# Construction of Evaluation Index System of Physical Education Teaching Environment in Colleges and Universities Based on 5G Technology

Authors: Wen Tian; Yu Tie; Ruiting Wang; Lu Zhou
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

A top notch actual training climate has an indispensable main impetus for the physical and mental improvement of youths and kids and the nature of actual instruction. This paper proposes an assessment file arrangement of actual instruction showing climate in schools and colleges in light of 5G innovation, planning to fabricate an assessment list arrangement of actual training educating in the climate of 5G data innovation in the coordination stage. The technique for this paper is to concentrate on the organization energy effectiveness model, layout a framework model and 5G list demonstrating, and afterward propose a showing climate assessment review and examination strategy. The job of these strategies is to plan sensible assessment norms based on hypothetical examination. It utilizes 5G data innovation and organization effectiveness and lastly constructs a school sports ecological assessment framework. Through the poll overview, this paper comprehends what is going on of actual schooling showing climate in Chinese universities and colleges and tracks down the purposes behind its disparities and issues. This paper develops actual training climate assessment file framework, researches the game climate assessment and actual exercise direction, and afterward proposes actual instruction and assessment framework under 5G innovation. The outcomes show that in the assessment list arrangement of actual instruction showing climate in schools and colleges, the heaviness of material climate is 0.6796, demonstrating that the most dire errand for schools and colleges to further develop the actual training climate is to work on the actual climate.


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## Body

## 1. Introduction

The educational environment is an important factor that educational activities must rely on and cannot be separated from. It is contrasted by different disciplines; the nature of college sports climate straightforwardly influences the improvement of instructive exercises. The hypothetical development of the assessment file arrangement of sports climate in schools and colleges offers a specific hypothetical help for the objective assessment of sports climate. It provides a reference value for regulating and optimizing the college sports environment and fully demonstrates the maximum function of the college sports environment.This paper will examine and concentrate on the possibility, idea, capacity, piece, and effect on the actual schooling system of 5G innovation, with an attention on the public actual instruction climate in colleges. All instructive exercises should be done under explicit existence conditions. This unique transient and spatial condition is the instructive climate. As a special educational environment, the public sports environment in colleges and universities is an important part of the sports system. The nature of actual instruction climate affects the viability of instructive exercises. Through this examination, this paper will layout the assessment record framework and assessment standard arrangement of the public actual schooling climate in schools and colleges. The assessment completely mirrors the uplifting outlook of instruction, which gives a reference to the public actual schooling in universities and colleges. This paper attempts to continuously improve the quality of public sports teaching in colleges and universities through evaluation, promote the construction of public sports environment in colleges and universities, blind spots, and avoid randomness. For colleges chasing or rehearsing the reconciliation of 5G data innovation and instructive climate, the rating scale has significant functional directing importance. Obviously, this scale can likewise be utilized to quantify the capacity of schools and colleges to utilize 5G innovation, which has a particular position of authority for instructors to utilize data innovation.According to the viewpoint of 5G innovation, this paper has recommended that the public actual instruction climate in schools and colleges is one of the significant elements influencing the nature of public actual training educating in universities and colleges. Also, it laid out a useful assessment framework by joining subjective and quantitative techniques. This paper plans the ecological standard arrangement of public actual instruction in schools and colleges. As a kind of perspective for public actual instruction in schools and colleges, it advances the development of public actual training climate in schools and colleges and the further improvement of schooling level.


