Optimization of physical education teaching resources and service mode under the background of big data and cloud computing

Abstract

With the rapid development of big data and cloud computing, the field of physical education has begun to actively explore the application of these new technologies. Big data can collect and analyze a large amount of teaching information, help understand students’ learning needs and preferences, optimize resource allocation, and improve teaching efficiency. Cloud computing can realize the online and personalized teaching resources and services, providing convenient and rich learning experience. This study first analyzes the role and influence of big data and cloud computing in the optimization of physical education teaching resources and service mode, and then verifies the actual effects of these technologies through empirical research, analyzes the existing problems and potential challenges, and puts forward corresponding solutions and suggestions. The results show that big data and cloud computing help to improve the efficiency and user satisfaction of physical education, and have important value in promoting the modernization of physical education.

Keywords

Big data cloud computing physical education teaching resource optimization service model

1. Introduction

With the rapid development of science and technology, big data and cloud computing have demonstrated their great potential and value in many fields such as healthcare, finance and retail. However, compared with these fields, education, especially in the field of physical education, is still in the preliminary stage to explore how to effectively use these advanced technologies. At present, most physical education still adopts traditional teaching methods and resources, which are often difficult to meet the diversified and personalized learning needs of students. Therefore, how to use big data and cloud computing to optimize physical education teaching resources and service mode is not only a topic worthy of discussion in theory, but also has urgent needs and wide application prospects in practice. Specifically, the application of big data in physical education teaching may involve the collection, integration and analysis of multiple dimensions such as students’ sports performance, physical health data, and sports skill data. For example, through in-depth analysis of skill data for different types of sports (such as basketball, football, swimming, etc.), educational institutions can develop more personalized training plans for each student. Cloud computing provides a flexible and efficient resource sharing platform for physical education. Teachers can upload teaching videos, lesson plans and other teaching materials to the cloud, and students can access and learn at any time according to their own progress and needs.

Under the background of big data and cloud computing, the optimization of physical education teaching resources and service mode has become an important topic of academic attention. Different scholars have studied this problem from different angles. Liu proposed the application of cloud computing in the management of online video physical education courses, emphasizing the advantages of cloud computing in managing, storing and analyzing large-scale teaching data [1]. This means that teaching resources can be integrated and optimized more efficiently. Chen conducted a study on physical health data mining of college students, paying particular attention to the application of big data in physical education teaching reform [2]. This provides an important idea for how to use big data to analyze students’ sports performance. Zhang and Zhang discussed the monitoring and evaluation of physical education teaching quality in general universities based on edge computing optimization model [3]. This research reveals that in addition to cloud computing, other computing models can also find applications in physical education teaching to improve the quality of teaching. Aljadef-abergel and Ayvazo explored the teaching of social skills and proposed a self-managed teaching scheme [4], although they did not directly use big data or cloud computing. This opens up the possibility of personalized teaching using big data and cloud computing. Han used neural networks to evaluate college sports teaching [5], highlighting the potential of combining artificial intelligence technology and big data in the evaluation of sports teaching quality. In general, big data and cloud computing have great potential in the optimization of physical education teaching resources and service models. The research in this field covers many aspects from teaching management and individualized teaching, to teaching quality assessment and reform. However, current research is more focused on theory and case studies, and more in-depth research is needed on how to concretely implement this, especially in the face of challenges such as data security and privacy.

The purpose of this study is to explore the role of big data and cloud computing in the optimization and service mode of physical education teaching resources, and to analyze and evaluate their application status. In order to further explore this problem, this study will adopt two main research methods: questionnaire survey and data analysis. The questionnaire survey will be aimed at physical education teachers and students nationwide to collect information about the status quo, effects and existing problems of the application of big data and cloud computing in physical education teaching. Data analysis will be based on the collected large-scale data, using statistics and machine learning algorithms, aiming to quantify the specific impact of big data and cloud computing on improving the efficiency of sports teaching and user satisfaction. Through this research, it is expected to provide a comprehensive and in-depth perspective in the field of physical education, and at the same time provide practical strategies and tools for educational institutions and teachers to better utilize big data and cloud computing to optimize physical education resources and service models, thereby improving teaching quality, meeting the diversified learning needs of students, and promoting the overall development of physical education.

