# Design of the Online Learning System for College Physical Education Teaching Based on the BP Neural Network

Authors: Bin Li; Shaofen Wang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The popularization of the campus network provides the possibility for the realization of online sports teaching. Based on the current situation of college physical education, the existing electronic textbooks, individual technical education software, educational videos, and other resources are neatly integrated. It builds a sports network education platform that is helpful for students to learn and realizes open interactive education. Nowadays, with the development of the Internet, artificial intelligence technology has solved many problems for people. The learning model combining artificial intelligence and online education has also become a new trend of educational informatization. This paper is based on the BP neural network in artificial intelligence to explore the design of the college sports teaching system in school. The purpose of this paper is at developing an online learning system suitable for college sports, so as at improving the learning efficiency and learning experience of college sports students. This paper firstly introduces the background and research significance of online learning, the BP neural network. Then, it introduces the design of the BP neural network and online system; in this paper, a series of introductions are given to its structure, function, and recommendation algorithm. Afterwards, this paper tests the performance of the designed system and finds that the average response time of the system is 0.978 s. Finally, this paper does a questionnaire survey to the users of the learning system. It is found that both in the recognition and in the liking sections, the satisfaction of students and teachers is relatively high.


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## Body

## 1. Introduction

In recent years, with the rapid development of the information technology industry, the rapid popularization of home broadband networks and personal high-speed mobile networks, and the popularization of the concept of “lifelong learning,” online education has emerged and continued to develop. In response to the outbreak of the new crown epidemic in 2020 and in response to the call of “suspending classes and not learning,” online education immediately became the only choice for universities, primary and secondary schools, and educational institutions. So far, online education has continued to develop and has become a new model of education in the Internet era. The development of online education in higher education is also evident. The Internet is a perfect combination of multimedia technology and modern educational technology. Computers, mobile phones, e-readers, and the Internet are highly interactive, expressive, straightforward, and comprehensive. It can make up for the deficiencies of traditional education methods, and it has no time and space constraints. It can make college physical education break through restrictions and allow students to learn knowledge anytime, anywhere.Technologies such as big data and artificial intelligence have become important driving forces leading technological innovation. The deep integration of the BP neural network in artificial intelligence and education promotes the development of online education into a new teaching mode. To this end, an online sports teaching system is established, so that students and teachers can achieve teaching interaction across time and space through the sports online teaching platform. The online learning platform uses the Internet to realize the integration of science and technology and education and teaching. It breaks the limitation of the teaching location and the unbalanced educational resources existing in traditional education. It realizes the integration and sharing of educational resources. And with the exponential growth of network resources, the resources that people can obtain are gradually diversified. This brings a better learning experience to users.The innovation of this article is as follows: (1) based on the BP neural network, the online learning system of college sports is designed. It designs the BP network algorithm, recommendation algorithm, system function, and system framework of the system in detail and (2) the system performance test is done to verify the stability and efficiency of the system performance in this paper, and the satisfaction of teachers and students to the system is investigated through questionnaires.


## 2. Related Work

Since the popularization of online education, the online learning system has been inseparable from it. The online learning system is the medium of online education, so many scholars have studied it. Bodyanskiy et al. studied an evolutionary cascaded neurofuzzy system and its online learning process. The system is based on traditional Kohonen neurons. And it solves the clustering problem of nonstationary data flow under uncertain conditions when data appears in the form of sequence flow in online mode. However, this study lacks reasonable experiments to verify the conclusions [1]. While most adjunct faculty tends to have clinical expertise, many lack formal training in online teaching. Slade et al. described how faculty utilizes technology to develop and implement faculty support sites. This provides ongoing mentoring to online adjunct faculty and encourages informal mentoring relationships. The shortcoming of this research is the lack of specific recommendations [2]. Qu and Chen combine the TAM (technology acceptance model) with PAD (pleasure, arousal, domination) to propose a new comprehensive model. They elucidated the influence of teachers’ emotions on the intention to use PBL (project-based learning) online systems. The results show that in the original TAM, the perceived usefulness and perceived ease of use variables are both positive for attitudes and intentions to use the PBL online system. Among PAD variables, pleasure and arousal variables were positively correlated with attitude variables. The dominance variable is positively correlated with the perceived ease of use variable [3]. Zhao and Shan selected 2019 as the research material through the online learning support service system. They used the weight setting of feature attributes and learning evaluation algorithm to analyze the learning result data [4]. Da’I et al. aim to identify the motivation of sports students through online learning for the 2020/2021 academic year. Da’I et al. used descriptive survey methods and questionnaires for data collection. The results show that through the online learning system physical education subjects of SMA + AL Fatimah Bojonegoro in class X 2020/2021, the students’ learning motivation belongs to the “moderate” group, which is 73.21%. The learning motivation of PE students belongs to the middle category [5]. Perumal I proposed an adaptive learning system framework. It combines multiple sources of personalization, including prior knowledge, working memory capacity, and learning styles. The various results of the analysis suggest a positive relationship between the technical skills, attitudes and behaviors, flexibility, government policies, and availability of virtual learning practices of primary school teachers in Malaysia [6]. Hussain et al. selected two educational institutions in Sargodha by a convenient sampling. They used questionnaires as a data collection tool to record the personal opinions of the participants’ teachers. Data analysis employed descriptive and inferential statistical designs. The overall survey results show that online learning is an effective learning system. It can meet the educational needs of distance learners, but the questionnaires in this experiment are not designed comprehensively [7].


## 3. Design Method of the College Sports Online Learning System Based on the BP Neural Network

### 3.1. BP Neural Network

#### 3.1.1. Basic Structure

The BP neural network, also known as the multilayer feedforward neural network, can be divided into the input layer, hidden layer, and output layer. The characteristics of this neural network model are as follows: neurons in each layer only have connections between neurons in adjacent layers; there is no connection between neurons in each layer; there is no feedback connection between neurons in each layer. The topology of the BP neural network is shown in Figure1.Figure 1
BP neural network topology.When an input pattern is provided to the network, the input pattern is sent from the input layer unit to the hidden layer unit. Then, according to the connection path, the error is returned to each layer and the weight and threshold of each layer connection are corrected to reduce the error [8]. After it changes the connection weights and thresholds, the new connection weights are used. Values and thresholds are computed from input patterns, which generate output responses that match the expected output. It iterates until the error is less than the specified value.


#### 3.1.2. BP Neural Network Algorithm

The most basic principle of the BP algorithm is error correction. The learning process includes forward calculation and error backward calculation. Taking the 3-layer BP neural network as an example, the whole learning process can be divided into the following steps:(1) Initializing the Network and Parameters. Initialized parameters are as follows: the connection weight W1 between the input layer and the hidden layer neurons, the connection weight W2 between the hidden layer and the output layer neurons, the threshold of the hidden layer neurons h1, and the output layer neuron threshold h2.(2) Input Training Samples. The training samples of BP neural network are the input vector and expected output vector.(3) Response Values of the Hidden Layer and Output Layer. The output of the hidden layer node h is as follows:
(1)yhk=f∑i=1N1W1∙xi+h1.The output of the output layer nodes is as follows:
(2)osk=g∑h=1N2W2∙yh+h2.Among them,xi is the input signal, y is the output of the hidden layer node, and k represents the kth sample in the sample set. f and g are transfer functions; the most commonly used is the sigmoid function.(4) Calculation of Network Error. In the BP learning algorithm, the error function can be defined by whether the network output generated by a single sample is consistent with the expected response output:
(3)E=∑S=1N3Tsk−osk2.The global sum of squares errorE of the network can also be used to judge whether the actual response output of the network output layer is consistent with the expected response output [9]. Its defined error function is as follows:
(4)E=∑k=1N∑S=1N3Tsk−osk2.(5) Calculation of the Correction Error of Each Neuron in the Output Layer and the Hidden Layer. It assumes that the expected output vector of the neurons in the output layer is Ts=T1,T2,⋯,TN and the actual output vector is Os=O1,O2,⋯,ON. Then, the correction error of the output layer neurons is as follows:
(5)Ds=osktsk−osk1−osk,where s=1,2,⋯,N and the correction error of each neuron in the hidden layer is as follows:
(6)Eh=∑s=1N3DsW2yh1−yh,where h=1,2,⋯,N.(6) Correction of Connection Weights and Thresholds. The standard BP algorithm is a gradient descent algorithm. To minimize the error, the steepest gradient descent method is used to optimize the weights. The direction of change of its weights and thresholds is the direction of the fastest decline according to the operation processing function—the negative direction of the gradient. The adjustment amount of the weight is as follows:
(7)∆W2=−μ∂E∂W2=μDsyh,∆W1=−μ∂E∂W1=μEhxi.Threshold adjustment amount is as follows:(8)θh=μ∂E∂θh,θs=μ∂E∂θs,θk+1=θk+μ∂E∂θk,where μ represents the learning step size.