## 2. Related Work

Through careful analysis of the sample literature, in the 1990s, the research on the teaching environment began to have a theoretical construction and gradually made breakthroughs with different disciplines such as English and mathematics as the entry point. This stage developed relatively quickly, and a series of theoretical research results appeared one after another. Hsu et al. present a small-scale double-band bandpass channel with interlocking ventured impedance resonators (SIRs). The channel is expected to have two simultaneous passbands covering six assigned recurrence groups in the sub-6 GHz range for fifth-age (5G) new radio access advances. This channel can handle the impedance proportion of the interlocked SIR to have two passbands at the ideal recurrence [1]. Charles et al.’s consolidation of fifth-age (5G) organizations into the brilliant network will make new “edge” and “haze” innovation plans of action in utilities, while empowering savvy control and mechanization. Nonetheless, they argue that the increased implementation of WSNs also introduces security barriers, especially related to confidentiality, accessibility, and security management, resulting in unpredictable expenditures and disasters for utilities and customers [2]. Moradi et al. recommend that 5G will bring heterogeneous parcel sizes and traffic types, as well as a developing requirement for energy proficiency. They utilize channel expectation to foster number programming models. They want to limit the energy use of the client gadget while real-time video and set out expanded rest open doors while forestalling cushion subcurrent [3]. The point of García-Rico et al. was to get the commitment of service learning (SL) to expanding mindfulness and awareness of the sustainable development goals (SDGs) in Physical Education Teacher Education (PETE) programs. Their respondents were 81 advanced education level understudies from the PETE program. Their fundamental discoveries show that economical showing techniques can advance and improve how understudies might interpret information [4]. Choi et al. incorporate actual schooling into a college setting and look at what actual training educational program content and educator connection mean for undergrads’ active work levels. They ran a 10-week actual instruction season at a college. In their trials, they utilized the immediate time inspecting device “Wellness Guidance Time Observation System” to gather and examine information on active work level, course satisfied, and educator conduct [5]. Xu comprehensively combed the impact of physical education on students. He believes that daily life-oriented physical education goals can change students’ lifestyles, meet their individual needs, and stimulate their creativity. He puts forward targeted physical education strategies based on front-line teaching experience. This strategy provides a reference for front-line PE workers [6]. Xiong et al. accept that the unequal allotment of educator assets is the fundamental variable prompting the disparity of K-12 schooling in China. The current techniques to take care of this issue are more conventional, like live instructing and recorded addresses by excellent educators. They proposed the utilization of ICT to take care of this issue. They proposed to assemble a particular ongoing video conferencing climate (RVCE) to help distance educating [7].