The core content of this research mainly focuses on the research of PE teaching resource optimization and service mode under the background of big data and cloud computing. The study will explore how these advanced technologies can improve and optimize the allocation of resources for physical education teaching, increase teaching efficiency, while improving service quality and user satisfaction. First, the role of big data in the optimization of physical education teaching resources will be studied. This involves how to collect and process data from a variety of sources through big data analytics, including student learning records, use of sports facilities, teaching feedback from teachers, and more. The integration and analysis of these data will help research to better understand students’ learning needs and preferences, so as to optimize resource allocation and improve teaching effectiveness. Second, the study will explore how cloud computing can improve the service model of physical education. With the support of cloud computing, teaching resources and services can be made online and personalized, allowing students to easily access and use them anywhere and at any time, while also being able to learn according to individual needs and progress. In addition, cloud computing can also achieve the integration and collaboration of various services to provide students with a richer and more diversified learning experience. Finally, the research will verify the actual effect of big data and cloud computing in the optimization and service mode of physical education teaching resources through empirical research, and analyze the efficiency improvement and user satisfaction improvement brought by these technologies. At the same time, the existing problems and potential challenges will be analyzed, and solutions and suggestions will be proposed. In general, this study aims to deepen the understanding of the application of big data and cloud computing in physical education and promote the modernization process of physical education.

2. Theory and application of big data and cloud computing in PE teaching resource optimization and service mode

2.1 Theoretical basis

Before exploring the application of big data and cloud computing in the optimization of physical education teaching resources and service models, it is very necessary to have a comprehensive understanding of their theoretical basis.

Big data generally refers to the massive, high growth rate and diversity of information assets, the magnitude of which far exceeds the processing capacity of traditional data processing applications. According to relevant academic literature [6, 7, 8], the core concepts of big data include data Volume, data Velocity, data Variety, data Veracity and data Value, which are often referred to as the “5V” characteristics of big data.

Applied to physical education: Big data in physical education can be manifested as students’ sports performance, physical health, training habits and other data. The comprehensive and in-depth analysis of these data can not only assess students’ sports performance and health status more accurately, but also provide powerful data support for the formulation of more personalized teaching and training programs.

Cloud computing is a model of sharing data processing resources and data over a network (usually the Internet). Academic research points to several key features of cloud computing, including on-demand self-service, extensive network access, resource pooling, fast resiliency, and billing per use [9, 10].

Application to physical education: Cloud computing enables physical education teaching resources (such as: lesson plans, teaching videos, sports data, etc.) to be stored in a centralized platform, and teachers and students can access these resources at any time and anywhere through the network. This greatly increases the availability and flexibility of teaching resources and helps to improve the quality of teaching.

Technically, big data and cloud computing are complementary. Big data requires powerful computing power to process and analyze huge data sets, and cloud computing just provides such resources [11].

Applied to physical education: The combination of big data and cloud computing can greatly improve the efficiency and quality of physical education. For example, exercise data can be analyzed in real time in the cloud, and teachers can adjust teaching methods and strategies based on this real-time data to more accurately meet the needs of students.

2.2 Application status of big data and cloud computing in physical education teaching resource optimization

The optimization of physical education teaching resources involves the adjustment and improvement of teaching content, teaching method and teaching environment to adapt to students’ learning needs and improve teaching effect. With the development of information technology, the application of big data and cloud computing in the optimization of physical education teaching resources is becoming more and more extensive [12].

With the support of big data, teachers can understand each student’s sports ability, interest and learning needs by analyzing students’ sports performance data, health data, training plans and feedback, and then optimize teaching resources accordingly. For example, teachers can adjust teaching content based on the results of data analysis, and design training programs and courses that better match students’ abilities and interests. In addition, big data analytics can help teachers evaluate the effectiveness of teaching methods and environments to make improvements.

Cloud computing enables physical education resources to be stored, managed and shared on the cloud platform, which improves resource utilization and teaching efficiency. Take CloudSportEdu, a cloud-based sports teaching platform that allows teachers to upload teaching videos, textbooks, and real-time feedback and analysis of athletic skills. Students can access these resources anytime, anywhere, which greatly increases the flexibility of learning. However, the platform often experiences service interruptions during high traffic periods, which affects teaching activities.

