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.

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).

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):

The equation is:

Variants of nonlinear motion equations:

The solution is:

According to the relationship between R2 and R3:

Calculation:

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

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:

Expressed as follows:

For college PE, the optimal value P is:

The parameters ai and aj are:

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.

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.

In edge computing, it is assumed that the Tu unit can upload only one data. If its transmission power is expressed as p 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:

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

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 β j − 1 is completed. Therefore, t comp j ` can be expressed as the following recursive form:

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.

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.

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.

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.

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).

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.

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

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.

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.

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%.

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.

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.

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.

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.