## 3. Construction Method of Teaching Environment Evaluation Index System

### 3.1. Network Energy Efficiency Model

There are two standard portable remote organization energy productivity assessment strategies in the business. One is the energy consumption per unit area, which is defined as the total power consumption of the mobile wireless network divided by the total area (w/m2). The area is usually constant. This approach of focusing on the total energy consumption of the network helps environmental groups measure carbon dioxide emissions in the region. Another indicator is the energy consumption per bit, which is defined as the system data transfer rate divided by the total energy consumption, and the unit is bit/S.w or bit/J for short. This method of comprehensively considering network performance and system energy consumption has been adopted by the Japanese 5G promotion group and is defined as the main energy efficiency indicator. This paper also adopts this method as the basic energy efficiency evaluation method, such as the following equation:
(1)EE=TP,where P is the energy consumption of the target system and T is the capacity of the target system [8]. For a homogeneous network scenario with only macro base stations, the EEnet statistical formula for network energy efficiency is as follows:
(2)EEnet=∑i=1NMTiM∑i=1NMPiM,where NM is the number of macro stations, TiM and PiM are the throughput and power consumption of the ith macro station [9]. The parameters of the energy consumption model of each low-power node need to be considered separately; furthermore, the energy proficiency of the heterogeneous organization is displayed in the following formula:
(3)EEnet=∑i=1NMTiM+∑j=1NrTjP∑i=1NMPiM+∑j=1NrTjP.A base station generally includes multiple sets of transceivers, each of which includes a power amplifier unit PA, a radio frequency unit RF, a baseband unit BB, a DC-DC converter, cooling equipment, and a power supply trunk. Based on Earth’s internal investigation and Sandvine report, the energy consumption components PA, RF, BB, DC, CO, MS, and total energy consumption of macro and pico as a function of load are shown in Figure1. The load is expressed as Pout/Pmax.Figure 1
Changes in energy consumption composition of pico base stations with load.The black circle in the figure represents the energy consumption of the base station when it is sleeping. It tends to be seen that the RF yieldPout has a direct relationship with the energy utilization Pin of the base station, and the energy utilization of the full-scale base station vacillates significantly with the heap change. Be that as it may, the adjustment of the energy utilization of pico is little, in light of the fact that the power utilization of the power enhancer unit PA represents a huge extent of the energy utilization of the full-scale base station, and the baseband (BB) handling unit represents the biggest energy utilization of the pico. Simultaneously, it tends to be seen that dozing pico can lessen the energy utilization of countless base stations [10]. The straight energy utilization model of the base station is displayed in the following equation:
(4)Pin=NTRX×P0+ΔpPout,0<Pout<Pmax,NTRX×Psleep,Pout=0.The energy utilization model boundaries of various base stations in the formula are displayed in Table1.Table 1
Boundaries of energy utilization models for various base stations.
Base station typeNTRXPmax (w)Po (w)ΔPPsleep (w)Macro61.22122.55.54.2RRH63.8047.77.87.1Micro211.0040.23.75.6Pico28.7460.77.73.4Femto219.3279.84.72.6Aiming at the energy-efficient network deployment problem of heterogeneous networks proposed in this chapter, this paper defines the optimization objective function shown in Equation (5), to acquire the ideal number of pico arrangements NP and position frameworks Xp and Yp. Two position matrices are used to represent each pico two-dimensional space position, which are one-dimensional matrices of length Np.
(5)maxNp,Xp,YpEE=TP=∑m=1NMTmM+∑p=1NpTpP∑m=1NMPmM+∑p=1NpPpP,where PmM and PpP, respectively, represent the energy consumption of the mth eNB and the pth pico, and TmM,TpP, respectively, represent the throughput rates of the mth eNB and the pth pico. Accepting that there are NK clients in the organization, TmM,TmP can be expressed as Equations (6) and (7):
(6)TmM=∑k=1NKwk,0,m×tk,(7)TmP=∑k=1NKwk,p,m×tk,where wk,0,m is the indicator of the user accessing the macro station m. When the user accesses the mth macro station, its value is 1, and when the user does not access the mth macro station, its value is 0. wk,p,m is a flag for the user to access the pth pico. When the user accesses the pth pico, its value is 1, and when the pth pico is not accessed, its value is 0. tk is the throughput pace of the kth client. The relating power is displayed in the following equations:
(8)PmM=∑k=1NKwk,0,m×Pk,(9)PmP=∑k=1NKwk,p,m×Pk,where Pk represents the transmit power allocated to the kth user by its serving base station [11]. In the network, this paper assumes that the location of the macro base station is fixed. In order to simplify the form, p=0 is used to represent the access to the corresponding macro base station. The pico deployment optimization objective function is reexpressed in the form of Equation (10). Constraints: assuming that a user can only access one base station (simultaneous access of multiple cells in COMP mode is not supported), there are constraints (Equation (11)). The spectrum resources of the base station are limited, and the constraints Equations (12) and (13) are obtained.
(10)maxNp,Xp,YpEE=∑k=1NK∑p=0Np∑m=1NMwk,p,m×tk∑k=1NK∑p=0Np∑m=1NMwk,p,m×Pk,(11)s.t.∑p=0Np∑m=1NMwk,p,m=1fork=1,2,⋯,Nk,(12)∑k=1NKwk,0,mnk,0,m<RmMform=1,2,⋯,NM,(13)∑k=1NKwk,p,mnk,p,m<RpPform=1,2,⋯,NP,