#### 3.1.3. Levenberg-Marquardt BP Improved Algorithm

This paper adopts the LMBP algorithm, which is an improved BP algorithm. Compared with the standard BP algorithm, LMBP overcomes the shortcomings of poor convergence performance and long convergence time and the number of iteration steps increases greatly with the increase of precision or even does not converge. For medium-sized networks, the LMBP algorithm is the most suitable. For a three-layer neural network, the algorithm is as follows:(9)Xk+1=Xk−JTXkJXk+θkI−1∗JTXkVXk,where JXk is the Jacobian matrix, I is the identity matrix, VXk is the error vector, and Xk is the parameter vector.The steps of the algorithm are as follows:(1)
It submits all inputs to the network and computes the corresponding network outputs and errors, computing the sum of squared errors for all inputs(2)
It first initializes the sensitivity, recursively calculates the sensitivity, and then augments each individual matrix into the Marquardt sensitivity(3)
It repeatedly calculates the sum of squared errors. The algorithm is considered to converge when the magnitude of the gradient is less than a given value or the sum of the squared errors is less than a certain target error [9, 10]


### 3.2. Recommended Methods of College Sports Learning Paths

#### 3.2.1. User-Based Collaborative Filtering Algorithm Construction

The recommendation algorithm used in this paper is a user-based collaborative filtering algorithm. The basic idea of user-based collaborative filtering recommendation algorithm is that similar users have similar interests. The items they like are also roughly the same, and users are recommended to items that similar users like. The recommendation diagram of the user-based collaborative filtering recommendation algorithm is shown in Figure2:Figure 2
User-based recommendation algorithm.Proceed as follows:(1)
Construction of the scoring matrixThe user’s rating information for items is obtained from the system, and a user-item rating matrix is constructed [11]. It assumes that there are X users and Y items in the system; then, the rating matrix is X∗Y. Its form is as follows:
(10)R=r11r12r21r22⋯r1Y⋯r2Y⋯⋯rX1rX2⋯⋯⋯rXY.Each row represents the user’s evaluation of the system item, and each column represents the number of users who evaluated the item. If the user does not evaluate the item, the corresponding position is 0.(2)
Similarity between usersThe commonly used similarity calculation methods are as follows: modified cosine similarity, cosine similarity, Pearson correlation coefficient, etc. Cosine similarity is called cosine similarity. It evaluates the similarity of two vectors by calculating the cosine of their angle. Cosine similarity draws vectors according to coordinate values into a vector space, such as the most common two-dimensional space. The formula for calculating cosine similarity is as follows:(11)simu,v=u⟶∙v⟶u⟶∗v⟶=∑uivi∑ui2∑vi2.The formula for calculating the Pearson correlation coefficient is as follows:(12)simu,v=∑i∈Iu,vRui−Ru¯Rvi−Rv¯∑i∈Iu,vRui−Ru¯2∑i∈Iu,vRvi−Rv¯2.u⟶ and v⟶ represent the feature vectors of user u and user v, respectively, and Iu and Iv represent the set of rating items of user u and user v, respectively. Iu,v means the set of items that both user u and user v have rated. Rui and Rvi denote user u and user v’s ratings for item i, respectively. Ru¯ and Rv¯ represent the mean of item ratings for user u and user v, respectively [12].
(3)
Recommend content to target usersUsing the similarity between the target user and users of similar user groups, the target user’s preference for unrated items is calculated according to formula (12). Finally, it sorts the calculated favorite degrees in a descending order and selects N items as the candidate set recommended by the target user.
(13)Pui=Ru¯+∑v∈Ki∩Su,nsimu,v∗Rvi−Rv¯∑v∈Ki∩Su,nsimu,v.In the formula,Ki represents the set of users who have evaluated the item and Su,n represents the n users who are most similar to user u.


#### 3.2.2. Data Processing Hadoop

Hadoop is an open-source computing platform that maximizes the power of distributed clusters for high-speed computing and storage. Developers can easily develop and run applications that process large amounts of data on Hadoop. The core technologies of Hadoop are the distributed file system HDFS and the MapReduce programming model [13, 14]. HDFS is mainly used for storage of large-scale datasets, while MapReduce is mainly used for parallel operations of large-scale datasets.(1) HDFS. HDF meters use a master-slave architecture to implement data storage. Its architecture is shown in Figure 3.Figure 3
HDFS architecture diagram.Client is the client of the distributed file system and is mainly responsible for processing user access requests and requests for uploading files. The NameNode is used to manage the namespace of the entire distributed file system. DataNode is the place where the decentralized file system stores data and plays the role of storing block data in the file system [15].(2) MapReduce. The basic idea of MapReduce is to manage separately. First, the computing task is divided into multiple small computing tasks, and then, each small computing task is distributed to each server in the cluster for independent computing. Finally, the results of each server are calculated and combined to obtain the final calculation result. Its calculation process is shown in Figure 4.Figure 4
MapReduce calculation process.


#### 3.2.3. Inspirational Information

Heuristic information means that after learning a learning object, the learner will choose the learning object that suits him according to his intuition. Intuition mainly includes one’s own knowledge level, learning style, and media preference [16, 17]. Learners will compare their own knowledge level with the difficulty of the learning object, their own learning style, and the applicable style of the learning object. They also compare their own interest-type preferences with those of the subjects that they are learning and then make a comprehensive choice.(1) Knowledge Level. It sets the difficulty of learning object m as dm, and the knowledge level of target learner L to the knowledge points attached to j (defined in the domain knowledge model of this paper) as l0. The knowledge level heuristic information formula is as follows:
(14)ηL,m−L=1−l0−dm.(2) Learning Style. The learner learning style is a vector. Let the learning style of user L be sL and the knowledge expression characteristics of learning object m be cm. The learning style heuristic formula is as follows:
(15)ηL,m−S=1−sL−cm.(3) Interests and Preferences. The learner interest preference is a vector. Let the interest preference of user L be hL and the media type of learning object m be qm. It gets the heuristic formula of interest preference:
(16)ηL,m−h=1−hL−qm.Based on the knowledge level, learner style, interest, and preference, the inspiration information formula is obtained:(17)ηL,m=1−l0−dm∗1−sL−cm∗1−hL−qm.


### 3.3. Functional Design of the College Sports Online Learning System

#### 3.3.1. Basic Functional Framework Design

The online learning system has three roles: student user, teacher user, and administrator. Student users can log in to the system to perform the course study, course evaluation, completion of course assignments, and download of course resources. Teachers log in to the system to maintain and manage course resources, student information, and personal information. Administrators can view and modify teacher information, student information, and course information. The administrator side includes teacher user management rights [18].The functional modules of this system are mainly divided into four core modules: user registration and login module, online learning module, course recommendation module, and background management module. If a new user registers first, fill in the information that meets the specifications on the registration page, including the verification code, account password, and other information. After the background verification is passed and the data is stored, it will jump to the system home page. The content of the registration page is concise and easier for users to operate. The online learning module is one of the core functions of the online learning system. It makes recommendations based on behavioral data generated by users of this module. In this module, users can conduct the course study, complete course assignments, download course resources, and evaluate courses. The specific algorithm has been introduced in the previous section. The course recommendation module provides users with more accurate course push, which effectively improves the efficiency of user learning. The background management module contains two roles of teachers and administrators. It is mainly to perform user information management, course resource management, authority management, and other operations.


