmoid function is the activation function as given in(8)fx=11+e−x.From the above training process, I can conclude that determining the weight of the network is the main purpose of network training. When the network achieves the accuracy criteria of the total error function, the training process terminates; otherwise, it is continually updated according to the weights and thresholds, and ultimately a classifier is generated.


## 3.1. BP Neural Network and Its Algorithm

Since Hebb's introduction of learning principles in the 1940s, several artificial neural network learning algorithms have been proposed. In the book “Parallel Distributed Processing,” published in 1986, a team of scientists led by McCelland and Rumelhart described the error backpropagation technique of multilayer perceptron’s with a nonlinear continuous transformation function. The multilayer network concept proposed by Minsky was researched and presented. BP networks are multilayer views that are taught utilizing error backpropagation methods.


### 3.1.1. BP Neural Network Model

In the history of the development of artificial neural networks, the BP algorithm has played a significant role in the arrival of the second research climax of artificial neural networks. In a sense, the emergence of the BP algorithm has ended the history of multilayer networks without training algorithms and is considered a training method for multilevel network systems. Furthermore, it has a strong mathematics base; therefore, changing its connection rights is compelling. After being published in 1986, the BP algorithm quickly became the most extensively used multilevel network training method, and it played a significant role in the popularization and deployment of artificial neural networks. It is made up of two processes: one for forwarding information propagation and the other for error propagation, which is known as the BP neural network. Each neuron receives the external input information and transmits it to the next higher-layer neuron. It is the responsibility of the middle layer to perform internal information processing activities and transform information. Here, the hidden layer can be used from where the data are sent to the output layer at the end. Put an understanding into action by completing the forward propagation process. Whenever the actual output differs from what was predicted, the error is propagated backward. Error propagates from the output layer back to the hidden layer and then to the input layer. At this point, the output layer has been affected. The process of neural network learning involves the repetitive forward and reverse propagation of information and errors. It is time to stop learning when the specified number of learning sessions has elapsed or when the output error has been decreased to the specified levels.On other hand, the BP neural network is a multilayered feedforward neural network that enables the achievement using the error backpropagation technique. The 3-layer network structure built on the BP algorithm consists of neurons in the input, hidden, and output layers. Among these layers, the input layer is in charge of receiving and delivering external data, while the hidden layer is in charge of data processing and the output layer is responsible for the outcomes of information. During the processing of the neural network, the information is sent from the input layer to the output layer via the hidden layer. If the current output matches the predicted output, the training is complete. Otherwise, the error is returned to the network. Fresh weights and threshold values for each layer are obtained and conveyed to the input layer one after another computing the error signal of the outcome and expectation in reverse, as per the original connection path. Neural network memory training refers to the forwarding of information and the frequent change of error backpropagation. Convergence and the related stable weights are obtained when the network's global error is smaller than the given error value and learning ceases. Figure1 depicts the flowchart of the BP neural network algorithm [14, 15].Figure 1
Flowchart of the BP neural network algorithm.


### 3.1.2. BP Neural Network Algorithm

One of the most extensively used neural network algorithms is the backpropagation (BP) neural network method, which is a multilayer feedforward network trained using the error backpropagation technique. BP neural network is a supervised network learning method, and its input is a sample calculated using(1)p1,t1,p2,t2,…,pq,tq,where pp<i<q is the input of the network and the corresponding target output is tip<i<q′.The output of the network is the output of the last layer of neurons, which can be obtained using(2)a=aM.Then, the actual output is compared with the target output. In order to obtain the mean square error, the function described in (3) should be used.(3)Fx=Ee2=Et−a2.In order to achieve the best performance of the BP network, the network parameters in the algorithm need to be adjusted to minimize the mean square error, so (3) is also called the performance index.Then, use the approximate steepest descent method to update the bias and weights:(4)Wmk+1=Wmk−asmam−1T,(5)bmk+1=bmk−asm.In equations (4) and (5), after the kth network training, the weight matrix of the mth layer is W, and b is the bias value of the mth layer. It needs to go through the kth network training and the m−1 layer. The output vector of is a, s is an index describing the output error of the m layer, that is, the sensitivity index of the mth layer.In order to enable the network to meet specific application requirements, the following steps are required: continuous input of sample vectors, according to the actual output and standard output errors, adjust the training weights through corresponding rules, and finally make the performance index meet the specified requirements. This process usually needs to be repeated.