### 3.2. System Model and 5G Index Modeling

This section mainly introduces the system model and problem modeling. This chapter assumes that the network infrastructure provider owns and controls the physical network infrastructure resources [12]. At the same time, network infrastructure providers can slice physical network resources into multiple virtual networks (i.e., network slices). And it leases and sells these network slices to service providers according to a certain price mechanism. These service providers rent virtual network resources from network infrastructure providers according to their own business needs and provide customers with a variety of services. The model is shown in Figure 2.Figure 2
Cache node based on virtualization technology.A hub is situated at the focal point of the organization and can be viewed as more significant. Moreover, the significance of hubs should be arranged and concurred between specialist organizations and organization framework suppliers, so in this model, a weight is set for every hub, and the heaviness of hubi is meant as wi. This section expects that the cost of reserve assets per unit limit is communicated as p, so the weighted cost of store assets per unit limit in hub i can be communicated as pi=pwi.In view of the conversation, the value that organization cutk should pay to hub i in unit time T can be communicated as pixikyxik. Correspondingly, the value Pk that organization cut k should pay to all hubs in unit time T can be communicated as follows:
(14)Pk=∑i=1NpixikyxikT.The priceP that should be paid to the network infrastructure provider for all network slices can be expressed as follows:
(15)P=∑i=1MPk=∑k=1M∑i=1NpwixikyxikT.Then again, giving reserving administration forces specific expenses on network foundation suppliers, in numerous written works, the store energy cost is viewed as a significant expense and has gotten broad consideration from the scholastic local area. This model predominantly considers the energy utilization of reserving, including content storing energy utilization and content reaction energy utilization [13]. However, although the user selection scheme reduces interference by dividing highly correlated users according to the correlation coefficient, the idea is still based on two-user scenarios and empirical analysis [14]. As shown in Figure 3, the architecture model of wireless network virtualization is shown.Figure 3
Architectural model for wireless network virtualization.Among them, SP stands for service provider, MNO stands for mobile network operator, MVNO stands for mobile virtual network operator, MVNP stands for mobile virtual network provider, and InP stands for infrastructure provider.To make remote asset the board meet the advancement needs of remote organization virtualization, wireless resource management technology needs to be able to reasonably configure and manage different virtual networks. This paper studies radio resource management from the perspective of spectrum slicing in wireless network virtualization. In particular, in view of the qualities of remote organization virtualization innovation, each virtual organization has its own prerequisites, such as focusing on higher speed, lower delay, and better stability. To achieve these requirements, the number of resource blocks allocated per virtual network plays an important role [15]. On the off chance that a virtual organization is dispensed an enormous number of asset impedes, its presentation can be better ensured generally. In any case, since the absolute number of asset blocks in the framework is restricted, or at least, it is not limitless, it merits focusing on the most proficient method to sensibly designate a suitable number of asset blocks for a long-time organization. The exploration on this issue will assist the framework with further developing the use pace of framework assets and give a more adaptable and powerful designation technique for the requirements of various virtual organizations.


### 3.3. Investigation and Analysis Methods of Teaching Environment Evaluation

During the time spent the poll review, we effectively ingest the ideas and assessments given by the current specialists on the assessment record arrangement of actual schooling showing climate and counsel the applicable writing again to form the assessment file arrangement of actual training climate in customary schools and colleges [16]. Then, at that point, the assessment markers at all levels are planned into polls, and the really measurable boundaries of the review are discussed as follows.


#### 3.3.1. Coefficient of Variation

The coefficient of variation is the ratio of the standard deviation of each metric to the weighted mean. The clearer the coefficient of variation, the more consistent the expert opinion. In general, experts may disagree if the coefficient of variation is 25% or higher. SupposeXij represents the ith expert and the jth index score. Currently, there are n experts and M indices in the scale as follows:
(16)Mj=1n∑i=1nXij,Sj=1n−1∑i=1nXij−Mj.The formula for calculating the coefficient of variation is as follows:(17)Vj=SjMj.The coefficient of variety of the record isVj, the standard deviation of the list is Sj, and the number juggling mean of the file is Mj. The more modest the worth of Vj, the higher the coordination level of specialists on the j file.