#### 3.3.2. Learning the Operating Mechanism of the System Engine

The operation process of the online learning system includes 6 major links: knowledge point selection, learning situation diagnosis, learning path recommendation, learning process recording, learning result diagnosis, and learning effect evaluation. It is specifically as shown in Figure5.Figure 5
The operating mechanism of the learning system.Learners enter the online learning system and select knowledge points to learn. After selecting knowledge points, extract learning objectives and conduct learning diagnosis. The system recommends knowledge points to learners based on their identity information or diagnosis results. The selected resource is pushed and presented to the learner, and the presentation method is determined according to the characteristics of the learning resource. The system tracks and records the learning situation of learners in real time. After the learner has completed the learning, it is diagnosed and the degree of knowledge mastery of the learner is assessed. Finally, it evaluates the learners in combination with the learning process and the results. The evaluation is jointly conducted by the teacher and the platform [19].


#### 3.3.3. Acquisition of Teacher Guidance Information

Teacher-led learning needs to capture the learning situation of students. This paper will comprehensively consider the offline learning information, the external learning information of the system, and the internal learning information of the system and use data analysis technology to provide information assistance for teachers to guide learning. The process of obtaining the guidance information is shown in Figure6.Figure 6
Flowchart for obtaining study guide information.In the whole process, in the first step, teachers import the standardized evaluation scores of students into the student scoring management system. The evaluation scores generated by students in other external learning systems are imported into the big data exchange center [20]. Through the filtering and transformation of the data center, the data is stored in the student’s grade management system and the two parts of the data together form the student’s standardized evaluation data. The second step is the diagnosis of learning effect. The learner’s evaluation data is obtained from the performance management system and combined with the learner’s learning action database. Through daily practice, homework results, and other learning process data, analyze the learning effect of learners on knowledge. The third step is to analyze the data of group learning effect taught by teachers to students according to the data of group learning situation and knowledge point learning situation, so as to obtain each learning situation. The system actively recommends tutoring information, and teachers decide whether to carry out tutoring activities based on their specific learning situation and their own teaching experience. That is, it is up to the subject teachers to decide whether or not to carry out the guiding activities.


## 3.1. BP Neural Network

### 3.1.1. Basic Structure

The BP neural network, also known as the multilayer feedforward neural network, can be divided into the input layer, hidden layer, and output layer. The characteristics of this neural network model are as follows: neurons in each layer only have connections between neurons in adjacent layers; there is no connection between neurons in each layer; there is no feedback connection between neurons in each layer. The topology of the BP neural network is shown in Figure1.Figure 1
BP neural network topology.When an input pattern is provided to the network, the input pattern is sent from the input layer unit to the hidden layer unit. Then, according to the connection path, the error is returned to each layer and the weight and threshold of each layer connection are corrected to reduce the error [8]. After it changes the connection weights and thresholds, the new connection weights are used. Values and thresholds are computed from input patterns, which generate output responses that match the expected output. It iterates until the error is less than the specified value.


### 3.1.2. BP Neural Network Algorithm

The most basic principle of the BP algorithm is error correction. The learning process includes forward calculation and error backward calculation. Taking the 3-layer BP neural network as an example, the whole learning process can be divided into the following steps:(1) Initializing the Network and Parameters. Initialized parameters are as follows: the connection weight W1 between the input layer and the hidden layer neurons, the connection weight W2 between the hidden layer and the output layer neurons, the threshold of the hidden layer neurons h1, and the output layer neuron threshold h2.(2) Input Training Samples. The training samples of BP neural network are the input vector and expected output vector.(3) Response Values of the Hidden Layer and Output Layer. The output of the hidden layer node h is as follows:
(1)yhk=f∑i=1N1W1∙xi+h1.The output of the output layer nodes is as follows:
(2)osk=g∑h=1N2W2∙yh+h2.Among them,xi is the input signal, y is the output of the hidden layer node, and k represents the kth sample in the sample set. f and g are transfer functions; the most commonly used is the sigmoid function.(4) Calculation of Network Error. In the BP learning algorithm, the error function can be defined by whether the network output generated by a single sample is consistent with the expected response output:
(3)E=∑S=1N3Tsk−osk2.The global sum of squares errorE of the network can also be used to judge whether the actual response output of the network output layer is consistent with the expected response output [9]. Its defined error function is as follows:
(4)E=∑k=1N∑S=1N3Tsk−osk2.(5) Calculation of the Correction Error of Each Neuron in the Output Layer and the Hidden Layer. It assumes that the expected output vector of the neurons in the output layer is Ts=T1,T2,⋯,TN and the actual output vector is Os=O1,O2,⋯,ON. Then, the correction error of the output layer neurons is as follows:
(5)Ds=osktsk−osk1−osk,where s=1,2,⋯,N and the correction error of each neuron in the hidden layer is as follows:
(6)Eh=∑s=1N3DsW2yh1−yh,where h=1,2,⋯,N.(6) Correction of Connection Weights and Thresholds. The standard BP algorithm is a gradient descent algorithm. To minimize the error, the steepest gradient descent method is used to optimize the weights. The direction of change of its weights and thresholds is the direction of the fastest decline according to the operation processing function—the negative direction of the gradient. The adjustment amount of the weight is as follows:
(7)∆W2=−μ∂E∂W2=μDsyh,∆W1=−μ∂E∂W1=μEhxi.Threshold adjustment amount is as follows:(8)θh=μ∂E∂θh,θs=μ∂E∂θs,θk+1=θk+μ∂E∂θk,where μ represents the learning step size.


### 3.1.3. Levenberg-Marquardt BP Improved Algorithm

This paper adopts the LMBP algorithm, which is an improved BP algorithm. Compared with the standard BP algorithm, LMBP overcomes the shortcomings of poor convergence performance and long convergence time and the number of iteration steps increases greatly with the increase of precision or even does not converge. For medium-sized networks, the LMBP algorithm is the most suitable. For a three-layer neural network, the algorithm is as follows:(9)Xk+1=Xk−JTXkJXk+θkI−1∗JTXkVXk,where JXk is the Jacobian matrix, I is the identity matrix, VXk is the error vector, and Xk is the parameter vector.The steps of the algorithm are as follows:(1)
It submits all inputs to the network and computes the corresponding network outputs and errors, computing the sum of squared errors for all inputs(2)
It first initializes the sensitivity, recursively calculates the sensitivity, and then augments each individual matrix into the Marquardt sensitivity(3)
It repeatedly calculates the sum of squared errors. The algorithm is considered to converge when the magnitude of the gradient is less than a given value or the sum of the squared errors is less than a certain target error [9, 10]


## 3.1.1. Basic Structure

The BP neural network, also known as the multilayer feedforward neural network, can be divided into the input layer, hidden layer, and output layer. The characteristics of this neural network model are as follows: neurons in each layer only have connections between neurons in adjacent layers; there is no connection between neurons in each layer; there is no feedback connection between neurons in each layer. The topology of the BP neural network is shown in Figure1.Figure 1
BP neural network topology.When an input pattern is provided to the network, the input pattern is sent from the input layer unit to the hidden layer unit. Then, according to the connection path, the error is returned to each layer and the weight and threshold of each layer connection are corrected to reduce the error [8]. After it changes the connection weights and thresholds, the new connection weights are used. Values and thresholds are computed from input patterns, which generate output responses that match the expected output. It iterates until the error is less than the specified value.