## 3.1.1. BP Neural Network Model

In the history of the development of artificial neural networks, the BP algorithm has played a significant role in the arrival of the second research climax of artificial neural networks. In a sense, the emergence of the BP algorithm has ended the history of multilayer networks without training algorithms and is considered a training method for multilevel network systems. Furthermore, it has a strong mathematics base; therefore, changing its connection rights is compelling. After being published in 1986, the BP algorithm quickly became the most extensively used multilevel network training method, and it played a significant role in the popularization and deployment of artificial neural networks. It is made up of two processes: one for forwarding information propagation and the other for error propagation, which is known as the BP neural network. Each neuron receives the external input information and transmits it to the next higher-layer neuron. It is the responsibility of the middle layer to perform internal information processing activities and transform information. Here, the hidden layer can be used from where the data are sent to the output layer at the end. Put an understanding into action by completing the forward propagation process. Whenever the actual output differs from what was predicted, the error is propagated backward. Error propagates from the output layer back to the hidden layer and then to the input layer. At this point, the output layer has been affected. The process of neural network learning involves the repetitive forward and reverse propagation of information and errors. It is time to stop learning when the specified number of learning sessions has elapsed or when the output error has been decreased to the specified levels.On other hand, the BP neural network is a multilayered feedforward neural network that enables the achievement using the error backpropagation technique. The 3-layer network structure built on the BP algorithm consists of neurons in the input, hidden, and output layers. Among these layers, the input layer is in charge of receiving and delivering external data, while the hidden layer is in charge of data processing and the output layer is responsible for the outcomes of information. During the processing of the neural network, the information is sent from the input layer to the output layer via the hidden layer. If the current output matches the predicted output, the training is complete. Otherwise, the error is returned to the network. Fresh weights and threshold values for each layer are obtained and conveyed to the input layer one after another computing the error signal of the outcome and expectation in reverse, as per the original connection path. Neural network memory training refers to the forwarding of information and the frequent change of error backpropagation. Convergence and the related stable weights are obtained when the network's global error is smaller than the given error value and learning ceases. Figure1 depicts the flowchart of the BP neural network algorithm [14, 15].Figure 1
Flowchart of the BP neural network algorithm.


## 3.1.2. BP Neural Network Algorithm

One of the most extensively used neural network algorithms is the backpropagation (BP) neural network method, which is a multilayer feedforward network trained using the error backpropagation technique. BP neural network is a supervised network learning method, and its input is a sample calculated using(1)p1,t1,p2,t2,…,pq,tq,where pp<i<q is the input of the network and the corresponding target output is tip<i<q′.The output of the network is the output of the last layer of neurons, which can be obtained using(2)a=aM.Then, the actual output is compared with the target output. In order to obtain the mean square error, the function described in (3) should be used.(3)Fx=Ee2=Et−a2.In order to achieve the best performance of the BP network, the network parameters in the algorithm need to be adjusted to minimize the mean square error, so (3) is also called the performance index.Then, use the approximate steepest descent method to update the bias and weights:(4)Wmk+1=Wmk−asmam−1T,(5)bmk+1=bmk−asm.In equations (4) and (5), after the kth network training, the weight matrix of the mth layer is W, and b is the bias value of the mth layer. It needs to go through the kth network training and the m−1 layer. The output vector of is a, s is an index describing the output error of the m layer, that is, the sensitivity index of the mth layer.In order to enable the network to meet specific application requirements, the following steps are required: continuous input of sample vectors, according to the actual output and standard output errors, adjust the training weights through corresponding rules, and finally make the performance index meet the specified requirements. This process usually needs to be repeated.


## 3.2. Improvement of BP Neural Network Algorithm

### 3.2.1. Limitations of BP Neural Network Algorithm

According to mathematical theory, the BP neural networks are well suited for handling problems involving intricate internal mechanisms and capable of implementing any complex nonlinear mapping. It is possible to automatically derive “fair” solution rules using the BP neural network. After learning a collection of examples and accurate replies, the BP neural network completes this process. It follows that the brain’s BP neural network is capable of generalization.Experiments have proven that this BP neural network method is particularly successful in dealing with a wide range of challenges. Since its introduction, the BP neural network has been widely employed. While the BP neural network has various advantages and prospective applications, it is not without limitations. The BP neural network has numerous significant problems, including a very slow training speed, difficulty with local minima on a high-dimensional surface, and an algorithm convergence issue. These final two faults in particular have the capacity to eliminate the network. A neural network’s learning process reduces the network’s overall error to a minimum. The BP network’s convergence mechanism has two flaws: slow convergence and the presence of “local minima.” This occurrence will occur during learning new material. The discrepancy between the actual result and the expected output persists even after numerous repetitions of learning. This slower rate of error reduction leads to even more static changes, with the network finally converging to a tiny local region. The phenomena are the outcomes of the minimum point. The sigmoid function is employed as an independent variable in this equation to induce nonlinearity. This results in a large number of local minimum points in the weight space formed by the sigmoid function, as well as many local minimum points.It is impossible to reach the global minimum when training with the gradient descent technique to apply the BP algorithm. The following issues are likely to arise:The BP algorithm has a slow convergence rate and a low learning efficiency. This might take several hours or more for more challenging problems that necessitate training the BP algorithm.When training the BP algorithm, it may fall into a small valley area due to the paraboloid’s uneven surface, and this small valley area generates an extremely low local value. Put a stop to any training that would try to stray from the local minimum.The number of features characterizing each input sample must be the same, and the newly added sample will alter the previously taught network.


