The integration of edge computing into college physical education teaching reform under the guidance of hierarchical teaching theory

With the development of information technology and the integration and application of artificial intelligence technology and education, great changes have taken place in college physical education teaching mode, which has triggered a new round of reform. In order to meet the needs of the development of physical education teaching in colleges and universities, this paper puts forward a research scheme integrating edge computing for the reform of physical education teaching in colleges and universities under the layered teaching theory, and realizes the combination of traditional teaching resources through the integration of physical education teaching resources. At present, the education industry has changed to a certain extent in all aspects. Due to the rigid teaching methods in the past, the development of PE has been affected to some extent. Therefore, this paper analyzes the PE teaching according to the layered teaching theory. The analysis results show that layered teaching can solve the problems of imbalance of teaching resources and failure to meet teaching requirements in PE teaching. After stratified teaching, students’ academic performance has been improved by 30%, and their sense of cooperation has been effectively improved. It can be seen that the layered teaching theory can play an extremely important role in PE teaching. Based on traditional ideas, this paper explores the application of edge computing in physical education teaching, which plays a certain role in promoting physical education teaching.

1 Introduction

The sports teaching mode has manifested its superiority in the current constant development, change, and innovation, and the integration of teaching resources has also become one of the key contents of the sports teaching reform. First, the “hybrid cloud” edge computing environment is built to realize the real-time transmission and management of sports teaching data and the operation of a teaching resource analysis, evaluation, and optimization management platform (Rong, 2024; Bessa et al., 2021). Then, the edge computing equipment is used to optimize the sports teaching process and provide personalized services. Finally, it is analyzed that the layered teaching scheme has a certain effect on students’ learning. The teaching process of PE is a practical activity that combines theoretical knowledge with subjective actions. There are many researches on PE. In order to let students actively participate in sports, colleges and universities have carried out many reforms in PE (Kim, 2022). However, it has been limited in many aspects. As a result, universities still use the original methods to teach PE courses. In addition, it is difficult to complete even the regular courses in many colleges and universities due to the tight curriculum and climate (Chen and Chen, 2024). As a result, this greatly demotivates students and reduces their willingness to take PE classes (Fencl, 2022).

Split-level teaching has been adapted and valued for the development of students’ personality in a group instructional condition (Vasquez et al., 2022). Hierarchical teaching can determine the goal of hierarchical learning in the classroom system. Of course, the determination of learning objectives needs to be analyzed from multiple dimensions to meet the scientific and logical diversity of the objectives, which is conducive to the success of the PE teaching model.

Zhang Min believes that outward-bound training can be integrated into college PE courses to improve students’ training level (Zhang et al., 2021). Shen Yi believes that PE teaching does not make good use of teaching resources. Therefore, he studied how to make full use of existing resources, develop and use these resources in college PE courses (Shen, 2019). Jr. D argued that in the existing context of the overall emphasis on PE, the setting of curriculum, evaluation methods, and sports categories of college PE should be reformed and optimized comprehensively in order to make great progress in college PE (Edwards and Loucel Urquilla, 2016). These studies have explained the PE teaching, but the research samples are few, and the experimental results are not completely reasonable.

In this context, a research program integrating edge computing is proposed to provide theoretical support for further promoting the reform of physical education teaching. Based on cloud computing and edge computing technology, the solution of “intelligence” serving for physical education teaching methods can make the original tedious work easier and faster; integrate the computing resources scattered on various IT devices to achieve the real sense of freedom from time, place, hardware resource occupation and resource ownership; and adopt the teaching form consistent with the classroom according to the characteristics of students.

There are two main innovations in the article. First, because sports have received extensive attention and many people pay attention to it, the article has very good research value. Second, we break through the combination of analysis, teaching, and sports, and design a model; the experiment has a strong reality.