## 3.1.2. BP Neural Network Algorithm

The most basic principle of the BP algorithm is error correction. The learning process includes forward calculation and error backward calculation. Taking the 3-layer BP neural network as an example, the whole learning process can be divided into the following steps:(1) Initializing the Network and Parameters. Initialized parameters are as follows: the connection weight W1 between the input layer and the hidden layer neurons, the connection weight W2 between the hidden layer and the output layer neurons, the threshold of the hidden layer neurons h1, and the output layer neuron threshold h2.(2) Input Training Samples. The training samples of BP neural network are the input vector and expected output vector.(3) Response Values of the Hidden Layer and Output Layer. The output of the hidden layer node h is as follows:
(1)yhk=f∑i=1N1W1∙xi+h1.The output of the output layer nodes is as follows:
(2)osk=g∑h=1N2W2∙yh+h2.Among them,xi is the input signal, y is the output of the hidden layer node, and k represents the kth sample in the sample set. f and g are transfer functions; the most commonly used is the sigmoid function.(4) Calculation of Network Error. In the BP learning algorithm, the error function can be defined by whether the network output generated by a single sample is consistent with the expected response output:
(3)E=∑S=1N3Tsk−osk2.The global sum of squares errorE of the network can also be used to judge whether the actual response output of the network output layer is consistent with the expected response output [9]. Its defined error function is as follows:
(4)E=∑k=1N∑S=1N3Tsk−osk2.(5) Calculation of the Correction Error of Each Neuron in the Output Layer and the Hidden Layer. It assumes that the expected output vector of the neurons in the output layer is Ts=T1,T2,⋯,TN and the actual output vector is Os=O1,O2,⋯,ON. Then, the correction error of the output layer neurons is as follows:
(5)Ds=osktsk−osk1−osk,where s=1,2,⋯,N and the correction error of each neuron in the hidden layer is as follows:
(6)Eh=∑s=1N3DsW2yh1−yh,where h=1,2,⋯,N.(6) Correction of Connection Weights and Thresholds. The standard BP algorithm is a gradient descent algorithm. To minimize the error, the steepest gradient descent method is used to optimize the weights. The direction of change of its weights and thresholds is the direction of the fastest decline according to the operation processing function—the negative direction of the gradient. The adjustment amount of the weight is as follows:
(7)∆W2=−μ∂E∂W2=μDsyh,∆W1=−μ∂E∂W1=μEhxi.Threshold adjustment amount is as follows:(8)θh=μ∂E∂θh,θs=μ∂E∂θs,θk+1=θk+μ∂E∂θk,where μ represents the learning step size.


## 3.1.3. Levenberg-Marquardt BP Improved Algorithm

This paper adopts the LMBP algorithm, which is an improved BP algorithm. Compared with the standard BP algorithm, LMBP overcomes the shortcomings of poor convergence performance and long convergence time and the number of iteration steps increases greatly with the increase of precision or even does not converge. For medium-sized networks, the LMBP algorithm is the most suitable. For a three-layer neural network, the algorithm is as follows:(9)Xk+1=Xk−JTXkJXk+θkI−1∗JTXkVXk,where JXk is the Jacobian matrix, I is the identity matrix, VXk is the error vector, and Xk is the parameter vector.The steps of the algorithm are as follows:(1)
It submits all inputs to the network and computes the corresponding network outputs and errors, computing the sum of squared errors for all inputs(2)
It first initializes the sensitivity, recursively calculates the sensitivity, and then augments each individual matrix into the Marquardt sensitivity(3)
It repeatedly calculates the sum of squared errors. The algorithm is considered to converge when the magnitude of the gradient is less than a given value or the sum of the squared errors is less than a certain target error [9, 10]


## 3.2. Recommended Methods of College Sports Learning Paths

### 3.2.1. User-Based Collaborative Filtering Algorithm Construction

The recommendation algorithm used in this paper is a user-based collaborative filtering algorithm. The basic idea of user-based collaborative filtering recommendation algorithm is that similar users have similar interests. The items they like are also roughly the same, and users are recommended to items that similar users like. The recommendation diagram of the user-based collaborative filtering recommendation algorithm is shown in Figure2:Figure 2
User-based recommendation algorithm.Proceed as follows:(1)
Construction of the scoring matrixThe user’s rating information for items is obtained from the system, and a user-item rating matrix is constructed [11]. It assumes that there are X users and Y items in the system; then, the rating matrix is X∗Y. Its form is as follows:
(10)R=r11r12r21r22⋯r1Y⋯r2Y⋯⋯rX1rX2⋯⋯⋯rXY.Each row represents the user’s evaluation of the system item, and each column represents the number of users who evaluated the item. If the user does not evaluate the item, the corresponding position is 0.(2)
Similarity between usersThe commonly used similarity calculation methods are as follows: modified cosine similarity, cosine similarity, Pearson correlation coefficient, etc. Cosine similarity is called cosine similarity. It evaluates the similarity of two vectors by calculating the cosine of their angle. Cosine similarity draws vectors according to coordinate values into a vector space, such as the most common two-dimensional space. The formula for calculating cosine similarity is as follows:(11)simu,v=u⟶∙v⟶u⟶∗v⟶=∑uivi∑ui2∑vi2.The formula for calculating the Pearson correlation coefficient is as follows:(12)simu,v=∑i∈Iu,vRui−Ru¯Rvi−Rv¯∑i∈Iu,vRui−Ru¯2∑i∈Iu,vRvi−Rv¯2.u⟶ and v⟶ represent the feature vectors of user u and user v, respectively, and Iu and Iv represent the set of rating items of user u and user v, respectively. Iu,v means the set of items that both user u and user v have rated. Rui and Rvi denote user u and user v’s ratings for item i, respectively. Ru¯ and Rv¯ represent the mean of item ratings for user u and user v, respectively [12].
(3)
Recommend content to target usersUsing the similarity between the target user and users of similar user groups, the target user’s preference for unrated items is calculated according to formula (12). Finally, it sorts the calculated favorite degrees in a descending order and selects N items as the candidate set recommended by the target user.
(13)Pui=Ru¯+∑v∈Ki∩Su,nsimu,v∗Rvi−Rv¯∑v∈Ki∩Su,nsimu,v.In the formula,Ki represents the set of users who have evaluated the item and Su,n represents the n users who are most similar to user u.


### 3.2.2. Data Processing Hadoop

Hadoop is an open-source computing platform that maximizes the power of distributed clusters for high-speed computing and storage. Developers can easily develop and run applications that process large amounts of data on Hadoop. The core technologies of Hadoop are the distributed file system HDFS and the MapReduce programming model [13, 14]. HDFS is mainly used for storage of large-scale datasets, while MapReduce is mainly used for parallel operations of large-scale datasets.(1) HDFS. HDF meters use a master-slave architecture to implement data storage. Its architecture is shown in Figure 3.Figure 3
HDFS architecture diagram.Client is the client of the distributed file system and is mainly responsible for processing user access requests and requests for uploading files. The NameNode is used to manage the namespace of the entire distributed file system. DataNode is the place where the decentralized file system stores data and plays the role of storing block data in the file system [15].(2) MapReduce. The basic idea of MapReduce is to manage separately. First, the computing task is divided into multiple small computing tasks, and then, each small computing task is distributed to each server in the cluster for independent computing. Finally, the results of each server are calculated and combined to obtain the final calculation result. Its calculation process is shown in Figure 4.Figure 4
MapReduce calculation process.


### 3.2.3. Inspirational Information

Heuristic information means that after learning a learning object, the learner will choose the learning object that suits him according to his intuition. Intuition mainly includes one’s own knowledge level, learning style, and media preference [16, 17]. Learners will compare their own knowledge level with the difficulty of the learning object, their own learning style, and the applicable style of the learning object. They also compare their own interest-type preferences with those of the subjects that they are learning and then make a comprehensive choice.(1) Knowledge Level. It sets the difficulty of learning object m as dm, and the knowledge level of target learner L to the knowledge points attached to j (defined in the domain knowledge model of this paper) as l0. The knowledge level heuristic information formula is as follows:
(14)ηL,m−L=1−l0−dm.(2) Learning Style. The learner learning style is a vector. Let the learning style of user L be sL and the knowledge expression characteristics of learning object m be cm. The learning style heuristic formula is as follows:
(15)ηL,m−S=1−sL−cm.(3) Interests and Preferences. The learner interest preference is a vector. Let the interest preference of user L be hL and the media type of learning object m be qm. It gets the heuristic formula of interest preference:
(16)ηL,m−h=1−hL−qm.Based on the knowledge level, learner style, interest, and preference, the inspiration information formula is obtained:(17)ηL,m=1−l0−dm∗1−sL−cm∗1−hL−qm.