### 3.2.2. Improved BP Neural Network Algorithm Steps

The BP network and its many patterns are used in the majority of neural network models in the use of artificial neural networks. This is not to say that the BP network is flawless; there will always be flaws in its algorithm, for instance, falling into the least part of the local part during the training process, the convergence speed being rather slow, the network having more duplication and the samples newly added affecting the samples learned, and so on. To address these flaws, the researcher has proposed several better techniques [16]. Its enhanced approaches are broadly grouped into three categories: improving the speed of neural network training, improving training accuracy, and avoiding slipping into the lowest value of the local portion. The added momentum approach and the variable learning rate technique are two of the most common strategies [17]. Figure 2 explains my suggested framework for college basketball teaching deep learning using deep learning.Figure 2
Framework for college basketball teaching deep learning using deep learning.According to my proposed scheme, the basketball data are collected from the players and stored in a database. From here, the data are cleaned and noise and raw data are removed. Consequently, features are generated from these data with the help of feature generation techniques. When the features are generated successfully, the best features are then selected and assigned to deep learning, from where two possibilities are obtained either top game strategy prescription or predict the short output of the basketball.First, a three-layer neural network needs to be set up, for which the following describes the training process of the improved BP network:The number of units in the input layer =NThe number of units in the hidden layer =LThe number of units in the output layer =MThe input vectorw of the network is X the actual output vector of the network = YThe output vector of the intermediate layer =HThe target vector of the training group is represented by the output vectorDThe weight from the output layer uniti to the hidden layer unit p = VThe weight from the hidden layer unitj to the output layer unit K = Wo and j are the threshold values of the output unit and the hidden unit, respectivelyAccording to the previous description, the output of each unit in the middle layer is obtained by(6)hj=f∑i=0N−1Vijxi+ϕj.Then, the output of each unit of the output layer is calculated using(7)yk=f∑j=0L−1Wjkhj+θk.Here, fx using the sigmoid function is the activation function as given in(8)fx=11+e−x.From the above training process, I can conclude that determining the weight of the network is the main purpose of network training. When the network achieves the accuracy criteria of the total error function, the training process terminates; otherwise, it is continually updated according to the weights and thresholds, and ultimately a classifier is generated.


## 3.2.1. Limitations of BP Neural Network Algorithm

According to mathematical theory, the BP neural networks are well suited for handling problems involving intricate internal mechanisms and capable of implementing any complex nonlinear mapping. It is possible to automatically derive “fair” solution rules using the BP neural network. After learning a collection of examples and accurate replies, the BP neural network completes this process. It follows that the brain’s BP neural network is capable of generalization.Experiments have proven that this BP neural network method is particularly successful in dealing with a wide range of challenges. Since its introduction, the BP neural network has been widely employed. While the BP neural network has various advantages and prospective applications, it is not without limitations. The BP neural network has numerous significant problems, including a very slow training speed, difficulty with local minima on a high-dimensional surface, and an algorithm convergence issue. These final two faults in particular have the capacity to eliminate the network. A neural network’s learning process reduces the network’s overall error to a minimum. The BP network’s convergence mechanism has two flaws: slow convergence and the presence of “local minima.” This occurrence will occur during learning new material. The discrepancy between the actual result and the expected output persists even after numerous repetitions of learning. This slower rate of error reduction leads to even more static changes, with the network finally converging to a tiny local region. The phenomena are the outcomes of the minimum point. The sigmoid function is employed as an independent variable in this equation to induce nonlinearity. This results in a large number of local minimum points in the weight space formed by the sigmoid function, as well as many local minimum points.It is impossible to reach the global minimum when training with the gradient descent technique to apply the BP algorithm. The following issues are likely to arise:The BP algorithm has a slow convergence rate and a low learning efficiency. This might take several hours or more for more challenging problems that necessitate training the BP algorithm.When training the BP algorithm, it may fall into a small valley area due to the paraboloid’s uneven surface, and this small valley area generates an extremely low local value. Put a stop to any training that would try to stray from the local minimum.The number of features characterizing each input sample must be the same, and the newly added sample will alter the previously taught network.


## 3.2.2. Improved BP Neural Network Algorithm Steps

The BP network and its many patterns are used in the majority of neural network models in the use of artificial neural networks. This is not to say that the BP network is flawless; there will always be flaws in its algorithm, for instance, falling into the least part of the local part during the training process, the convergence speed being rather slow, the network having more duplication and the samples newly added affecting the samples learned, and so on. To address these flaws, the researcher has proposed several better techniques [16]. Its enhanced approaches are broadly grouped into three categories: improving the speed of neural network training, improving training accuracy, and avoiding slipping into the lowest value of the local portion. The added momentum approach and the variable learning rate technique are two of the most common strategies [17]. Figure 2 explains my suggested framework for college basketball teaching deep learning using deep learning.Figure 2
Framework for college basketball teaching deep learning using deep learning.According to my proposed scheme, the basketball data are collected from the players and stored in a database. From here, the data are cleaned and noise and raw data are removed. Consequently, features are generated from these data with the help of feature generation techniques. When the features are generated successfully, the best features are then selected and assigned to deep learning, from where two possibilities are obtained either top game strategy prescription or predict the short output of the basketball.First, a three-layer neural network needs to be set up, for which the following describes the training process of the improved BP network:The number of units in the input layer =NThe number of units in the hidden layer =LThe number of units in the output layer =MThe input vectorw of the network is X the actual output vector of the network = YThe output vector of the intermediate layer =HThe target vector of the training group is represented by the output vectorDThe weight from the output layer uniti to the hidden layer unit p = VThe weight from the hidden layer unitj to the output layer unit K = Wo and j are the threshold values of the output unit and the hidden unit, respectivelyAccording to the previous description, the output of each unit in the middle layer is obtained by(6)hj=f∑i=0N−1Vijxi+ϕj.Then, the output of each unit of the output layer is calculated using(7)yk=f∑j=0L−1Wjkhj+θk.Here, fx using the sigmoid function is the activation function as given in(8)fx=11+e−x.From the above training process, I can conclude that determining the weight of the network is the main purpose of network training. When the network achieves the accuracy criteria of the total error function, the training process terminates; otherwise, it is continually updated according to the weights and thresholds, and ultimately a classifier is generated.