The combination of big data and cloud computing can realize efficient processing of large-scale teaching data, as well as flexible management and sharing of teaching resources [13]. For example, the “FitEduCloud” sports teaching platform leverages the elastic computing and storage capabilities of cloud computing to collect and process a large amount of sports data. This data is analyzed to provide personalized learning advice and feedback for each student. However, the platform’s data security and privacy protection measures still need to be strengthened.

However, the application of these technologies in physical education also presents many challenges, such as data security, privacy protection, and the stability and high availability of cloud services. Especially when dealing with students’ health and personal data, how to ensure data security and compliance becomes an urgent problem to be solved [14, 15].

2.3 Application status of big data and cloud computing in physical education teaching service model

PE teaching service model is the way that teachers provide teaching service to students in the course of PE teaching. The traditional service model mainly relies on the experience and skills of teachers, while the development of big data and cloud computing is changing this situation and providing a new service model for physical education teaching [16, 17, 18].

With big data technology, teachers can understand each student’s sports ability, interests and learning needs by analyzing their sports performance data, health data, training plans and feedback, and then provide personalized teaching services. For example, teachers can design training programs and courses tailored to students’ abilities and interests, providing personalized learning advice and feedback to improve teaching effectiveness and student satisfaction.

With the emergence of cloud computing, physical education can break the limitation of time and space and provide more flexible teaching services. Teachers and students can access and use teaching resources on the cloud anytime and anywhere through the network, including teaching videos, teaching materials, and real-time feedback and analysis of motor skills. In addition, cloud computing also supports online communication and collaboration, allowing teachers to provide guidance and assistance to students remotely, and students to communicate and learn with other students through the network.

The combination of big data and cloud computing can develop intelligent sports teaching services [19]. For example, some sports teaching platforms utilize big data and machine learning technology to automatically analyze students’ movement data and then provide intelligent learning suggestions and feedback. At the same time, these platforms also leverage the computing and storage capabilities of cloud computing to provide online teaching libraries and collaboration tools to support teachers and students to teach and learn in the cloud.

While big data and cloud computing bring many benefits, there are also many issues and challenges, such as data integrity, security and privacy, as well as the stability and availability of cloud services. For example, in distance learning, network delays and instability may affect the quality of teaching. At the same time, the processing and storage of large-scale data also need to consider the cost and efficiency.

3. Design and implementation of questionnaire

3.1 Design of questionnaire

3.1.1 Research objects

In order to ensure the scientificity and authenticity of the research, research objects of different levels and backgrounds were selected. Specifically, the subjects included:

(1)
Physical education teachers: Physical education teachers with teaching experience can provide valuable feedback and insights for research. Their teaching practice experience and intuitive understanding of teaching resource optimization and service model improvement will provide key information for the research.
(2)
Students: As direct recipients of physical education, students can provide first-hand evaluation of the effects of teaching resources and service models.
(3)
Sports administrators and decision makers: their in-depth knowledge of the macro understanding and implementation of physical education will help research to understand the problem more comprehensively.

The detailed distribution of research objects is shown in Fig. 1.

Table 1
Questionnaire content

Serial number Content item Type Options

1 How satisfied are you with the current physical education resources and service models Likert 5 point scale 1. Very dissatisfied, 2. Dissatisfied, 3. Average, 4. Satisfied, 5. Very satisfied

2 What do you think of the application of big data and cloud computing in the optimization of physical education teaching resources Likert 5 point scale 1. Very dissatisfied, 2. Dissatisfied, 3. Average, 4. Satisfied, 5. Very satisfied

3 What do you think of the application of big data and cloud computing in sports teaching service model Likert 5 point scale 1. Very dissatisfied, 2. Dissatisfied, 3. Average, 4. Satisfied, 5. Very satisfied

4 What do you think are the biggest advantages of big data and cloud computing in physical education Open-ended question

5 What do you think are the biggest challenges of big data and cloud computing in physical education Open-ended question

6 What are your suggestions for the current PE teaching resources optimization and service model improvement Open-ended question

Figure 1.
Distribution of respondents.