## 3.2.1. User-Based Collaborative Filtering Algorithm Construction

The recommendation algorithm used in this paper is a user-based collaborative filtering algorithm. The basic idea of user-based collaborative filtering recommendation algorithm is that similar users have similar interests. The items they like are also roughly the same, and users are recommended to items that similar users like. The recommendation diagram of the user-based collaborative filtering recommendation algorithm is shown in Figure2:Figure 2
User-based recommendation algorithm.Proceed as follows:(1)
Construction of the scoring matrixThe user’s rating information for items is obtained from the system, and a user-item rating matrix is constructed [11]. It assumes that there are X users and Y items in the system; then, the rating matrix is X∗Y. Its form is as follows:
(10)R=r11r12r21r22⋯r1Y⋯r2Y⋯⋯rX1rX2⋯⋯⋯rXY.Each row represents the user’s evaluation of the system item, and each column represents the number of users who evaluated the item. If the user does not evaluate the item, the corresponding position is 0.(2)
Similarity between usersThe commonly used similarity calculation methods are as follows: modified cosine similarity, cosine similarity, Pearson correlation coefficient, etc. Cosine similarity is called cosine similarity. It evaluates the similarity of two vectors by calculating the cosine of their angle. Cosine similarity draws vectors according to coordinate values into a vector space, such as the most common two-dimensional space. The formula for calculating cosine similarity is as follows:(11)simu,v=u⟶∙v⟶u⟶∗v⟶=∑uivi∑ui2∑vi2.The formula for calculating the Pearson correlation coefficient is as follows:(12)simu,v=∑i∈Iu,vRui−Ru¯Rvi−Rv¯∑i∈Iu,vRui−Ru¯2∑i∈Iu,vRvi−Rv¯2.u⟶ and v⟶ represent the feature vectors of user u and user v, respectively, and Iu and Iv represent the set of rating items of user u and user v, respectively. Iu,v means the set of items that both user u and user v have rated. Rui and Rvi denote user u and user v’s ratings for item i, respectively. Ru¯ and Rv¯ represent the mean of item ratings for user u and user v, respectively [12].
(3)
Recommend content to target usersUsing the similarity between the target user and users of similar user groups, the target user’s preference for unrated items is calculated according to formula (12). Finally, it sorts the calculated favorite degrees in a descending order and selects N items as the candidate set recommended by the target user.
(13)Pui=Ru¯+∑v∈Ki∩Su,nsimu,v∗Rvi−Rv¯∑v∈Ki∩Su,nsimu,v.In the formula,Ki represents the set of users who have evaluated the item and Su,n represents the n users who are most similar to user u.


## 3.2.2. Data Processing Hadoop

Hadoop is an open-source computing platform that maximizes the power of distributed clusters for high-speed computing and storage. Developers can easily develop and run applications that process large amounts of data on Hadoop. The core technologies of Hadoop are the distributed file system HDFS and the MapReduce programming model [13, 14]. HDFS is mainly used for storage of large-scale datasets, while MapReduce is mainly used for parallel operations of large-scale datasets.(1) HDFS. HDF meters use a master-slave architecture to implement data storage. Its architecture is shown in Figure 3.Figure 3
HDFS architecture diagram.Client is the client of the distributed file system and is mainly responsible for processing user access requests and requests for uploading files. The NameNode is used to manage the namespace of the entire distributed file system. DataNode is the place where the decentralized file system stores data and plays the role of storing block data in the file system [15].(2) MapReduce. The basic idea of MapReduce is to manage separately. First, the computing task is divided into multiple small computing tasks, and then, each small computing task is distributed to each server in the cluster for independent computing. Finally, the results of each server are calculated and combined to obtain the final calculation result. Its calculation process is shown in Figure 4.Figure 4
MapReduce calculation process.


## 3.2.3. Inspirational Information

Heuristic information means that after learning a learning object, the learner will choose the learning object that suits him according to his intuition. Intuition mainly includes one’s own knowledge level, learning style, and media preference [16, 17]. Learners will compare their own knowledge level with the difficulty of the learning object, their own learning style, and the applicable style of the learning object. They also compare their own interest-type preferences with those of the subjects that they are learning and then make a comprehensive choice.(1) Knowledge Level. It sets the difficulty of learning object m as dm, and the knowledge level of target learner L to the knowledge points attached to j (defined in the domain knowledge model of this paper) as l0. The knowledge level heuristic information formula is as follows:
(14)ηL,m−L=1−l0−dm.(2) Learning Style. The learner learning style is a vector. Let the learning style of user L be sL and the knowledge expression characteristics of learning object m be cm. The learning style heuristic formula is as follows:
(15)ηL,m−S=1−sL−cm.(3) Interests and Preferences. The learner interest preference is a vector. Let the interest preference of user L be hL and the media type of learning object m be qm. It gets the heuristic formula of interest preference:
(16)ηL,m−h=1−hL−qm.Based on the knowledge level, learner style, interest, and preference, the inspiration information formula is obtained:(17)ηL,m=1−l0−dm∗1−sL−cm∗1−hL−qm.


## 3.3. Functional Design of the College Sports Online Learning System

### 3.3.1. Basic Functional Framework Design

The online learning system has three roles: student user, teacher user, and administrator. Student users can log in to the system to perform the course study, course evaluation, completion of course assignments, and download of course resources. Teachers log in to the system to maintain and manage course resources, student information, and personal information. Administrators can view and modify teacher information, student information, and course information. The administrator side includes teacher user management rights [18].The functional modules of this system are mainly divided into four core modules: user registration and login module, online learning module, course recommendation module, and background management module. If a new user registers first, fill in the information that meets the specifications on the registration page, including the verification code, account password, and other information. After the background verification is passed and the data is stored, it will jump to the system home page. The content of the registration page is concise and easier for users to operate. The online learning module is one of the core functions of the online learning system. It makes recommendations based on behavioral data generated by users of this module. In this module, users can conduct the course study, complete course assignments, download course resources, and evaluate courses. The specific algorithm has been introduced in the previous section. The course recommendation module provides users with more accurate course push, which effectively improves the efficiency of user learning. The background management module contains two roles of teachers and administrators. It is mainly to perform user information management, course resource management, authority management, and other operations.


### 3.3.2. Learning the Operating Mechanism of the System Engine

The operation process of the online learning system includes 6 major links: knowledge point selection, learning situation diagnosis, learning path recommendation, learning process recording, learning result diagnosis, and learning effect evaluation. It is specifically as shown in Figure5.Figure 5
The operating mechanism of the learning system.Learners enter the online learning system and select knowledge points to learn. After selecting knowledge points, extract learning objectives and conduct learning diagnosis. The system recommends knowledge points to learners based on their identity information or diagnosis results. The selected resource is pushed and presented to the learner, and the presentation method is determined according to the characteristics of the learning resource. The system tracks and records the learning situation of learners in real time. After the learner has completed the learning, it is diagnosed and the degree of knowledge mastery of the learner is assessed. Finally, it evaluates the learners in combination with the learning process and the results. The evaluation is jointly conducted by the teacher and the platform [19].


### 3.3.3. Acquisition of Teacher Guidance Information

Teacher-led learning needs to capture the learning situation of students. This paper will comprehensively consider the offline learning information, the external learning information of the system, and the internal learning information of the system and use data analysis technology to provide information assistance for teachers to guide learning. The process of obtaining the guidance information is shown in Figure6.Figure 6
Flowchart for obtaining study guide information.In the whole process, in the first step, teachers import the standardized evaluation scores of students into the student scoring management system. The evaluation scores generated by students in other external learning systems are imported into the big data exchange center [20]. Through the filtering and transformation of the data center, the data is stored in the student’s grade management system and the two parts of the data together form the student’s standardized evaluation data. The second step is the diagnosis of learning effect. The learner’s evaluation data is obtained from the performance management system and combined with the learner’s learning action database. Through daily practice, homework results, and other learning process data, analyze the learning effect of learners on knowledge. The third step is to analyze the data of group learning effect taught by teachers to students according to the data of group learning situation and knowledge point learning situation, so as to obtain each learning situation. The system actively recommends tutoring information, and teachers decide whether to carry out tutoring activities based on their specific learning situation and their own teaching experience. That is, it is up to the subject teachers to decide whether or not to carry out the guiding activities.