## 4. Experience and Results

In this part, the outcomes have been examined utilizing the suggested algorithm. For the assumed layout, here I have classified frequency for 5 levels such as categories: excellent, good, medium, pass, and failed.


### 4.1. Evaluation System Based on Improved BP Neural Network

Using a BP neural network to evaluate instructors’ classroom teaching quality is based on the idea that the BP neural network’s input vector is made up of several assessment indicators of classroom teaching quality. Furthermore, the evaluation value (the result of the expert’s evaluation) is included in the BP neural network’s output vector. A sensible design has been used to create the vector, network structure, and training samples. The training samples are then put into the network, and the results are derived from them. When the system malfunction satisfies the specified requirements, the network model generated serves as the needed model for assessing classroom teaching quality.The BP neural network is used to evaluate the teaching quality of teachers. The training sample is the sample group selected by the teacher evaluation. After training and learning, the assessment model becomes universal and may be used to assess teacher teaching quality in a variety of schools. The neural network is more objective and fair than other traditional teacher teaching quality evaluations since the needed parameters are achieved through training and learning. The artificial neural network’s input unit is made up of basic markers for evaluating instructor teaching quality. The output unit is made up of teacher teaching quality, which is then combined to build the matching neural network, which is known as the BP neural network’s teaching quality evaluation model.


### 4.2. Evaluation Index System Based on Improved BP Neural Network

The index system is critical in demonstrating the objectivity and impartiality of the process used to evaluate teaching quality. Different colleges and institutions utilize different assessment index techniques, as do the weights allocated to each indicator. As a consequence, each college and institution should develop its own scientific and fair assessment index method.Teachers’ teaching quality assessment indicators serve as a guide, determining what indicators are utilized for evaluation and which indicators are often regarded by teachers. Therefore, the establishment and selection of indicators should not only reflect the essence of teaching but also reflect typical and objective indicators. Otherwise, the arbitrary selection of indicators will render teacher assessment meaningless. As a consequence of the neural network model’s output, instructors’ teaching quality evaluation results are classified into five categories: excellent, good, intermediate, pass, and fail. Table1 shows the output value of the neural network corresponds to the equivalent standard.Table 1
The output value of the neural network corresponds to the equivalent standard.
LevelOutputExcellent>0.9Good0.81∼0.9Medium0.7∼0.8Pass0.6∼0.69Failed<0.6This research calculated the teacher's teaching quality assessment index system and built the evaluation index of the student evaluation neural network based on actual teaching activities at a certain university. Each first-level indication has three to five secondary indicators, for a total of 18 secondary indicators that accurately reflect instructors' instructional situations. This indicator system determines the network topology of the teaching quality evaluation model.


### 4.3. Evaluation Data Initialization Processing

This index uses the percentile system since the input is derived from kids’ grades, while the neural network uses the sigmoid function and the input child's range is [0,1]. This necessitates first performing a data normalization process on the raw data. Typical normalization functions include the exponential function and maximum and minimum methods. This paper normalizes using the maximum and minimum methods since they preserve the original meaning better and do not easily lead to information loss. The data processing is transformed in a linear fashion using this approach.


### 4.4. Evaluation Model Based on Improved BP Neural Network Algorithm

#### 4.4.1. Determination of the Number of BP Network Layers

The famous Kolmogorov theorem network asserts that every continuous function mapping relationship can be approximated using a backpropagation neural network with a hidden layer, and this is true. A BP neural network with an S-type hidden layer and a linear output layer may estimate a continuous rational function in the closed interval. The principle of designing a BP network, a three-layer BP network, can meet the requirements when solving problems. Although increasing the number of layers will further reduce mistakes and enhance accuracy, the gain is not worth it in most cases because improving accuracy would also complex the network, hence extending the network’s training time.As the error accuracy may be enhanced by utilizing a hidden layer, increasing the number of neurons in the hidden layer is also a possibility. It is considerably simpler in terms of structural realization than adding additional hidden layers, and its training impact is easier to monitor and change than increasing the number of layers. As a result, a three-layer BP network may complete the mapping from nondimensional to dimensional arbitrary space. Furthermore, increasing the number of hidden layer neurons can enhance network accuracy. This research will design the teacher’s teaching quality evaluation model using three levels of BP network structure.The model is made up of two layers: input and output, as well as an intermediate hidden layer. The weight often connects the input layer to the intermediate hidden layer and the intermediate hidden layer to the output layer in the network. (9) may be used to compute this.(9)zj=fx,wij,w˜ij,where x represents the input sample, its actual output at the jth node is represented by zj, and the function f is a complex nonlinear function that is compounded by the weights of each layer of the network and the node function. The BP neural network dynamically adjusts the weights by propagating the error backward while correcting the error. When the number of learning times is large enough, it can achieve using(10)limE=lim12∑p=1q∑j=1ndj−zj2=0.Here,q is the number of samples and E is the total error of q samples.