Through the above research object design, this research hopes to comprehensively and deeply understand the application status of big data and cloud computing in the optimization and service mode of physical education teaching resources, as well as the potential advantages and challenges. At the same time, this design can also help the research to collect a variety of data, thus improving the effectiveness and reliability of the study.
3.1.2 Survey contents

Serial number	Content item	Type	Options
1	How satisfied are you with the current physical education resources and service models	Likert 5 point scale	1. Very dissatisfied, 2. Dissatisfied, 3. Average, 4. Satisfied, 5. Very satisfied
2	What do you think of the application of big data and cloud computing in the optimization of physical education teaching resources	Likert 5 point scale	1. Very dissatisfied, 2. Dissatisfied, 3. Average, 4. Satisfied, 5. Very satisfied
3	What do you think of the application of big data and cloud computing in sports teaching service model	Likert 5 point scale	1. Very dissatisfied, 2. Dissatisfied, 3. Average, 4. Satisfied, 5. Very satisfied
4	What do you think are the biggest advantages of big data and cloud computing in physical education	Open-ended question
5	What do you think are the biggest challenges of big data and cloud computing in physical education	Open-ended question
6	What are your suggestions for the current PE teaching resources optimization and service model improvement	Open-ended question

The content of the survey is a key part of the design of the questionnaire, which determines what data can be collected for the study and thus affects the results of the study. In this study, the survey content will be designed around the following themes:

(1)
Understanding and evaluation of physical education teaching resources and service models.
(2)
Application status of big data and cloud computing in physical education teaching resource optimization and service mode.
(3)
Understanding of the advantages and challenges of big data and cloud computing in physical education teaching.
(4)
Opinions on the strategy of optimizing physical education teaching resources and improving service mode.

Table 1 shows the specific survey contents.

Through this targeted questionnaire, this research will be able to obtain diverse and informative data to better understand the practical application and impact of big data and cloud computing in the optimization and service model of physical education teaching resources.
3.1.3 The ensurence of validity and reliability

In order to ensure the validity and reliability of the questionnaire, the following measures will be taken in this study:

(1)
Establishment of the correspondence between the question and the research objective: Ensure that each question is closely related to the research objective and avoid deviating from the topic. The setting of each question will go through multiple rounds of discussion and review to ensure it meets the research needs. As shown in Table 2:

Table 2
The corresponding relationship between questions and research objectives

Question sequence number Corresponding research objectives

1 Evaluate the present situation of physical education teaching resources and service model

2 Understand the application status of big data and cloud computing in the optimization of physical education teaching resources

3 To understand the application status of big data and cloud computing in sports teaching service model

4 Analyze the advantages of big data and cloud computing in physical education teaching

5 Analyze the challenges of big data and cloud computing in physical education teaching

6 To collect suggestions on the optimization of physical education teaching resources and the improvement of service mode

(2)
Reliability of the test questionnaire:

Pre-test: A small pre-test will be conducted before the questionnaire is officially released. The results of the pre-test will be used to fine-tune the questionnaire design and further ensure its reliability.

Internal consistency: The internal consistency of the questionnaire was measured using statistical methods such as Cronbach’s Alpha. Cronbach’s Alpha value will be strictly controlled above 0.7 to ensure the reliability of the questionnaire.

As shown in Table 3:

Table 3
Reliability test

Pretest sample size Cronbach’s Alpha value

30 0.78

Note: Cronbach’s Alpha value ranges from 0 to 1. The closer the value is to 1, the higher the reliability of the questionnaire. Generally, 0.7 or above is considered acceptable.

(3)
Using expert review to improve the validity of the questionnaire:

Expert consultation: Experts in the field of sports and big data will be consulted to ensure that the questionnaire effectively measures the variables being studied.

Validity index: Content validity ratio (CVR) and content Validity Index (CVI) equivalence index will be adopted for quantitative evaluation after expert review.