## 3.3.1. Basic Functional Framework Design

The online learning system has three roles: student user, teacher user, and administrator. Student users can log in to the system to perform the course study, course evaluation, completion of course assignments, and download of course resources. Teachers log in to the system to maintain and manage course resources, student information, and personal information. Administrators can view and modify teacher information, student information, and course information. The administrator side includes teacher user management rights [18].The functional modules of this system are mainly divided into four core modules: user registration and login module, online learning module, course recommendation module, and background management module. If a new user registers first, fill in the information that meets the specifications on the registration page, including the verification code, account password, and other information. After the background verification is passed and the data is stored, it will jump to the system home page. The content of the registration page is concise and easier for users to operate. The online learning module is one of the core functions of the online learning system. It makes recommendations based on behavioral data generated by users of this module. In this module, users can conduct the course study, complete course assignments, download course resources, and evaluate courses. The specific algorithm has been introduced in the previous section. The course recommendation module provides users with more accurate course push, which effectively improves the efficiency of user learning. The background management module contains two roles of teachers and administrators. It is mainly to perform user information management, course resource management, authority management, and other operations.


## 3.3.2. Learning the Operating Mechanism of the System Engine

The operation process of the online learning system includes 6 major links: knowledge point selection, learning situation diagnosis, learning path recommendation, learning process recording, learning result diagnosis, and learning effect evaluation. It is specifically as shown in Figure5.Figure 5
The operating mechanism of the learning system.Learners enter the online learning system and select knowledge points to learn. After selecting knowledge points, extract learning objectives and conduct learning diagnosis. The system recommends knowledge points to learners based on their identity information or diagnosis results. The selected resource is pushed and presented to the learner, and the presentation method is determined according to the characteristics of the learning resource. The system tracks and records the learning situation of learners in real time. After the learner has completed the learning, it is diagnosed and the degree of knowledge mastery of the learner is assessed. Finally, it evaluates the learners in combination with the learning process and the results. The evaluation is jointly conducted by the teacher and the platform [19].


## 3.3.3. Acquisition of Teacher Guidance Information

Teacher-led learning needs to capture the learning situation of students. This paper will comprehensively consider the offline learning information, the external learning information of the system, and the internal learning information of the system and use data analysis technology to provide information assistance for teachers to guide learning. The process of obtaining the guidance information is shown in Figure6.Figure 6
Flowchart for obtaining study guide information.In the whole process, in the first step, teachers import the standardized evaluation scores of students into the student scoring management system. The evaluation scores generated by students in other external learning systems are imported into the big data exchange center [20]. Through the filtering and transformation of the data center, the data is stored in the student’s grade management system and the two parts of the data together form the student’s standardized evaluation data. The second step is the diagnosis of learning effect. The learner’s evaluation data is obtained from the performance management system and combined with the learner’s learning action database. Through daily practice, homework results, and other learning process data, analyze the learning effect of learners on knowledge. The third step is to analyze the data of group learning effect taught by teachers to students according to the data of group learning situation and knowledge point learning situation, so as to obtain each learning situation. The system actively recommends tutoring information, and teachers decide whether to carry out tutoring activities based on their specific learning situation and their own teaching experience. That is, it is up to the subject teachers to decide whether or not to carry out the guiding activities.


## 4. Realization and Application of the Online Learning System

### 4.1. System Design

#### 4.1.1. Overall System Structure

Figure7 shows the overall structure of the online learning system in colleges and universities. The client is mainly developed on the Android platform, while the server runs on a high-performance Linux host. In the actual development of this system, it is realized based on Java language programming. The mobile service platform can be divided into three parts: front end, back end, and processing. The front-end part of the platform is mainly responsible for parsing and responding to terminal requests. The back-end part is made up of a series of connectors. In addition to realizing the connection with the existing teaching system, this system also needs to consider the connection with other systems in the future, so as to realize the expansibility of the teaching system. The processing part of the platform mainly completes protocol data conversion, logic processing of specific mobile services, etc. Through the mobile terminal, users can selectively obtain rich online teaching resources according to their personalized learning needs. They can also conduct mutual learning among students by establishing mobile learning groups. At the same time, teachers and students can also conduct two-way communication through discussion areas and emails.Figure 7
Learning system architecture.


#### 4.1.2. System Database Design

The system uses the MySQL database to store and manage data. The data table of each module includes the user information table, course information table, course evaluation table, course work table, and other main data tables.(1) User Information Form. User attribute information is stored in the user information table, and its design is shown in Table 1.Table 1
User information form.
Field nameData typeDescribeUser_idInt(11)User IDpasswordVarchar(32)User passwordusernameVarchar(60)User namegenderVarchar(20)User genderemailVarchar(50)User mailbox(2) Course Information Sheet. The course information table contains course attribute information required in the recommendation algorithm. It mainly records the detailed information of each subject course, including course description information, course difficulty level, course number of students, and other information. The design of the course information table is shown in Table 2.Table 2
Course information sheet.
Field nameData typeDescribeidInt(11)Course IDClass_idInt(11)Course classification IDnameVarchar(200)Course nameLearn_timesIntegerLearning timesstudentsIntegerNumber of studentsdescriptionVarchar(2000)Number of students(3) Course Evaluation Form. The course evaluation data table stores the user’s evaluation and rating data for the course. The design of the course evaluation data table is shown in Table 3.Table 3
Course evaluation form.
Field nameData typeDescribeidInt(20)Evaluation IDcidInt(11)Course IDuidInt(11)User IDcontentTextEvaluation contentgradeInt(4)ScorecreatetimeDatetimeEvaluation date(4) Course Work Sheet. The coursework data table stores the detailed information of the assignment, including the assignment topic and topic options. A course corresponds to multiple assignments. The design of the coursework data table is shown in Table 4.Table 4
Coursework schedule.
Field nameData typeDescribeCourse_idVarchar(20)Course IDUser_idVarchar(20)User IDExam_idVarcharJob IDtitleVarcharHomework topic


#### 4.1.3. Implementation of Each Module of the System

(1) User Login Registration Module. The user login module is the basic link of course learning. New users should register first and fill in the user name, mobile phone number, password, and other information on the registration page according to the specifications. After they fill in the form, they click the register and log in button and the registration operation is completed after searching and saving in the database. If the database query finds that the mobile phone number already exists, it will prompt “The account already exists, please log in.” The user clicks the “Existing account, log in now” button in the lower left corner of the registration form or the login button at the top of the home page to enter the login interface.(2) Online Learning Modules. This module is the core module of the system. After logging in to the system, users can select the courses that they are interested in in the course list on the homepage to start the course study or continue to study courses on the course page of the personal center. After entering the course details page, users can view the course profile, teacher and institution profiles, and course details. They can choose to bookmark courses, start learning, etc. and click to start learning to enter the learning page.(3) Course Recommendation Module. After entering the system, users can view the recommended list of popular courses on the right side of the all courses page. The main information of the course is displayed in the recommendation list, including the course title, difficulty level, number of participants, total course duration, and other information. Users can select courses of interest by clicking on the title or course icon.(4) Background Management Module. The background management module of this system adopts dual-authority settings, including two roles as a teacher and administrator. Administrators can perform functions such as viewing and modifying student and teacher information, user permissions, and maintaining course information. To sum up, whether in the stability of the system processing or in the response speed, the system performs well, which proves that the performance of the system is relatively good.


### 4.2. System Performance Test Results and Analysis

This experiment mainly detects the nonfunctional aspects of the system through performance testing. This system is an online learning platform, and the number of users visiting the website is large. In order to verify that the system can run normally and stably in the case of multiple clients, the computing response time of the system is tested. Taking the course learning module as an example, 100 clients are simulated and each client sends an HTTP request at an interval of 1 second to perform a system performance test. The test results are shown in Figure8.Figure 8
Performance test graph.As can be seen in Figure8, the system’s requests per second and responses per second are almost the same. It proves that there is no error or timeout in the request processing of the system and the stability of the system is very good. Looking at the response time of the system, the response time per second of the system is within the interval of [0.8, 1.2]. And within one minute of the test, the average response time of the system was 0.978 s. This value is ideal, and it proves that the system also performs well in terms of running speed.


### 4.3. Student Satisfaction Questionnaire

In this experiment, 200 students and 20 teachers of physical education majors in 3 colleges and universities in a certain area were selected as the experimental objects. It allows them to experience the online learning system designed in this paper and then conducts a satisfaction survey of these students and teachers.