#### 4.4.2. Determination of the Number of Neurons in Each Layer

(1) Determine the Number of Neurons in the Input Layer. The input layer parameters for a determined task dictate how many neurons are in the input layer. The total number of evaluation indications is frequently counted. According to this idea, secondary indicators for teacher teaching quality evaluation are governed by the number of neurons in the input layer.(2) Determine the Number of Neurons in the Output Layer. Teaching quality evaluation of teachers is a process of “qualitative certain, quantity-qualitative.” This judgment is converted from qualitative to quantitative output via the BP network model. Following this translation, the desired teaching quality assessment results based on the evaluation level and output results are achieved. The amount of neurons in the output layer represents the teacher's teaching quality. As a result, the output layer has one neuron. I use the final grade of the teacher's teaching quality evaluation to achieve this purpose.(3) Number of Hidden Layer Neurons. Selecting the number of hidden layer neurons is a tough task because there is no theoretical basis for making this decision. It is usually figured out through trial and error and the designer’s knowledge.Because of this, the network cannot be trained or detect new samples if the hidden layer unit selection is too tiny. Increasing the number of hidden layer units can help improve the neural network's accuracy in matching the training data, but doing so will extend learning time, diminish neural network convergence speed, and result in errors that are below optimal. As a result, an ideal number of hidden layer neurons is required for a BP network.To strengthen the neural network’s adaptability, promote the neural network’s ability, and accelerate training, reducing the number of hidden layer nodes is recommended. As a result, the number of concealed units should be kept to a minimum to fulfill the learning accuracy. Using the “trial and error method” is an option. Stop training if the convergence criterion is not met or if the number of training times is too great.


#### 4.4.3. Determination of Training Function, Learning Raten (Step Size), Momentum Factor, and Transfer Function

(1) Determination of Training Function. The total technique being used in my training system to detect a specific input and convert it to output is referred to as the training function. Backpropagation and its numerous forms, as well as weight/bias training, are notable examples. Table 2 shows the comparison of training results of different training functions. According to this figure, this study chooses 4 algorithms such as gradient descent, gradient descent with adaptive learning rate, Levenberg–Marquardt, and gradient descent with adaptive learning rate and momentum factor, respectively.Table 2
Comparison of training results of different training functions.
AlgorithmFunctionTraining stepsAccuracyGradient descentTraingd180000.05Gradient descent with adaptive learning rateTraingdm160000.025Levenberg–MarquardtTrainlm50006.92eGradient descent with adaptive learning rate and momentum factorTraingdx6003.28eFigure3 explains the comparison among steps and accuracy of network training gradient descent algorithm when using the trained function. There are 18000 steps in the training process of this algorithm, and it gives an accuracy of 0.05.Figure 3
Network training graph when using the Traingd function.Similar to the above, Figure4 explains the comparison among steps and accuracy of network training gradient descent with adaptive learning rate when using the Traingdm function. There are 16000 steps in the training process of this algorithm, and it gives an accuracy of 0.025.Figure 4
Network training graph when using the Traingdm function.In addition to the above, Figure5 explains the comparison among steps and accuracy of network training Levenberg–Marquardt algorithm when using the Trainglm function. There are 5000 steps in the training process of this algorithm, and it gives an accuracy of 6.92 e.Figure 5
Network training graph when using the Trainglm function.Apart from the above, Figure6 shows the comparison among steps and accuracy of network training gradient descent with adaptive learning rate and momentum factor when using the Traingdx. There are 600 steps in the training process of this algorithm, and it gives an accuracy of 3.28 e.Figure 6
Network training graph when using the Traingdx function.I can see from Table2 and Figures 3–6 that the gradient descent technique with adaptive learning rate and momentum factor has the best training accuracy and the fastest training speed.(1) Determination of Learning Rate n (Step Size). In the gradient descent method, if the learning rate n is too small, the convergence speed will be very slow, resulting in a longer training time. If n is too large, the system may be unstable and may cause violent iterations. At the same time, the effective learning rate at the beginning of training may not be suitable for later training. In this case, generally, it can only be selected based on experience. The selection range of the learning rate in this model is between 0.005 and 0.9.According to the evaluation data of this paper, the test result shows a learning rate of 0.04 is more appropriate.(2) Determination of the Momentum Factor (a). An additional momentum component is used to keep the network from going to a local minimum, while it is being trained. The momentum factor is, on average, around 0.85 in my experience.(3) Determination of Transfer Function. Functions and sigmoid functions. It is often dictated by the connection between input and output, as well as the transfer function to be used. If the output result does not contain a negative number, logarithmic S can be utilized. If the output value is negative, a hyperbolic tangent can be employed.The tangent sigmoid function tansig is selected by the hidden layer transfer function because the integers in the sample set range from 0 to 100. All networks have a sigmoid output, and linear functions such as purelin can make the network's output take any value between 0 and 1. This paper's actual output lies in the range [0,1]; hence, the output layer transfer function uses the logarithmic sigmoid function logsig.


## 4.1. Evaluation System Based on Improved BP Neural Network

Using a BP neural network to evaluate instructors’ classroom teaching quality is based on the idea that the BP neural network’s input vector is made up of several assessment indicators of classroom teaching quality. Furthermore, the evaluation value (the result of the expert’s evaluation) is included in the BP neural network’s output vector. A sensible design has been used to create the vector, network structure, and training samples. The training samples are then put into the network, and the results are derived from them. When the system malfunction satisfies the specified requirements, the network model generated serves as the needed model for assessing classroom teaching quality.The BP neural network is used to evaluate the teaching quality of teachers. The training sample is the sample group selected by the teacher evaluation. After training and learning, the assessment model becomes universal and may be used to assess teacher teaching quality in a variety of schools. The neural network is more objective and fair than other traditional teacher teaching quality evaluations since the needed parameters are achieved through training and learning. The artificial neural network’s input unit is made up of basic markers for evaluating instructor teaching quality. The output unit is made up of teacher teaching quality, which is then combined to build the matching neural network, which is known as the BP neural network’s teaching quality evaluation model.


