1. Introduction

As the degree of informatization of physical education continues to deepen, attention is gradually being paid to the assessment and evaluation of physical education. The article proposes an expanded physical education teacher assessment and evaluation system. Literature [1] applies the analytic hierarchy process to establish a system of the physical education curriculum evaluation system and conduct empirical analysis, constructing an evaluation index system composed of 5 one-level indicators and 13 second-level indicators. In this practical operation, we can find that the system can clearly reflect the learning situation of college students for PE course selection, and the model can also be empirically analyzed. The system has strong operability. In the literature [2], interviews, evaluations, and tests, it is believed that the index has high reliability, validity, and easy-to-use characteristics, and it provides a scientific basis for physical education teaching. Literature [3] explained the necessity of including physical education into the college entrance examination system from different aspects; the article analyzed the importance of physical education and believed that including sports into the college entrance examination is a plan proposed by the system of caring for students, and it can also improve the importance of physical education. Literature [4] established a new event physical education teaching and curriculum evaluation based on the principles and methods of sociological statistics; this paper applies the established curriculum evaluation system to offline teaching practice, evaluates this emerging event, and explores the necessity of building a classroom curriculum evaluation system. According to the research results, the evaluation method has a great promotion effect on the course teaching and has a broad prospect for development. Through investigation and statistics, a comprehensive analysis of the intelligence method, literature [5] established a comprehensive evaluation index system according to the ability, quality, and knowledge structure of physical education teachers. In order to accurately evaluate the performance of faculty and staff, an effective evaluation system is essential. The literature [6] is investigating the status quo of the evaluation of teachers of physical education departments in American universities and colleges for providing analysis for an effective evaluation system. The research results show that the experimental results prove that not only can provide accurate information but also explore how the respondents view the important types of information used in the current teacher evaluation system. Literature [7] combined fuzzy mathematics decision-making with the MBO performance evaluation system in management theory and constructed a reasonable model of the performance. Based on the design model, the article uses analysis and evaluation methods to comprehensively evaluate performance and provides a reference for teacher evaluation. Literature [8] studied the theoretical basis of the practical ability evaluation system of physical education students in normal colleges, the determination of evaluation weights, the formulation of evaluation standards, and the evaluation of practical ability. The research has a great role in promoting the establishment of the evaluation system of college students. General physical education evaluation is organized by the school’s leaders, ignoring the importance and particularity of physical education, and selecting methods similar to other courses for curriculum evaluation; literature [9] relies on students and colleagues, quantitative analysis combined with quantitative statistics. In order to objectively, comprehensively, and truly reflect the present situation and effect of physical education, a university should have a practical teaching quality evaluation system, which is the basis for guaranteeing teaching quality. It is particularly important to establish a successful teaching quality evaluation system; literature [10] uses the analytic hierarchy process to establish a physical education teacher’s teaching quality indicator system, and the distribution of weights can provide a scientific basis for constructing the quality evaluation system of college physical education teachers. The article analyzes many aspects of pedagogy [11], investigates a large amount of data, and analyzes the status quo of teaching evaluation in a university in Xi’an. Literature [12] constructed a teaching evaluation index system for the sports major; the author analyzed the education ratings of professionals in the education field and constructed a teaching evaluation index system for the sports industry. Literature [13] uses fuzzy mathematics to develop physical education teaching and evaluation of curriculum application. The educational quality evaluation of colleges and universities is a huge project. The system can directly reflect the teaching quality of schools and also provides reliable information for the Ministry of Education. Literature [14] proposes that education reform has triggered an extensive process of research, restoration, systematization, and reorganization of the education system and has resulted in profound changes in the conceptual field. Literature [15] is the basis for the systematic analysis of the connotation and characteristics of school sports policy execution above. The system includes individuals and organizations, which are divided into multiple indicators to meet the school’s teaching evaluation tasks. Different literatures are put forward to analyze different teaching classes. From the above literatures, different research points can be reflected to analyze the relevance of the index system structure. It also reflects the fairness and impartiality of the evaluation system and can provide practical guidance for different teaching applications.

2. Research Status

3. Method

3.2. Teaching Quality Evaluation Model

3.2.1. Comparison of Professional Modules

We use box plots to reflect the characteristics of the data distribution. There are six numerical features [21], as shown in Figure 1.

We can evaluate the situation of different samples by comparing $$ [22]; the calculation formula is

$$ (11)

If there is a big difference between the samples, you can use the formula:

$$ (12)

Use the concept of elasticity in economics to describe the changes in samples between different years. The general definition of elasticity can be expressed as the percentage of the rate of change of one variable (Y) (ΔY/Y) and the rate of change of another variable (X)(ΔX/X) mathematics. The expression is

$$ (13)

Divide the sample into N levels and assign different weights W_j [23], the calculation formula is

$$ (14)

Use a_i,j, b_i,j to represent the number of people in the j-th level in the i sample. The calculation formula is

$$ (15)

$$ (16)

$$ (17)

Use elasticity to reflect the degree of change of each sample in different periods [24].

$$ (18)

3.2.2. Module Comparison

The clustering treatment can better reflect the differences between groups and is a commonly used method for evaluating group characteristics [25]; the calculation formula is

$$ (19)

where i₁ = 1, 2, ⋯, k and j₁ = 1, 2, ⋯, s.