#### 4.3.1. Questionnaire Design

There are two questions in this questionnaire, as follows:(1)
Do you think this learning system can improve your learning (teaching) efficiency(2)
Do you like the learning format based on this learning systemOf these two questions, the first question is to investigate student and teacher acceptance of the learning system. The second question was to explore how much students and teachers liked the learning system. The two questions explore students’ and teachers’ satisfaction with the learning system from two perspectives, respectively.


#### 4.3.2. Recognition Survey

This experiment investigates whether students and teachers think that the new model course is helpful to their learning or teaching and the results are shown in Figure9.Figure 9
Recognition survey results.As can be seen in Figure9, for students, the number of students who choose to agree that the new model course can improve their learning efficiency is the largest, reaching 42%. The number of people who chose to strongly agree came second, reaching 39.5%, while the number of people who chose to disagree was only 3%. This proves that students have a high degree of recognition of the new model course. For teachers, those who choose to strongly agree that the new model curriculum can improve their teaching efficiency is the largest number, reaching 40%. The least number of people who choose to disagree is 10%. Therefore, teachers’ recognition of the learning system is also relatively high.


#### 4.3.3. Likeness Survey

A survey was conducted on students’ and teachers’ liking for the learning system, and the results are shown in Figure10.Figure 10
Likeability survey results.It can be seen that among the students, the number of people who choose to like the online learning system is the largest, accounting for 37%. The number of people who choose not to like it only accounts for 5%, which is the least. Among teachers, the number of people who choose to like the online learning system very much is the largest, accounting for 35%, and the number of people who choose not to like it is also the least. To sum up, it can be seen that both students and teachers are satisfied with the learning system designed in this paper.It can be seen from the above recognition survey and favorite survey that both students and teachers are highly satisfied with the learning system in this paper and there is no significant difference between the satisfaction of teachers and students.


## 4.1. System Design

### 4.1.1. Overall System Structure

Figure7 shows the overall structure of the online learning system in colleges and universities. The client is mainly developed on the Android platform, while the server runs on a high-performance Linux host. In the actual development of this system, it is realized based on Java language programming. The mobile service platform can be divided into three parts: front end, back end, and processing. The front-end part of the platform is mainly responsible for parsing and responding to terminal requests. The back-end part is made up of a series of connectors. In addition to realizing the connection with the existing teaching system, this system also needs to consider the connection with other systems in the future, so as to realize the expansibility of the teaching system. The processing part of the platform mainly completes protocol data conversion, logic processing of specific mobile services, etc. Through the mobile terminal, users can selectively obtain rich online teaching resources according to their personalized learning needs. They can also conduct mutual learning among students by establishing mobile learning groups. At the same time, teachers and students can also conduct two-way communication through discussion areas and emails.Figure 7
Learning system architecture.


### 4.1.2. System Database Design

The system uses the MySQL database to store and manage data. The data table of each module includes the user information table, course information table, course evaluation table, course work table, and other main data tables.(1) User Information Form. User attribute information is stored in the user information table, and its design is shown in Table 1.Table 1
User information form.
Field nameData typeDescribeUser_idInt(11)User IDpasswordVarchar(32)User passwordusernameVarchar(60)User namegenderVarchar(20)User genderemailVarchar(50)User mailbox(2) Course Information Sheet. The course information table contains course attribute information required in the recommendation algorithm. It mainly records the detailed information of each subject course, including course description information, course difficulty level, course number of students, and other information. The design of the course information table is shown in Table 2.Table 2
Course information sheet.
Field nameData typeDescribeidInt(11)Course IDClass_idInt(11)Course classification IDnameVarchar(200)Course nameLearn_timesIntegerLearning timesstudentsIntegerNumber of studentsdescriptionVarchar(2000)Number of students(3) Course Evaluation Form. The course evaluation data table stores the user’s evaluation and rating data for the course. The design of the course evaluation data table is shown in Table 3.Table 3
Course evaluation form.
Field nameData typeDescribeidInt(20)Evaluation IDcidInt(11)Course IDuidInt(11)User IDcontentTextEvaluation contentgradeInt(4)ScorecreatetimeDatetimeEvaluation date(4) Course Work Sheet. The coursework data table stores the detailed information of the assignment, including the assignment topic and topic options. A course corresponds to multiple assignments. The design of the coursework data table is shown in Table 4.Table 4
Coursework schedule.
Field nameData typeDescribeCourse_idVarchar(20)Course IDUser_idVarchar(20)User IDExam_idVarcharJob IDtitleVarcharHomework topic


### 4.1.3. Implementation of Each Module of the System

(1) User Login Registration Module. The user login module is the basic link of course learning. New users should register first and fill in the user name, mobile phone number, password, and other information on the registration page according to the specifications. After they fill in the form, they click the register and log in button and the registration operation is completed after searching and saving in the database. If the database query finds that the mobile phone number already exists, it will prompt “The account already exists, please log in.” The user clicks the “Existing account, log in now” button in the lower left corner of the registration form or the login button at the top of the home page to enter the login interface.(2) Online Learning Modules. This module is the core module of the system. After logging in to the system, users can select the courses that they are interested in in the course list on the homepage to start the course study or continue to study courses on the course page of the personal center. After entering the course details page, users can view the course profile, teacher and institution profiles, and course details. They can choose to bookmark courses, start learning, etc. and click to start learning to enter the learning page.(3) Course Recommendation Module. After entering the system, users can view the recommended list of popular courses on the right side of the all courses page. The main information of the course is displayed in the recommendation list, including the course title, difficulty level, number of participants, total course duration, and other information. Users can select courses of interest by clicking on the title or course icon.(4) Background Management Module. The background management module of this system adopts dual-authority settings, including two roles as a teacher and administrator. Administrators can perform functions such as viewing and modifying student and teacher information, user permissions, and maintaining course information. To sum up, whether in the stability of the system processing or in the response speed, the system performs well, which proves that the performance of the system is relatively good.


## 4.1.1. Overall System Structure

Figure7 shows the overall structure of the online learning system in colleges and universities. The client is mainly developed on the Android platform, while the server runs on a high-performance Linux host. In the actual development of this system, it is realized based on Java language programming. The mobile service platform can be divided into three parts: front end, back end, and processing. The front-end part of the platform is mainly responsible for parsing and responding to terminal requests. The back-end part is made up of a series of connectors. In addition to realizing the connection with the existing teaching system, this system also needs to consider the connection with other systems in the future, so as to realize the expansibility of the teaching system. The processing part of the platform mainly completes protocol data conversion, logic processing of specific mobile services, etc. Through the mobile terminal, users can selectively obtain rich online teaching resources according to their personalized learning needs. They can also conduct mutual learning among students by establishing mobile learning groups. At the same time, teachers and students can also conduct two-way communication through discussion areas and emails.Figure 7
Learning system architecture.


## 4.1.2. System Database Design

The system uses the MySQL database to store and manage data. The data table of each module includes the user information table, course information table, course evaluation table, course work table, and other main data tables.(1) User Information Form. User attribute information is stored in the user information table, and its design is shown in Table 1.Table 1
User information form.
Field nameData typeDescribeUser_idInt(11)User IDpasswordVarchar(32)User passwordusernameVarchar(60)User namegenderVarchar(20)User genderemailVarchar(50)User mailbox(2) Course Information Sheet. The course information table contains course attribute information required in the recommendation algorithm. It mainly records the detailed information of each subject course, including course description information, course difficulty level, course number of students, and other information. The design of the course information table is shown in Table 2.Table 2
Course information sheet.
Field nameData typeDescribeidInt(11)Course IDClass_idInt(11)Course classification IDnameVarchar(200)Course nameLearn_timesIntegerLearning timesstudentsIntegerNumber of studentsdescriptionVarchar(2000)Number of students(3) Course Evaluation Form. The course evaluation data table stores the user’s evaluation and rating data for the course. The design of the course evaluation data table is shown in Table 3.Table 3
Course evaluation form.
Field nameData typeDescribeidInt(20)Evaluation IDcidInt(11)Course IDuidInt(11)User IDcontentTextEvaluation contentgradeInt(4)ScorecreatetimeDatetimeEvaluation date(4) Course Work Sheet. The coursework data table stores the detailed information of the assignment, including the assignment topic and topic options. A course corresponds to multiple assignments. The design of the coursework data table is shown in Table 4.Table 4
Coursework schedule.
Field nameData typeDescribeCourse_idVarchar(20)Course IDUser_idVarchar(20)User IDExam_idVarcharJob IDtitleVarcharHomework topic