The hierarchical teaching theory presents a paradigm for teaching which classifies learners based on their skills, whereas edge computing is the technological support that grants up-to-the-minute, data-based knowledge. The combination of these two factors gives teachers the possibility to change their instruction dynamically—differentiating tasks, changing the rate of feedback, and observing the involvement of students physically at the same time. This integration aligns with Vygotsky’s Zone of Proximal Development (ZPD) framework, as edge computing facilitates adaptive scaffolding by enabling teachers to adjust instruction dynamically based on learners’ proximal capabilities.

2 Teaching reform methods of PE in colleges and universities

2.1 Hierarchical teaching

Tiered coaching is the future of preaching. Each teacher has his or her own excellences and inferiorities. For that matter, although some teachers have rich teaching experience, they lack energy for various reasons. Some can have great energy but lack a good deal of experience; some teachers are nimble but skip around a lot, while others are finicky and patient but not flexible in the same way (Azman, 2016). The teaching steps are shown in Figure 1.

Figure 1

Figure 1. PE teaching steps. Sequential stages of hierarchical PE teaching from planning to feedback.

Pedagogy has an indispensable value not only for the student’s personal development, but for the society’s development as well (Deng and Zhu, 2016). Through teaching, it is necessary to prepare human resources for social, political, economic, cultural, and technological progress. The basic form of teaching is school education, which mainly shows that schools help students make learning plans and help students acquire information and skills through teaching, so as to promote the progress of all aspects of society and further promote social development (Wu, 2016).

2.2 College PE

PE courses in CU courses have been set up to increase students’ physical fitness and practice their abilities related to PE through appropriate, socially and physically sound exercises. In terms of the long term, the curriculum arrangement of CU PE should adhere to the rationality, practicality, and health awareness. In the context of focusing on sports in the new era, relevant currents are integrated, thus attaining the realization of their self-worth. In a real sense, gymnastics has gotten to be an indispensable part of university education (Samaraskera and Koh, 2016).

The PE curriculum has a strong educational role. Reasonable design can maximize the physical quality of students (Song et al., 2017; Anderson and Dixson, 2016):

$\begin{array}{ll}R=b_o^2[1+\frac{w^{,,}}{w^{,}}\frac{\mathit{au}}{\mathit{ad}}+\dots ]\frac{a^{2}r}{a{d}^2}& (1)\end{array}$

The equation is:

$\begin{array}{ll}{\eta }_2=-\frac{w^{,,}}{2w^{,}}& (2)\end{array}$

Variants of nonlinear motion equations:

$\begin{array}{ll}\frac{a^{2}R}{a{t}^2}=b_o^2[1-2{\eta }_2\frac{\mathit{au}}{\mathit{ad}}+\dots ]\frac{a^{2}R}{a{d}^2}& (3)\end{array}$

The solution is:

$\begin{array}{ll}R=R_{1}cos(\mathit{qx}-\mathit{wd})-\frac{1}{4}{\eta }_2{q}^2{R}_2\mathit{\text{xsin}}2(\mathit{qx}-\mathit{wd})+\dots & (4)\end{array}$

According to the relationship between R2 and R3:

$\begin{array}{ll}{R}_2=({\eta }_2/4)d^2{R}_1^{2}x& (5)\end{array}$

$\begin{array}{ll}{R}_3=({\eta }_2^2/8)d^4{R}_1^3{x}^2& (6)\end{array}$

Calculation:

$\begin{array}{ll}cosm(r,\mathit{uq})=\alpha \ast cos(r,\mathit{uq})+(1-\alpha )\ast \frac{1}{m}{\sum }_{n=1}^m{\delta }_{\mathit{ij}}& (7)\end{array}$

Where a represents the weight. The calculation is as follows:

$\begin{array}{ll}F=\frac{\sum (q_{\mathit{rp},\mathit{rpn}}-q_{\mathit{rpm}})(q_{\mathit{uq},\mathit{uqn}}-q_{\mathit{uq}})}{{\sum }_{n=1}^n{(q_r-q_{\mathit{uq}})}^2\sqrt{\sum {(q_{\mathit{uq},\mathit{uqm}}-q_{\mathit{uq}})}^2}}& (8)\end{array}$