## 4.2. Evaluation Index System Based on Improved BP Neural Network

The index system is critical in demonstrating the objectivity and impartiality of the process used to evaluate teaching quality. Different colleges and institutions utilize different assessment index techniques, as do the weights allocated to each indicator. As a consequence, each college and institution should develop its own scientific and fair assessment index method.Teachers’ teaching quality assessment indicators serve as a guide, determining what indicators are utilized for evaluation and which indicators are often regarded by teachers. Therefore, the establishment and selection of indicators should not only reflect the essence of teaching but also reflect typical and objective indicators. Otherwise, the arbitrary selection of indicators will render teacher assessment meaningless. As a consequence of the neural network model’s output, instructors’ teaching quality evaluation results are classified into five categories: excellent, good, intermediate, pass, and fail. Table1 shows the output value of the neural network corresponds to the equivalent standard.Table 1
The output value of the neural network corresponds to the equivalent standard.
LevelOutputExcellent>0.9Good0.81∼0.9Medium0.7∼0.8Pass0.6∼0.69Failed<0.6This research calculated the teacher's teaching quality assessment index system and built the evaluation index of the student evaluation neural network based on actual teaching activities at a certain university. Each first-level indication has three to five secondary indicators, for a total of 18 secondary indicators that accurately reflect instructors' instructional situations. This indicator system determines the network topology of the teaching quality evaluation model.


## 4.3. Evaluation Data Initialization Processing

This index uses the percentile system since the input is derived from kids’ grades, while the neural network uses the sigmoid function and the input child's range is [0,1]. This necessitates first performing a data normalization process on the raw data. Typical normalization functions include the exponential function and maximum and minimum methods. This paper normalizes using the maximum and minimum methods since they preserve the original meaning better and do not easily lead to information loss. The data processing is transformed in a linear fashion using this approach.


## 4.4. Evaluation Model Based on Improved BP Neural Network Algorithm

### 4.4.1. Determination of the Number of BP Network Layers

The famous Kolmogorov theorem network asserts that every continuous function mapping relationship can be approximated using a backpropagation neural network with a hidden layer, and this is true. A BP neural network with an S-type hidden layer and a linear output layer may estimate a continuous rational function in the closed interval. The principle of designing a BP network, a three-layer BP network, can meet the requirements when solving problems. Although increasing the number of layers will further reduce mistakes and enhance accuracy, the gain is not worth it in most cases because improving accuracy would also complex the network, hence extending the network’s training time.As the error accuracy may be enhanced by utilizing a hidden layer, increasing the number of neurons in the hidden layer is also a possibility. It is considerably simpler in terms of structural realization than adding additional hidden layers, and its training impact is easier to monitor and change than increasing the number of layers. As a result, a three-layer BP network may complete the mapping from nondimensional to dimensional arbitrary space. Furthermore, increasing the number of hidden layer neurons can enhance network accuracy. This research will design the teacher’s teaching quality evaluation model using three levels of BP network structure.The model is made up of two layers: input and output, as well as an intermediate hidden layer. The weight often connects the input layer to the intermediate hidden layer and the intermediate hidden layer to the output layer in the network. (9) may be used to compute this.(9)zj=fx,wij,w˜ij,where x represents the input sample, its actual output at the jth node is represented by zj, and the function f is a complex nonlinear function that is compounded by the weights of each layer of the network and the node function. The BP neural network dynamically adjusts the weights by propagating the error backward while correcting the error. When the number of learning times is large enough, it can achieve using(10)limE=lim12∑p=1q∑j=1ndj−zj2=0.Here,q is the number of samples and E is the total error of q samples.


### 4.4.2. Determination of the Number of Neurons in Each Layer

(1) Determine the Number of Neurons in the Input Layer. The input layer parameters for a determined task dictate how many neurons are in the input layer. The total number of evaluation indications is frequently counted. According to this idea, secondary indicators for teacher teaching quality evaluation are governed by the number of neurons in the input layer.(2) Determine the Number of Neurons in the Output Layer. Teaching quality evaluation of teachers is a process of “qualitative certain, quantity-qualitative.” This judgment is converted from qualitative to quantitative output via the BP network model. Following this translation, the desired teaching quality assessment results based on the evaluation level and output results are achieved. The amount of neurons in the output layer represents the teacher's teaching quality. As a result, the output layer has one neuron. I use the final grade of the teacher's teaching quality evaluation to achieve this purpose.(3) Number of Hidden Layer Neurons. Selecting the number of hidden layer neurons is a tough task because there is no theoretical basis for making this decision. It is usually figured out through trial and error and the designer’s knowledge.Because of this, the network cannot be trained or detect new samples if the hidden layer unit selection is too tiny. Increasing the number of hidden layer units can help improve the neural network's accuracy in matching the training data, but doing so will extend learning time, diminish neural network convergence speed, and result in errors that are below optimal. As a result, an ideal number of hidden layer neurons is required for a BP network.To strengthen the neural network’s adaptability, promote the neural network’s ability, and accelerate training, reducing the number of hidden layer nodes is recommended. As a result, the number of concealed units should be kept to a minimum to fulfill the learning accuracy. Using the “trial and error method” is an option. Stop training if the convergence criterion is not met or if the number of training times is too great.