#### 3.3.2. Coordination Coefficient

The coordination level of specialists for a specific record can be reflected by the size of the coordination coefficientW, and there are two calculation formulas. When experts do not give the same evaluation to each indicator, the formula is expressed as follows:
(18)W=12∑j=1ndj2nm2n2−1.When experts have the same evaluation of each indicator, the formula is expressed as follows:(19)W=12∑j=1ndj2m2n3−n−m∑i=1mTi,where Ti=∑L=1LtL3−tL is the number of identical score groups in the i expert score, L=1,2,⋯,L, and tL is the number of identical scores in the L group. The coordination coefficient is addressed by W, the complete number of specialists is addressed by m, the quantity of pointers is addressed by n, and d is addressed by the distinction between every marker level and the mean of the amount of n pointer levels. Ti is called the correction coefficient, and dj represents the difference between the sum of the importance scores of the ith index and the average value of the sum of the importance scores of each index. The value of W is between (0, 1). The larger the value of W, the higher the coordination level. After 2 to 3 rounds of adjustment, the value of W generally fluctuates around 0.5.The adjusted degree of significance test should use the rank consistency test (nonparametric test) [17]. If P>0.05, this paper can consider that the expert opinion rating is unreliable and the result is not ideal. P<0.05 indicates that the expert opinion rating is credible. K. Pearson X2 tested the importance of the degree of adjustment with the formula:
(20)X2=1mnn+1−1/n−1∑i=1mTi∑j=1ndj2.The foundation of the assessment record of actual schooling showing climate in normal universities and colleges has a generally excellent impact on the development of understudies’ advantage in actual training and the improvement of class environment. In this way, while deciding markers, the accompanying standards should be completely controlled: the rule of objectivity, the rule of possibility, the guideline of scientificity, and the rule of extensiveness.