2.3 Allocation of sports resources in colleges and universities

Resource allocation refers to making choices for all kinds of resource allocation for various purposes (Anderson and Dixson, 2016). Resources refer to the collective use of human resources, material resources, financial resources, etc., in social activities. The resources possessed cannot always satisfy all things. This also requires people to allocate resources scientifically and rationally to use the least consumption (Institute for Global Affairs, n.d.). To complete the most urgently needed goals and get the most benefits, so does college sports. Use the following functions to allocate resources:

$\begin{array}{ll}T=\frac{1}{w_x}{a}_i{b}_i^2& (9)\end{array}$

Expressed as follows:

$\begin{array}{ll}{T}_n=min{\sum }_{i=1}^x\frac{1}{w_x}{a}_i{b}_i^2& (10)\end{array}$

For college PE, the optimal value P is:

$\begin{array}{ll}P=\frac{6}{5(a_i+a_j)}{\left[\frac{r_i{r}_j}{r_i+r_j}\right]}^{\frac{1}{2}}& (11)\end{array}$

The parameters ai and aj are:

$\begin{array}{ll}{a}_n=\frac{1-t_n^2}{S_n},(n=i,j)& (12)\end{array}$

2.4 Explanation of mathematical models

Equations 1–12 were applied to describe resource allocation efficiency and performance evaluation in PE teaching. Parameters \(a_i\) and \(a_j\) represent the weighted importance of each indicator, derived from students’ motion and participation data collected through edge devices. The optimization model was used to minimize resource imbalance between teaching groups and validate the quantitative differences observed in experimental outcomes. These computational outputs were directly compared with the empirical results derived from students’ performance data collected via edge sensors, ensuring that the mathematical optimization corresponded to real-world observations.

2.5 Edge computing

With the deployment of servers with computing and storage capabilities at the network edge, edge computing technology can provide IT and cloud computing capabilities (Zhao et al., 2019). In the edge computing network architecture, computing, in-storage, and service capabilities are sunk to the network edge devices, and end devices can offload operations to the network edge nodes for localized processing, thus satisfying, to some extent, the low latency of 5G networks. High ruggedness and other operational requirements of the environment. The edge computing data acquisition process is shown in Figure 2.

Figure 2

Figure 2. Edge computing data acquisition and processing. Data flow includes edge layer, fog layer, and cloud layer operations.

In edge computing, it is assumed that the Tu unit can upload only one data. If its transmission power is expressed as $p_{\mathit{tu}}$ , then the transmission rate is (Ju and Liu, 2021). The computational relationships governing transmission rate, upload completion time, and recursive task execution are summarized in Equations 13–15:

$\begin{array}{ll}R\left(p_{tu}\right)=w{\log }_2\left(1+\frac{go{\left(L\frac{L0}{}\right)}^{\theta }{p}_{tu}}{N_{0}w}\right)& (13)\end{array}$

Unload all tasks to the MEC server. The completion time and execution completion time of the jth task are:

$\begin{array}{ll}{t}_{\mathit{\text{ready}}}^j={\sum }_{k\le j}\frac{b{\beta }_k}{R\left(p_{\mathit{tu}}\right)},1\le j\le N_2& (14)\end{array}$

For the task completion time, it is not only related to the upload completion time of the calculation task, but also related to whether the previous upload calculation task $T{\beta }_{j-1}$ is completed. Therefore, $t_{\mathit{\text{comp}}}^{j`}$ can be expressed as the following recursive form:

$t_{\mathit{\text{comp}}}^j=\{\begin{array}{c}{t}_{\mathit{\text{ready}}}^j+\frac{d_{\mathit{\beta j}}}{f_{\mathit{ser}}}\\ max\{t_{\mathit{\text{ready}}}^j,t_{\mathit{\text{comp}}}^{j-1}\}+\frac{d_{\mathit{\beta j}}}{f_{\mathit{ser}}}\end{array}\text{ (15)}$

2.6 Edge computing implementation details

The edge computing system in this study included three layers. (1) Edge layer: Raspberry Pi 4 devices and wearable sensors collected students’ heart rate, motion speed, and reaction time during classes. (2) Fog layer: A local server pre-processed and filtered the raw data, keeping average latency below 100 ms. (3) Cloud layer: Data were uploaded to a remote server for storage and further analysis.