## 4.1.3. Implementation of Each Module of the System

(1) User Login Registration Module. The user login module is the basic link of course learning. New users should register first and fill in the user name, mobile phone number, password, and other information on the registration page according to the specifications. After they fill in the form, they click the register and log in button and the registration operation is completed after searching and saving in the database. If the database query finds that the mobile phone number already exists, it will prompt “The account already exists, please log in.” The user clicks the “Existing account, log in now” button in the lower left corner of the registration form or the login button at the top of the home page to enter the login interface.(2) Online Learning Modules. This module is the core module of the system. After logging in to the system, users can select the courses that they are interested in in the course list on the homepage to start the course study or continue to study courses on the course page of the personal center. After entering the course details page, users can view the course profile, teacher and institution profiles, and course details. They can choose to bookmark courses, start learning, etc. and click to start learning to enter the learning page.(3) Course Recommendation Module. After entering the system, users can view the recommended list of popular courses on the right side of the all courses page. The main information of the course is displayed in the recommendation list, including the course title, difficulty level, number of participants, total course duration, and other information. Users can select courses of interest by clicking on the title or course icon.(4) Background Management Module. The background management module of this system adopts dual-authority settings, including two roles as a teacher and administrator. Administrators can perform functions such as viewing and modifying student and teacher information, user permissions, and maintaining course information. To sum up, whether in the stability of the system processing or in the response speed, the system performs well, which proves that the performance of the system is relatively good.


## 4.2. System Performance Test Results and Analysis

This experiment mainly detects the nonfunctional aspects of the system through performance testing. This system is an online learning platform, and the number of users visiting the website is large. In order to verify that the system can run normally and stably in the case of multiple clients, the computing response time of the system is tested. Taking the course learning module as an example, 100 clients are simulated and each client sends an HTTP request at an interval of 1 second to perform a system performance test. The test results are shown in Figure8.Figure 8
Performance test graph.As can be seen in Figure8, the system’s requests per second and responses per second are almost the same. It proves that there is no error or timeout in the request processing of the system and the stability of the system is very good. Looking at the response time of the system, the response time per second of the system is within the interval of [0.8, 1.2]. And within one minute of the test, the average response time of the system was 0.978 s. This value is ideal, and it proves that the system also performs well in terms of running speed.


## 4.3. Student Satisfaction Questionnaire

In this experiment, 200 students and 20 teachers of physical education majors in 3 colleges and universities in a certain area were selected as the experimental objects. It allows them to experience the online learning system designed in this paper and then conducts a satisfaction survey of these students and teachers.


### 4.3.1. Questionnaire Design

There are two questions in this questionnaire, as follows:(1)
Do you think this learning system can improve your learning (teaching) efficiency(2)
Do you like the learning format based on this learning systemOf these two questions, the first question is to investigate student and teacher acceptance of the learning system. The second question was to explore how much students and teachers liked the learning system. The two questions explore students’ and teachers’ satisfaction with the learning system from two perspectives, respectively.


### 4.3.2. Recognition Survey

This experiment investigates whether students and teachers think that the new model course is helpful to their learning or teaching and the results are shown in Figure9.Figure 9
Recognition survey results.As can be seen in Figure9, for students, the number of students who choose to agree that the new model course can improve their learning efficiency is the largest, reaching 42%. The number of people who chose to strongly agree came second, reaching 39.5%, while the number of people who chose to disagree was only 3%. This proves that students have a high degree of recognition of the new model course. For teachers, those who choose to strongly agree that the new model curriculum can improve their teaching efficiency is the largest number, reaching 40%. The least number of people who choose to disagree is 10%. Therefore, teachers’ recognition of the learning system is also relatively high.


### 4.3.3. Likeness Survey

A survey was conducted on students’ and teachers’ liking for the learning system, and the results are shown in Figure10.Figure 10
Likeability survey results.It can be seen that among the students, the number of people who choose to like the online learning system is the largest, accounting for 37%. The number of people who choose not to like it only accounts for 5%, which is the least. Among teachers, the number of people who choose to like the online learning system very much is the largest, accounting for 35%, and the number of people who choose not to like it is also the least. To sum up, it can be seen that both students and teachers are satisfied with the learning system designed in this paper.It can be seen from the above recognition survey and favorite survey that both students and teachers are highly satisfied with the learning system in this paper and there is no significant difference between the satisfaction of teachers and students.


## 4.3.1. Questionnaire Design

There are two questions in this questionnaire, as follows:(1)
Do you think this learning system can improve your learning (teaching) efficiency(2)
Do you like the learning format based on this learning systemOf these two questions, the first question is to investigate student and teacher acceptance of the learning system. The second question was to explore how much students and teachers liked the learning system. The two questions explore students’ and teachers’ satisfaction with the learning system from two perspectives, respectively.


## 4.3.2. Recognition Survey

This experiment investigates whether students and teachers think that the new model course is helpful to their learning or teaching and the results are shown in Figure9.Figure 9
Recognition survey results.As can be seen in Figure9, for students, the number of students who choose to agree that the new model course can improve their learning efficiency is the largest, reaching 42%. The number of people who chose to strongly agree came second, reaching 39.5%, while the number of people who chose to disagree was only 3%. This proves that students have a high degree of recognition of the new model course. For teachers, those who choose to strongly agree that the new model curriculum can improve their teaching efficiency is the largest number, reaching 40%. The least number of people who choose to disagree is 10%. Therefore, teachers’ recognition of the learning system is also relatively high.


## 4.3.3. Likeness Survey

A survey was conducted on students’ and teachers’ liking for the learning system, and the results are shown in Figure10.Figure 10
Likeability survey results.It can be seen that among the students, the number of people who choose to like the online learning system is the largest, accounting for 37%. The number of people who choose not to like it only accounts for 5%, which is the least. Among teachers, the number of people who choose to like the online learning system very much is the largest, accounting for 35%, and the number of people who choose not to like it is also the least. To sum up, it can be seen that both students and teachers are satisfied with the learning system designed in this paper.It can be seen from the above recognition survey and favorite survey that both students and teachers are highly satisfied with the learning system in this paper and there is no significant difference between the satisfaction of teachers and students.


## 5. Discussion

With the deepening of the reform of the university education system, more and more attention has been paid to the quality of university education. Because education must adapt to the times, higher education must introduce new technologies to improve educational efficiency and improve educational quality. The interactive learning environment of the network platform helps to improve the desire to learn, rich video resources, and learning programs. It is a convenient information exchange tool and provides a good environment for coordinated learning and knowledge sharing. At the same time, the online education platform also provides functions such as Q&A, online communication, logging, evaluation, and testing. This provides powerful technical support for individual and cooperative learning. As a new technology, the BP neural network has opened up a new research method. It includes basic features such as pattern recognition, nonlinear classification, nonlinear mapping, learning classification, and real-time optimization. Therefore, it is necessary to design college online teaching based on the BP neural network.


## 6. Conclusion

This paper discusses the online learning system of college physical education teaching based on the BP neural network. This paper firstly introduces the background and research significance of online learning, BP neural network. Then, the structure and general algorithm of the BP neural network are introduced and the algorithm used by the system in this paper is proposed. After that, this paper designs the recommendation algorithm and function of the online learning system. This paper designs the general structure, functional modules, and data tables of the online learning system in the experimental part. And the performance of the system designed in this paper is tested, and it is found that the performance of the system is good. Finally, this paper does a questionnaire survey to the users of the learning system. It is found that both in the recognition and liking sections, the satisfaction of students and teachers is relatively high. The future work is to improve the online learning system, design more sections, and make the system more user-friendly through the design of recommendation algorithms.



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*Source: 4775906-2022-04-07.xml*