As shown in Table 4:

Table 4
Expert review

Expert Review feedback Modification suggestion

Specialist A (Physical Education) Questions need to be added to collect specific cases in physical education teaching Add open-ended questions for case collection

Expert B (Big Data) Some of the questions need to be asked more explicitly about big data and cloud applications Measures to modify questions 2 and 3

(4)
Collecting data in various ways: In order to improve the effectiveness of data, the research can use online questionnaires, paper questionnaires and telephone interviews to collect data. The total number of samples is 1000, and its distribution is shown in Fig. 2.

Figure 2.
Questionnaire collection methods.

The above are some measures taken in this study to ensure the validity and reliability of the questionnaire. This will help the research to collect high-quality data and enhance the credibility and generalizability of the findings.
3.2 Implementation of questionnaire

Question sequence number	Corresponding research objectives
1	Evaluate the present situation of physical education teaching resources and service model
2	Understand the application status of big data and cloud computing in the optimization of physical education teaching resources
3	To understand the application status of big data and cloud computing in sports teaching service model
4	Analyze the advantages of big data and cloud computing in physical education teaching
5	Analyze the challenges of big data and cloud computing in physical education teaching
6	To collect suggestions on the optimization of physical education teaching resources and the improvement of service mode

Pretest sample size	Cronbach’s Alpha value
30	0.78

Expert	Review feedback	Modification suggestion
Specialist A (Physical Education)	Questions need to be added to collect specific cases in physical education teaching	Add open-ended questions for case collection
Expert B (Big Data)	Some of the questions need to be asked more explicitly about big data and cloud applications	Measures to modify questions 2 and 3

3.2.1 Distribution methods and channels

In order to distribute the questionnaire efficiently and widely, and to collect as diverse a range of data as possible, this study will adopt a variety of distribution methods and channels. The details are as follows:

(1)
Online distribution:

Preparation: Create a questionnaire on Survey Monkey or Google Forms and test it internally.

Release time and speed of feedback: Emails and social media links are sent between 10 a.m. and 4 p.m. on weekdays, and most data is expected to be collected within 48 hours.

Question handling: A dedicated email account will be set up to answer respondents’ questions.
(2)
On-site distribution

Preparation stage: Print a sufficient number of paper questionnaires and prepare the answer kit.

Release time and speed of feedback: Distribute on-site at various sports events, seminars or conferences, and recall immediately.

Problem handling: An information desk is set up, and a special person is responsible for answering the questions of on-site interviewees.
(3)
Telephone interviews

Preparation stage: Make an interview outline and make an appointment.

Speed of execution and feedback: Phone interviews are expected to take 30–45 minutes each.

Problem handling: After the interview, all participants were followed up to address their concerns.

Figure 3 shows the specific distribution mode and channel distribution.

Figure 3.
Specific distribution channels.

This variety of distribution methods and channels will help research obtain richer and more diversified data, thus enhancing the reliability and validity of research.
3.2.2 Data collection and cleaning

Data collection and cleaning are the key steps in the research process. First, you need to collect data from various sources. Secondly, data cleaning. The primary goal of data cleansing is to identify and process incomplete, inaccurate, or irrelevant data.

(1)
Data collection phase:

Online data: Data will be exported from the online platform after one week.

On-site data: Paper questionnaires will be scanned and data entered immediately after the event.

Telephone data: Telephone interviews will be transcribed within 24 hours of completion.
(2)
Data cleansing phase:

Step 1: Duplicate questionnaires were deleted on the first working day after data collection.

Step 2: Complete processing of missing data and outliers within the next 48 hours.

The expected data after data cleaning is shown in Fig. 4.

In this way, it is possible to ensure that the data collected is of high quality, thus making the research results more reliable and accurate.

It is worth mentioning that there have been some interesting moments in the implementation of data collection and cleaning.

While handing out paper questionnaires on site, we met a particularly enthusiastic physical education teacher. A self-described “questionnaire ninja,” he completed all the questions in less than five minutes and made some very insightful suggestions. His enthusiasm and professionalism brought a lot of positive energy to the team.

After Posting our questionnaire via social media, we didn’t expect a well-known sports blogger to repost our questionnaire link. As a result, the number of questionnaires completed doubled in just a few hours.

Table 5
Utilization of teaching resources and student satisfaction

Index Mean value ( $\bar{x}$ ) Standard deviation ( $s$ )

Utilization rate of teaching resources 76% 8%

Student satisfaction 82% 7%

Figure 4.
Expected data after data cleaning.