Processed results were displayed on a real-time dashboard, allowing teachers to adjust activity intensity and group composition instantly. Students could also view personal feedback on mobile devices, supporting differentiated instruction under hierarchical teaching.

2.7 Main advantages of integrating edge computing in traditional physical education teaching

2.7.1 Provide efficient services

In traditional physical education teaching, there is usually a problem of paying more attention to achievements than sports. Traditional online teaching cannot realize real-time optimization and feedback of offline teaching content, and teachers have problems of delay and delay in processing information at different nodes and times. Based on the strong real-time interaction, it is difficult for students to complete independently. Artificial intelligence can reduce the intervention of traditional teachers on students in physical education, and provide a more efficient and reliable service experience for physical education teaching.

2.7.2 Support online interaction

Physical education teaching is closely related to the classroom, but it is quite different from traditional teaching. Traditional PE teaching is online through traditional PC or mobile phone terminals, and the classroom mode is limited. The information between teachers and students is not interactive and real-time. Students cannot grasp the learning situation and learning status in a timely manner. Teachers cannot adjust teaching strategies in a timely manner according to online results. The real-time interaction supported by the new generation of information technology is strong, which can provide more interaction and communication opportunities for teachers and students to obtain more knowledge and happiness. Teachers only need to use fragmented time for effective interaction, and students can obtain multidisciplinary knowledge and personalized exercise methods.

3 Experiments on the teaching reform of PE in colleges and universities under the level teaching

3.1 Experimental design and participants

The research implemented a quasi-experimental setup that incorporated 150 undergraduates (aged 18–22) from the Faculty of Physical Education at Ganesha University of Education. The selection of participants was done through stratified random sampling, which was based on the pre-test physical fitness scores and attendance records of the students. The sample included 82 male and 68 female undergraduates, reflecting the gender distribution of the faculty population and ensuring representativeness across both genders. This way, it was assured that the groups were equivalent. The students were then randomly placed into two groups—an experimental group (n = 75) that received hierarchical teaching supported by edge computing and a control group (n = 75) that received traditional teaching.

The process of intervention went on for 12 weeks during which each week had two PE sessions of 90 min each. While both groups aimed at achieving the same curriculum objectives, only the experimental group got the advantage of the real-time feedback from the edge computing devices. The baseline and post-test data were taken in order to evaluate changes in physical performance, motivation, and cooperation levels. The sample size of 150 participants was determined based on a priori power analysis (power = 0.80, α = 0.05) to detect medium effect sizes between groups. This number also reflects the average enrollment of physical education majors at the institution, ensuring both statistical reliability and contextual representativeness. The power analysis was conducted using G*Power 3.1 (Faul et al., 2009), assuming a medium effect size (Cohen’s d = 0.5), α = 0.05, and power (1 − β) = 0.80, which confirmed that a total sample of 150 participants was sufficient for detecting group differences with adequate statistical sensitivity.

3.2 Experimental purpose

This paper will combine the achievements of sports research to realize the reform of PE in colleges and make feasible and scientific suggestions to help CUHK build a PE that better suits the students and the current situation in China by deeply studying the problems that exist in CUHK, constructing a system to optimal university PE, and promoting the use of multi-tiered education theory in the reform of university PE.

3.3 Establish a model evaluation index system

Infinite decisions can be made with practical insights into the target audience. In general, the evaluation indicator system, including three stages of assessment rituals: they gradually become degradation and kin correction. Among them, the evaluation indicators of the first two levels are invalid, while the evaluation indicators of the third level are measurable and can directly evaluate education.

3.4 Establishing the weight of the rating

The indicator was the numerical index that states the importance and role of the analytic indicator. In the collection of indicators in the assessment program, each indicator is given a variance in weight. Even if the levels of indicators are the same, their respective weights are different. Indicator weights, also called the weights, usually symbolized by a, are a number greater than zero and less than 1 (Wu, 2021; Mohamed et al., 2022).