### 4.4.3. Determination of Training Function, Learning Raten (Step Size), Momentum Factor, and Transfer Function

(1) Determination of Training Function. The total technique being used in my training system to detect a specific input and convert it to output is referred to as the training function. Backpropagation and its numerous forms, as well as weight/bias training, are notable examples. Table 2 shows the comparison of training results of different training functions. According to this figure, this study chooses 4 algorithms such as gradient descent, gradient descent with adaptive learning rate, Levenberg–Marquardt, and gradient descent with adaptive learning rate and momentum factor, respectively.Table 2
Comparison of training results of different training functions.
AlgorithmFunctionTraining stepsAccuracyGradient descentTraingd180000.05Gradient descent with adaptive learning rateTraingdm160000.025Levenberg–MarquardtTrainlm50006.92eGradient descent with adaptive learning rate and momentum factorTraingdx6003.28eFigure3 explains the comparison among steps and accuracy of network training gradient descent algorithm when using the trained function. There are 18000 steps in the training process of this algorithm, and it gives an accuracy of 0.05.Figure 3
Network training graph when using the Traingd function.Similar to the above, Figure4 explains the comparison among steps and accuracy of network training gradient descent with adaptive learning rate when using the Traingdm function. There are 16000 steps in the training process of this algorithm, and it gives an accuracy of 0.025.Figure 4
Network training graph when using the Traingdm function.In addition to the above, Figure5 explains the comparison among steps and accuracy of network training Levenberg–Marquardt algorithm when using the Trainglm function. There are 5000 steps in the training process of this algorithm, and it gives an accuracy of 6.92 e.Figure 5
Network training graph when using the Trainglm function.Apart from the above, Figure6 shows the comparison among steps and accuracy of network training gradient descent with adaptive learning rate and momentum factor when using the Traingdx. There are 600 steps in the training process of this algorithm, and it gives an accuracy of 3.28 e.Figure 6
Network training graph when using the Traingdx function.I can see from Table2 and Figures 3–6 that the gradient descent technique with adaptive learning rate and momentum factor has the best training accuracy and the fastest training speed.(1) Determination of Learning Rate n (Step Size). In the gradient descent method, if the learning rate n is too small, the convergence speed will be very slow, resulting in a longer training time. If n is too large, the system may be unstable and may cause violent iterations. At the same time, the effective learning rate at the beginning of training may not be suitable for later training. In this case, generally, it can only be selected based on experience. The selection range of the learning rate in this model is between 0.005 and 0.9.According to the evaluation data of this paper, the test result shows a learning rate of 0.04 is more appropriate.(2) Determination of the Momentum Factor (a). An additional momentum component is used to keep the network from going to a local minimum, while it is being trained. The momentum factor is, on average, around 0.85 in my experience.(3) Determination of Transfer Function. Functions and sigmoid functions. It is often dictated by the connection between input and output, as well as the transfer function to be used. If the output result does not contain a negative number, logarithmic S can be utilized. If the output value is negative, a hyperbolic tangent can be employed.The tangent sigmoid function tansig is selected by the hidden layer transfer function because the integers in the sample set range from 0 to 100. All networks have a sigmoid output, and linear functions such as purelin can make the network's output take any value between 0 and 1. This paper's actual output lies in the range [0,1]; hence, the output layer transfer function uses the logarithmic sigmoid function logsig.


## 4.4.1. Determination of the Number of BP Network Layers

The famous Kolmogorov theorem network asserts that every continuous function mapping relationship can be approximated using a backpropagation neural network with a hidden layer, and this is true. A BP neural network with an S-type hidden layer and a linear output layer may estimate a continuous rational function in the closed interval. The principle of designing a BP network, a three-layer BP network, can meet the requirements when solving problems. Although increasing the number of layers will further reduce mistakes and enhance accuracy, the gain is not worth it in most cases because improving accuracy would also complex the network, hence extending the network’s training time.As the error accuracy may be enhanced by utilizing a hidden layer, increasing the number of neurons in the hidden layer is also a possibility. It is considerably simpler in terms of structural realization than adding additional hidden layers, and its training impact is easier to monitor and change than increasing the number of layers. As a result, a three-layer BP network may complete the mapping from nondimensional to dimensional arbitrary space. Furthermore, increasing the number of hidden layer neurons can enhance network accuracy. This research will design the teacher’s teaching quality evaluation model using three levels of BP network structure.The model is made up of two layers: input and output, as well as an intermediate hidden layer. The weight often connects the input layer to the intermediate hidden layer and the intermediate hidden layer to the output layer in the network. (9) may be used to compute this.(9)zj=fx,wij,w˜ij,where x represents the input sample, its actual output at the jth node is represented by zj, and the function f is a complex nonlinear function that is compounded by the weights of each layer of the network and the node function. The BP neural network dynamically adjusts the weights by propagating the error backward while correcting the error. When the number of learning times is large enough, it can achieve using(10)limE=lim12∑p=1q∑j=1ndj−zj2=0.Here,q is the number of samples and E is the total error of q samples.