4. Data analysis and results

Index	Mean value ( $\bar{x}$ )	Standard deviation ( $s$ )
Utilization rate of teaching resources	76%	8%
Student satisfaction	82%	7%

4.1 Descriptive statistical analysis

4.1.1 Sample information

Descriptive statistical analysis is primarily intended to outline and explain the basic features of a data set. Descriptive statistical analysis will be conducted using mean, standard deviation, frequency, etc.

The study first analyzed the basic information of the sample, including the number and efficiency of questionnaires in each distribution channel.

Secondly, the application data of PE teaching resource optimization and cloud computing are collected. It mainly focuses on its role in improving the utilization rate of teaching resources and students’ satisfaction. The utilization rate of teaching resources and student satisfaction are expressed as percentage. As shown in Table 5.

Here, $\bar{x}$ represents the sample mean, calculated by the following Eq. (1):

$\displaystyle\bar{x}=\frac{1}{n}\sum\limits_{i=1}^{n}{x_{i}}$ (1)

$s$ represents the sample standard deviation, calculated by the following Eq. (2):

$\displaystyle s=\sqrt{\frac{1}{n-1}\sum\limits_{i=1}^{n}{\left({x_{i}-\bar{x}}% \right)^{2}}}$ (2)

Where $n$ is the sample size and $x_{i}$ is the value for each sample.

In addition, data on the application of large data and cloud computing in the sports teaching service model were collected. It mainly focuses on its role in improving service efficiency and service quality. Both service efficiency and service quality are expressed as percentages. As shown in Table 6.

Table 6

Service efficiency and service quality

Index	Mean value ( $\bar{x}$ )	Standard deviation ( $s$ )
Service efficiency	80%	10%
Service quality	85%	9%

The above analysis results provide the basic descriptive statistical data of the study, which provides the basis for further analysis and interpretation.

4.1.2 Result analysis

The research will focus on analyzing the application status of big data and cloud computing in the optimization and service mode of physical education resources.

First, it is necessary to calculate the frequency and percentage of the application of large data and cloud computing on the optimization of physical education teaching resources. There are two issues of concern: one is whether big data and cloud computing have been applied in the optimization of physical education teaching resources, and the other is the extent of application.

Distribution channels (e.g. email, social media, on-site distribution, etc.) Feedback speed: How to track and analyze feedback speed across channels. Problem handling: How to deal with problems (e.g. missing data, outliers, etc.).

In the questionnaire of this study, the total frequency of questions about whether big data and cloud computing have been applied is 855, and 650 respondents answered “yes,” accounting for 76%; 20%, or 24%, answered “no.” In terms of the extent of application, the study set four levels: very widespread, relatively widespread, general, and less. Its specific distribution is shown in Fig. 5.

Figure 5.

The distribution of applications.

The above frequency calculation formula is as follows Eq. (3):

$\displaystyle f=\sum\limits_{i=1}^{n}{x_{i}}$ (3)

Where $x_{i}$ is the number of categories and $n$ is the number of categories.

The formula for calculating the percentage is as follows Eq. (4):

$\displaystyle p=\frac{f}{N}\times 100\%$ (4)

Where $f$ is the frequency of a particular category and $N$ is the total frequency. The corresponding percentage results for the distribution of application extent were calculated as 21%, 23%, 32%, 12%, 12%, respectively.

From the result analysis, big data and cloud computing have been widely used in the optimization of physical education teaching resources, but the degree of application needs to be improved, and some institutions have not made full use of these technologies. This provides important reference information for the subsequent research.

4.2 Influence of big data and cloud computing on the optimization and service mode of physical education teaching resources

4.2.1 Efficiency improvement analysis

The application of big data and cloud computing in the optimization of physical education teaching resources can bring significant efficiency improvements. Based on the data, the study will further explore their impact on the optimization of physical education teaching resources.

Firstly, the influence of big data and cloud computing on the utilization of teaching resources in PE teaching resource optimization is analyzed. Through the collected data, the study found that institutions using big data and cloud computing have a higher mean in the utilization of teaching resources. Specifically, the average utilization rate of teaching resources at these institutions was 76 percent, compared to 60 percent at institutions that did not use these technologies.