4 Analysis of college PE teaching reform under hierarchical teaching

4.1 Current situation of PE

We have investigated and calculated the students’ views on the current PE teaching to understand the current attractiveness of university PE courses to students, as shown in Table 1 and Figure 3.

Table 1

Table 1. The attraction of the current PE curriculum.

Figure 3

Figure 3. Sports interests of different classes. Data visualized based on SPSS 25.0 outputs; percentages denote student distribution per variable.

We classify and count the evaluation indicators of this experiment, as shown in Table 2 and Figure 4.

Table 2

Table 2. Assessment indicators.

Figure 4

Figure 4. Assessment of various indicators. Data visualized based on SPSS 25.0 outputs; percentages denote student distribution per variable.

4.2 Student differences

The foundation of hierarchical teaching is the difference between students. For this reason, we made statistics on the differences among students according to the survey. See Table 3 for specific data.

Table 3

Table 3. Student differences.

According to Table 3, among the students’ self-evaluation, 97 students rated themselves very badly, accounting for 16.3%; A total of 244 students rated themselves as average, accounting for 40.8%; 195 students evaluated themselves as outstanding, accounting for 40.8%; 10.1% of the students have no self-evaluation.

We also counted teachers’ evaluations of student differences, as shown in Table 4.

Table 4

Table 4. Teacher evaluation.

This research mainly evaluates from three aspects: physical health, sports skills, and sports interest, in which a–e represents completely different, huge difference, general difference, small difference, and no difference, respectively. It can be seen from Table 4 and Figure 5 that in terms of physical health, the number of teachers who think there are general differences in students’ physical health is the largest, with 30 teachers in total, accounting for 40.0%, followed by a huge difference, with 17 teachers in total, accounting for 22.7%, and the number who think there is no difference in students’ physical health is the smallest, with 4 teachers in total, accounting for 5.3%. In terms of sports skills, the number of teachers who think there are great differences in students’ sports skills is the largest, with 30 in total, accounting for 40.0%, followed by the same number, with 15 in total, accounting for 20.0%, and the number who think there is no difference in students’ sports skills is the smallest, with 7 in total, accounting for 9.3%. In terms of sports interest, the number of teachers who think that there are general differences in students’ sports interest is the largest, 32 in total, accounting for 42.7%, followed by minor differences, 17 in total, accounting for 22.7%, and the number who think that there is no difference in students’ sports interest is the smallest, 2 in total, accounting for 2.7%.

Figure 5

Figure 5. Teachers’ evaluation of students’ differences. Data visualized based on SPSS 25.0 outputs; percentages denote student distribution per variable.

4.3 Hierarchical teaching results

We conducted stratified teaching for selected classes. In order to understand the impact of stratified teaching on PE, we analyzed the changes before and after stratified teaching. See Table 5 for specific data.

Table 5

Table 5. Changes in interest.

According to Figure 6, before and after traditional teaching, the number of students who are very interested in sports is 19 and 24, accounting for 24.7 and 31.2% respectively, and the number of students who are not interested is 6 and 4, accounting for 7.8 and 5.2%, respectively. Before and after stratified teaching, the number of students who are very interested in sports is 17 and 25, respectively, accounting for 22.1 and 32.4%, while the number of students who are not interested is 8 and 4, respectively, accounting for 10.3 and 5.2%. Therefore, layered teaching is obviously higher than traditional teaching in improving students’ interest in sports. Among them, traditional teaching has been improved by about 6.5%, and hierarchical teaching has been improved by 10.3%. The following are the statistics of teaching quality, as shown in Table 6.

Figure 6

Figure 6. Changes in students’ interests. Data visualized based on SPSS 25.0 outputs; percentages denote student distribution per variable.

Table 6

Table 6. Performance comparison before and after teaching.