## 4.4.2. Determination of the Number of Neurons in Each Layer

(1) Determine the Number of Neurons in the Input Layer. The input layer parameters for a determined task dictate how many neurons are in the input layer. The total number of evaluation indications is frequently counted. According to this idea, secondary indicators for teacher teaching quality evaluation are governed by the number of neurons in the input layer.(2) Determine the Number of Neurons in the Output Layer. Teaching quality evaluation of teachers is a process of “qualitative certain, quantity-qualitative.” This judgment is converted from qualitative to quantitative output via the BP network model. Following this translation, the desired teaching quality assessment results based on the evaluation level and output results are achieved. The amount of neurons in the output layer represents the teacher's teaching quality. As a result, the output layer has one neuron. I use the final grade of the teacher's teaching quality evaluation to achieve this purpose.(3) Number of Hidden Layer Neurons. Selecting the number of hidden layer neurons is a tough task because there is no theoretical basis for making this decision. It is usually figured out through trial and error and the designer’s knowledge.Because of this, the network cannot be trained or detect new samples if the hidden layer unit selection is too tiny. Increasing the number of hidden layer units can help improve the neural network's accuracy in matching the training data, but doing so will extend learning time, diminish neural network convergence speed, and result in errors that are below optimal. As a result, an ideal number of hidden layer neurons is required for a BP network.To strengthen the neural network’s adaptability, promote the neural network’s ability, and accelerate training, reducing the number of hidden layer nodes is recommended. As a result, the number of concealed units should be kept to a minimum to fulfill the learning accuracy. Using the “trial and error method” is an option. Stop training if the convergence criterion is not met or if the number of training times is too great.


## 4.4.3. Determination of Training Function, Learning Raten (Step Size), Momentum Factor, and Transfer Function

(1) Determination of Training Function. The total technique being used in my training system to detect a specific input and convert it to output is referred to as the training function. Backpropagation and its numerous forms, as well as weight/bias training, are notable examples. Table 2 shows the comparison of training results of different training functions. According to this figure, this study chooses 4 algorithms such as gradient descent, gradient descent with adaptive learning rate, Levenberg–Marquardt, and gradient descent with adaptive learning rate and momentum factor, respectively.Table 2
Comparison of training results of different training functions.
AlgorithmFunctionTraining stepsAccuracyGradient descentTraingd180000.05Gradient descent with adaptive learning rateTraingdm160000.025Levenberg–MarquardtTrainlm50006.92eGradient descent with adaptive learning rate and momentum factorTraingdx6003.28eFigure3 explains the comparison among steps and accuracy of network training gradient descent algorithm when using the trained function. There are 18000 steps in the training process of this algorithm, and it gives an accuracy of 0.05.Figure 3
Network training graph when using the Traingd function.Similar to the above, Figure4 explains the comparison among steps and accuracy of network training gradient descent with adaptive learning rate when using the Traingdm function. There are 16000 steps in the training process of this algorithm, and it gives an accuracy of 0.025.Figure 4
Network training graph when using the Traingdm function.In addition to the above, Figure5 explains the comparison among steps and accuracy of network training Levenberg–Marquardt algorithm when using the Trainglm function. There are 5000 steps in the training process of this algorithm, and it gives an accuracy of 6.92 e.Figure 5
Network training graph when using the Trainglm function.Apart from the above, Figure6 shows the comparison among steps and accuracy of network training gradient descent with adaptive learning rate and momentum factor when using the Traingdx. There are 600 steps in the training process of this algorithm, and it gives an accuracy of 3.28 e.Figure 6
Network training graph when using the Traingdx function.I can see from Table2 and Figures 3–6 that the gradient descent technique with adaptive learning rate and momentum factor has the best training accuracy and the fastest training speed.(1) Determination of Learning Rate n (Step Size). In the gradient descent method, if the learning rate n is too small, the convergence speed will be very slow, resulting in a longer training time. If n is too large, the system may be unstable and may cause violent iterations. At the same time, the effective learning rate at the beginning of training may not be suitable for later training. In this case, generally, it can only be selected based on experience. The selection range of the learning rate in this model is between 0.005 and 0.9.According to the evaluation data of this paper, the test result shows a learning rate of 0.04 is more appropriate.(2) Determination of the Momentum Factor (a). An additional momentum component is used to keep the network from going to a local minimum, while it is being trained. The momentum factor is, on average, around 0.85 in my experience.(3) Determination of Transfer Function. Functions and sigmoid functions. It is often dictated by the connection between input and output, as well as the transfer function to be used. If the output result does not contain a negative number, logarithmic S can be utilized. If the output value is negative, a hyperbolic tangent can be employed.The tangent sigmoid function tansig is selected by the hidden layer transfer function because the integers in the sample set range from 0 to 100. All networks have a sigmoid output, and linear functions such as purelin can make the network's output take any value between 0 and 1. This paper's actual output lies in the range [0,1]; hence, the output layer transfer function uses the logarithmic sigmoid function logsig.


## 5. Conclusions

The Chinese sports business is now flourishing, but the effectiveness of sports training needs further improvements. The goal of this research was to overcome the problem of high-efficiency data utilization and intelligent collection in sports training systems by focusing on the basketball development system. This paper begins by providing an overview of the topic selection process, the significance of the topic in terms of research, and the present research status both domestically and internationally. It provides various typical ways of evaluating teacher teaching quality, examines and contrasts the methods of evaluating teacher teaching quality, and makes recommendations for their application. An artificial neural network built by BP is used to assess the quality of college basketball training. Besides, a model for measuring teaching quality has also been created, which is based on the updated BP neural network. Finally, simulation experiments are used to explore the features of the teacher’s teaching quality evaluation approach based on the updated BP neural network. The improved BP approach proposed in this study is distinguished from other comparable algorithms by its rapid convergence and high learning capabilities.



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*Source: 4829191-2022-07-19.xml*