Evaluation index Α_i1 is:

$$ (20)

We can get a professional teaching quality evaluation model.

$$ (21)

Then, there is

$$ (22)

ε_i indicates the situation of different modules:

$$ (23)

In this which,

$$ (24)

$$ (25)

Δ_i can be used as the basis for evaluation:

$$ (26)

In this which:

$$ (27)

Finally got:

$$ (28)

4. Experimental Part

4.1. Experimental Comparison Results

Using the teaching evaluation system, we conducted a school year of teaching practice. We set up three variables of experimental class, reference class, and standard class to conduct comparative experiments. We conducted statistics from the results of the 2018-2019 school year physical education evaluation student evaluation survey results. In the article, the indexes of teaching attitude, teaching ability, teaching content, teaching method, and teaching effect are comprehensively applied, and the students’ application time, the number of items, and the intensity of exercise are compared. The paper is also divided into reference class, experimental class, and standard class for comparison. The results show that the application of these indicators plays an important role.

The specific situation is as follows. Table 1 is shown; the statistical diagram is shown in Figure 2.

1. Student evaluation of teaching survey result table.

Class	Attitude	Ability	Content	Method	Effect
Reference class	21.45	17.09	12.85	17.11	17.11
Experimental class	21.8	17.38	13.09	17.43	17.47
Standard class	20.7	17.11	12.5	17.08	17.01

From Figure 2, we can see that the students in our test are more satisfied with their teachers than the reference class and standard class. Most students give the system a higher evaluation; the system has adjusted and adjusted the previous teaching methods. Compared with the previous teaching method, students’ interest in learning has shown a substantial increase; we have conducted a survey of students’ weekly exercise. The specific situation is shown in Table 2, and the statistical chart is shown in Figure 3.

2. Students’ weekly exercise status survey form.

Class	Student exercise time (hour)	Number of student exercise items (a)	Student exercise intensity (level)
Reference class	6.95	8.12	4
Experimental class	4.8	6.5	3
Standard class	5.0	7.1	2

From Figure 3, we can see that the students’ exercise time and sports items have been increased by four, which helps the students to form the consciousness of physical exercise in the subconscious.

4.2. System Test

We tested the average response time and query time of the system and their success rate and set up three modes of intelligent optimization, custom development, and platform development for comparison experiments. The specific conditions are shown in Table 3.

3. Intelligent system test.

System	Number of requests	3000	3500	4000	4500	5000	5500	6000
Internet of Things system	Average response time	33	45	55	95	135	205	279
	Success rate (%)	100	100	100	100	100	100	100
Custom development model	Average response time	40	55	86	180	250	420	500
	Success rate (%)	100	99.6	99.5	99.4	99.3	98.4	99.3
Platform development model	Average response time	53	65	100	220	420	460	550
	Success rate (%)	100	98.6	98.5	98.4	98.3	98.2	98

From Figures 4 and 5, we can get that, compared with the other two modes. The system with intelligent optimization method has obvious advantages in average response time and query efficiency. In concurrent multitasking, the average response time will increase, but the success rate will decrease.

The test parameters of multitasking capability of Internet of Things system are shown in Table 4 and Figures 6 and 7.

4. Test parameters of Internet of Things system.

System	Number of requests	90	120	150	180	210	240	270
Internet of Things system	Average response time	85	290	350	450	600	800	920
	Success rate (%)	100	100	100	100	100	100	100
Custom development model	Average response time	105	300	430	550	750	1150	1500
	Success rate (%)	100	99.6	99.5	99.4	99.3	99.2	99.1
Platform development model	Average response time	160	260	500	600	900	1400	1700
	Success rate (%)	99.7	99.5	99.4	99.3	99.2	99.1	99.0

In the experimental part of the article, the system is tested. From the experimental results, the system with intelligent optimization scheme has lower response time and higher success rate. From the point of view of system complexity, the time complexity of intelligent optimization scheme is n∗log(n), while the other two schemes are n² and n³, which have higher time complexity. As far as the overall performance is concerned, the intelligent optimization system has higher performance and better complexity.

4.3. Extended Research Analysis

This experiment tested the students’ satisfaction with the evaluation system and their knowledge of actions after using the teaching evaluation system. Three variables were set up: the experimental group, the control group, and the basic group. The specific experimental conditions are shown in Figures 8 and 9.

From Figures 8 and 9, we can see that the students’ satisfaction with the three groups of variables has increased to a certain extent after using the system. Among them, the satisfaction of the experimental group has been greatly improved, and the dissatisfaction has been greatly improved. The experimental results show that the teaching evaluation system can improve students’ summative evaluation of conventional teaching methods, and they have great satisfaction with the evaluation system.

From Figures 10 and 11, we can see that there is no significant difference in motor cognition among the students in the first three groups without using the teaching evaluation system. After using the system, there is a significant difference in motor cognition. The situation in the experimental group is more significant.

5. Conclusion

In short, the evaluation of physical education is an important link in the process of school physical education and plays an important role in constructing a complete and effective teaching process. Whether the establishment of physical teaching principles is reasonable, whether students’ learning behaviors are correct, and whether the teaching effect is excellent must be confirmed by the evaluation of physical education teachers. To this end, teachers should actively study the evaluation strategy of physical education classroom teaching to give full play to the evaluation function of classroom teaching, mobilize the enthusiasm of students to participate in sports, to enhance the effectiveness and quality of school physical education, improve students’ physical fitness, and lay a solid foundation for students’ development. More attention should be given to classroom teaching evaluation to achieve a comprehensive evaluation of teachers and students, and on this basis, enrich teaching methods, and provide schools with better quality physical education classroom teaching.

Conflicts of Interest

The authors declared that they have no conflicts of interest regarding this work.