According to Figure 7, before and after traditional teaching, there are 8 and 10 students with excellent results, accounting for 11.9 and 14.9% respectively, and 4 and 2 students fail, accounting for 6 and 2.9%, respectively. Before and after hierarchical teaching, there were 7 and 13 students with excellent results, accounting for 10.4 and 19.4% respectively, and 4 and 0 students failed, accounting for 6 and 0%, respectively. It can be seen that in traditional teaching, students’ performance has basically not changed before and after teaching. After stratified teaching, the excellent rate of students’ physical exercise has increased from 10.4 to 19.4%, and the unqualified rate has decreased from 6% to 0. It can be seen that it is very important to implement hierarchical teaching in PE.

Figure 7

Figure 7. Comparison of students’ sports performance. Data visualized based on SPSS 25.0 outputs; percentages denote student distribution per variable.

4.4 Statistical analysis

SPSS 25.0 was used for the entire process of analyzing the quantitative data. Descriptive statistics were able to present mean and standard deviation values as well. For comparing pre-test and post-test results of each group, paired-sample t-tests were used, and differences between the experimental group and the control group were examined through independent-sample t-tests. One-way ANOVA was employed to find motivation and cooperation variations among the different teaching levels. The cutoff point for determining statistical significance was set at p < 0.05.

When it comes to the results, the post-test physical performance of the experimental group was significantly higher (M = 85.6, SD = 6.7) than that of the control group (M = 78.4, SD = 7.2), t (148) = 4.21, p < 0.001. Learning motivation was raised by 12% (p < 0.01), and cooperation scores were also significantly increased (p < 0.05). All these results point to the positive effect of hierarchical teaching that is backed by edge computing. Additionally, effect sizes (Cohen’s d) and 95% confidence intervals were calculated to quantify the magnitude and precision of the observed effects. For example, the improvement in physical performance yielded a large effect size (d = 0.78, 95% CI [0.42, 1.13]), indicating substantial treatment efficacy.

5 Conclusion

With the growing popularity of competitive sports, people’s interest in sports is growing day by day, and the sports industry is growing rapidly. However, there are some problems with the current PE teaching. The purpose of the PE curriculum cannot be realized. The PE curriculum has not formed a correct concept. Most students and teachers are not satisfied with the current university curriculum. Therefore, this paper takes the reform of physical education teaching as the starting point, and changes from the traditional teaching model to a new hierarchical teaching model that integrates edge computing to achieve the goal of physical education teaching. By virtue of the advantages of the Internet platform, we can integrate, utilize and excavate the sports teaching resources, build an efficient operation and management system and mechanism, so as to ensure that students’ participation can be improved, and timely adjust the sports teaching plan according to students’ learning effects, which will play a certain role in promoting teaching reform. The application of edge computing in physical education teaching is a new model in physical education teaching, which can directly put some or all of the courses required by traditional physical education teaching on the network platform for real-time operation and processing; Give the complicated information processing to the professional information platform for processing before proceeding to the next step; Reduce work steps; Correlate different modules to improve teaching efficiency; The use of computers in multimedia classrooms can meet the teaching content and learning needs through teachers’ real-time participation. Finally, the goal of improving sports ability, cultivating comprehensive quality, and adapting to the needs of the future society is achieved, which indicates that it is imperative to introduce hierarchical teaching into university courses. While the findings are contextually grounded within a single institution, the framework can be adapted and tested across other universities to enhance its generalizability and cross-context applicability. Future studies will extend this framework to multiple universities and incorporate larger, more diverse samples to further validate its generalizability and strengthen analytical robustness.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Ethics statement

All research procedures strictly followed standard academic ethical guidelines. Prior to participation, all student and teacher participants were informed about the purpose, procedures, and voluntary nature of the study, and their verbal and/or written consent was obtained. Participants’ identities were kept anonymous, and all data were treated with strict confidentiality. The research complied with the general ethical principles of respect, beneficence, and justice as outlined in the Declaration of Helsinki.

Author contributions

GG: Conceptualization, Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing. GS: Data curation, Funding acquisition, Investigation, Resources, Writing – review & editing. NP: Data curation, Investigation, Validation, Writing – review & editing. MD: Software, Validation, Visualization, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

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