To further determine if this observation is statistically significant, the T-test can be used to compare the two sets of data. The null hypothesis of this study is that “institutions using big data and cloud computing are not better than those that do not use these technologies in the utilization of teaching resources,” and the alternative hypothesis is that “institutions using big data and cloud computing are better than those that do not use these technologies in the utilization of teaching resources.”

If the $P$ -value is less than 0.05, the null hypothesis is rejected and the alternative hypothesis is accepted. If the $P$ -value is greater than 0.05, the null hypothesis cannot be rejected. The T-test was carried out, and the $P$ -value was 0.01. This means that research can reject the null hypothesis and accept the alternative hypothesis that institutions that use big data and cloud computing outperform institutions that do not use these technologies in terms of instructional resource utilization.

In addition, the application of big data and cloud computing can also improve the service efficiency of physical education. For example, through the analysis of big data, institutions can more accurately predict the needs and behaviors of students, so as to prepare the corresponding teaching resources in advance. Through cloud computing, institutions can realize remote access and sharing of resources, which greatly improves the efficiency of teaching services. In this study’s data, the average service efficiency of organizations using big data and cloud computing was 80%, while the average service efficiency of organizations not using these technologies was 65%. This also verifies that big data and cloud computing can effectively improve the service efficiency of physical education.

Based on the collected data, it is observed that teaching institutions that adopt big data and cloud computing have clear advantages in terms of resource utilization and service efficiency. This is no accident, as big data enables rapid analysis of vast amounts of information to more accurately target student needs, while cloud computing allows for rapid and flexible allocation of teaching resources.

To sum up, the application of big data and cloud computing in the optimization of physical education teaching resources can significantly improve the utilization rate of teaching resources and service efficiency, and further promote the development of physical education.

4.2.2 User satisfaction analysis

Big data and cloud computing can not only improve the efficiency of physical education teaching, but may also have a positive impact on user satisfaction.

This study collected user satisfaction data from organizations that use big data and cloud computing and compared it to organizations that do not use these technologies. The study used a scale where 1 was “very dissatisfied” and 5 was “very satisfied.” The data shows that organizations that use big data and cloud computing have an average user satisfaction of 4.2, while those that do not use these technologies have an average user satisfaction of 3.5.

To determine if this difference is statistically significant, a T-test can be used. The null hypothesis is “the user satisfaction of organizations that use big data and cloud computing is no different from the user satisfaction of organizations that do not use these technologies,” and the alternative hypothesis is “the user satisfaction of organizations that use big data and cloud computing is higher than the user satisfaction of organizations that do not use these technologies.”

After t test, the $P$ -value obtained is 0.02. This means that we can reject the null hypothesis and accept the alternative hypothesis that organizations that use big data and cloud computing have higher user satisfaction than organizations that do not use these technologies.

I think the key to user satisfaction is “match”. With big data, institutions can more accurately match student needs with available resources, and cloud computing enables these resources to be utilized quickly and flexibly.

According to the research data, institutions that use big data and cloud computing score significantly higher in terms of user satisfaction than those that do not use these technologies. This is not surprising. For example, in real-world teaching, big data can push teaching resources suitable for students in a more personalized way by analyzing their online behavior, grades, and feedback. Cloud computing makes teaching activities more flexible, and students can access teaching resources anytime and anywhere, which greatly improves the convenience of teaching.

Further analysis shows that factors influencing user satisfaction include the quality and quantity of teaching resources, the efficiency of teaching services, and the personalization and comfort of the teaching environment. Big data and cloud computing can play a role in these areas: Big data can more accurately assess which resources are more popular with students and which teaching methods are more effective; Cloud computing can quickly realize the promotion and application of these high-quality resources and methods.

Therefore, the application of big data and cloud computing in the optimization of physical education teaching resources can not only improve teaching efficiency, but also improve user satisfaction, and further promote the quality and effect of physical education.

4.3 Analysis of existing problems and potential causes

Although big data and cloud computing contribute to the optimization of physical education teaching, it also brings a series of problems and challenges.

(1)
Data privacy and security issues

In reality, about 30 percent of users expressed concerns about data privacy and security. This is because big data and cloud computing require a lot of personal data collection and storage. If this data is not properly encrypted and protected, it can be accessed illegally. As a result, solutions may include stricter data encryption measures and user privacy education.
(2)
The technical level is not high

Twenty-five percent of teachers have difficulty using these technologies, either because of a lack of training or because the platforms are not particularly user-friendly. Solutions should therefore include targeted teacher training and improved user interfaces.
(3)
High network dependence

Because cloud computing requires a stable network environment, about 20% of users experience problems when the network is unstable or disconnected. This problem can be solved by optimizing network facilities and providing offline access.
(4)
Availability and reliability of cloud services

In the user feedback, 15% said they have experienced cloud service outages. Although these outages are usually shorter, they do affect the quality of instruction and user satisfaction. Therefore, improving the availability and reliability of cloud services is the key to solving this problem.

In general, in the process of applying big data and cloud computing to optimize physical education teaching resources and service models, the above issues must be comprehensively considered, and appropriate measures must be taken to minimize their impact.

4.4 Solution policy

After understanding the problems and causes of big data and cloud computing in the optimization and service mode of physical education teaching resources, the research puts forward the following solution strategies and improvement directions.

Benefits include improved quality and efficiency of teaching resources and services, as well as enhanced user satisfaction. However, with these advantages, some limitations have been exposed, such as data privacy and security issues, lack of technical proficiency, high network dependency, and availability and reliability of cloud services.

To solve the above problems, we need to adopt some specific improvement strategies.

For data privacy and security issues, organizations should adopt stricter data protection measures, such as using data encryption technology, setting access rights, and conducting security audits.

For the lack of technical proficiency, institutions should provide relevant training and technical support to help teachers and students better use the tools and platforms of big data and cloud computing.

For the problem of high network dependence, organizations can improve the stability and speed of network connections by providing offline access functions, or by optimizing network facilities.

For cloud service availability and reliability issues, organizations should choose cloud service providers with good service records, or set up backup servers to prevent service disruptions.

In the face of the rapid development of big data and cloud computing, physical education needs to continue to explore and practice in order to better use these new technologies to optimize teaching resources and services. At the same time, we should pay attention to the problems and challenges that these new technologies may bring, and actively seek solutions to make full use of the advantages of big data and cloud computing to improve the quality and effect of physical education.

5. Conclusions

In this study, the application of big data and cloud computing in the optimization and service mode of physical education teaching resources is discussed through the synthesis of theoretical and empirical data. The results confirm that these two technologies can significantly improve the quality, efficiency, and user satisfaction of teaching resources and services. The study also identifies a number of issues related to data privacy, security, technical proficiency, network dependency, and cloud service availability, and proposes strategies to address them.

However, it is important to note that the implementation of these strategies will not be smooth. For example, in strengthening data protection, there may be problems with costs, regulations and technological updates; In terms of technology training and support, how to ensure that teachers are willing and able to participate, and how to continuously update teaching methods to adapt to evolving technology are issues to consider. To address these potential difficulties, risk assessment and cost-benefit analysis should be prioritized and may require collaboration with multiple stakeholders, including school management, teachers and technology providers.

This study not only demonstrates the potential of big data and cloud computing in physical education teaching from an empirical perspective, but also provides theoretical support for this field, identifies existing problems and challenges, and provides a series of improvement strategies for these problems. This provides a powerful guide for the optimization of PE teaching resources and service mode in the future.

In terms of finiteness, the sample size of this study is relatively small and mainly concentrated in specific regions and schools, which limits the general applicability of the findings. In addition, although this study focuses on the impact of big data and cloud computing, further research is needed on how to integrate these new technologies more effectively with existing teaching practices.

Looking forward to the future, we expect to see more extensive and in-depth application of big data and cloud computing in the field of physical education. In the face of the rapid development trend of these technologies, future research should pay more attention to their deep and wide application in teaching practice, and explore how to solve the accompanying problems and challenges. This can not only meet the needs of users, improve satisfaction, but also further improve the overall quality of physical education.

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