## 3.1. Network Energy Efficiency Model

There are two standard portable remote organization energy productivity assessment strategies in the business. One is the energy consumption per unit area, which is defined as the total power consumption of the mobile wireless network divided by the total area (w/m2). The area is usually constant. This approach of focusing on the total energy consumption of the network helps environmental groups measure carbon dioxide emissions in the region. Another indicator is the energy consumption per bit, which is defined as the system data transfer rate divided by the total energy consumption, and the unit is bit/S.w or bit/J for short. This method of comprehensively considering network performance and system energy consumption has been adopted by the Japanese 5G promotion group and is defined as the main energy efficiency indicator. This paper also adopts this method as the basic energy efficiency evaluation method, such as the following equation:
(1)EE=TP,where P is the energy consumption of the target system and T is the capacity of the target system [8]. For a homogeneous network scenario with only macro base stations, the EEnet statistical formula for network energy efficiency is as follows:
(2)EEnet=∑i=1NMTiM∑i=1NMPiM,where NM is the number of macro stations, TiM and PiM are the throughput and power consumption of the ith macro station [9]. The parameters of the energy consumption model of each low-power node need to be considered separately; furthermore, the energy proficiency of the heterogeneous organization is displayed in the following formula:
(3)EEnet=∑i=1NMTiM+∑j=1NrTjP∑i=1NMPiM+∑j=1NrTjP.A base station generally includes multiple sets of transceivers, each of which includes a power amplifier unit PA, a radio frequency unit RF, a baseband unit BB, a DC-DC converter, cooling equipment, and a power supply trunk. Based on Earth’s internal investigation and Sandvine report, the energy consumption components PA, RF, BB, DC, CO, MS, and total energy consumption of macro and pico as a function of load are shown in Figure1. The load is expressed as Pout/Pmax.Figure 1
Changes in energy consumption composition of pico base stations with load.The black circle in the figure represents the energy consumption of the base station when it is sleeping. It tends to be seen that the RF yieldPout has a direct relationship with the energy utilization Pin of the base station, and the energy utilization of the full-scale base station vacillates significantly with the heap change. Be that as it may, the adjustment of the energy utilization of pico is little, in light of the fact that the power utilization of the power enhancer unit PA represents a huge extent of the energy utilization of the full-scale base station, and the baseband (BB) handling unit represents the biggest energy utilization of the pico. Simultaneously, it tends to be seen that dozing pico can lessen the energy utilization of countless base stations [10]. The straight energy utilization model of the base station is displayed in the following equation:
(4)Pin=NTRX×P0+ΔpPout,0<Pout<Pmax,NTRX×Psleep,Pout=0.The energy utilization model boundaries of various base stations in the formula are displayed in Table1.Table 1
Boundaries of energy utilization models for various base stations.
Base station typeNTRXPmax (w)Po (w)ΔPPsleep (w)Macro61.22122.55.54.2RRH63.8047.77.87.1Micro211.0040.23.75.6Pico28.7460.77.73.4Femto219.3279.84.72.6Aiming at the energy-efficient network deployment problem of heterogeneous networks proposed in this chapter, this paper defines the optimization objective function shown in Equation (5), to acquire the ideal number of pico arrangements NP and position frameworks Xp and Yp. Two position matrices are used to represent each pico two-dimensional space position, which are one-dimensional matrices of length Np.
(5)maxNp,Xp,YpEE=TP=∑m=1NMTmM+∑p=1NpTpP∑m=1NMPmM+∑p=1NpPpP,where PmM and PpP, respectively, represent the energy consumption of the mth eNB and the pth pico, and TmM,TpP, respectively, represent the throughput rates of the mth eNB and the pth pico. Accepting that there are NK clients in the organization, TmM,TmP can be expressed as Equations (6) and (7):
(6)TmM=∑k=1NKwk,0,m×tk,(7)TmP=∑k=1NKwk,p,m×tk,where wk,0,m is the indicator of the user accessing the macro station m. When the user accesses the mth macro station, its value is 1, and when the user does not access the mth macro station, its value is 0. wk,p,m is a flag for the user to access the pth pico. When the user accesses the pth pico, its value is 1, and when the pth pico is not accessed, its value is 0. tk is the throughput pace of the kth client. The relating power is displayed in the following equations:
(8)PmM=∑k=1NKwk,0,m×Pk,(9)PmP=∑k=1NKwk,p,m×Pk,where Pk represents the transmit power allocated to the kth user by its serving base station [11]. In the network, this paper assumes that the location of the macro base station is fixed. In order to simplify the form, p=0 is used to represent the access to the corresponding macro base station. The pico deployment optimization objective function is reexpressed in the form of Equation (10). Constraints: assuming that a user can only access one base station (simultaneous access of multiple cells in COMP mode is not supported), there are constraints (Equation (11)). The spectrum resources of the base station are limited, and the constraints Equations (12) and (13) are obtained.
(10)maxNp,Xp,YpEE=∑k=1NK∑p=0Np∑m=1NMwk,p,m×tk∑k=1NK∑p=0Np∑m=1NMwk,p,m×Pk,(11)s.t.∑p=0Np∑m=1NMwk,p,m=1fork=1,2,⋯,Nk,(12)∑k=1NKwk,0,mnk,0,m<RmMform=1,2,⋯,NM,(13)∑k=1NKwk,p,mnk,p,m<RpPform=1,2,⋯,NP,


## 3.2. System Model and 5G Index Modeling

This section mainly introduces the system model and problem modeling. This chapter assumes that the network infrastructure provider owns and controls the physical network infrastructure resources [12]. At the same time, network infrastructure providers can slice physical network resources into multiple virtual networks (i.e., network slices). And it leases and sells these network slices to service providers according to a certain price mechanism. These service providers rent virtual network resources from network infrastructure providers according to their own business needs and provide customers with a variety of services. The model is shown in Figure 2.Figure 2
Cache node based on virtualization technology.A hub is situated at the focal point of the organization and can be viewed as more significant. Moreover, the significance of hubs should be arranged and concurred between specialist organizations and organization framework suppliers, so in this model, a weight is set for every hub, and the heaviness of hubi is meant as wi. This section expects that the cost of reserve assets per unit limit is communicated as p, so the weighted cost of store assets per unit limit in hub i can be communicated as pi=pwi.In view of the conversation, the value that organization cutk should pay to hub i in unit time T can be communicated as pixikyxik. Correspondingly, the value Pk that organization cut k should pay to all hubs in unit time T can be communicated as follows:
(14)Pk=∑i=1NpixikyxikT.The priceP that should be paid to the network infrastructure provider for all network slices can be expressed as follows:
(15)P=∑i=1MPk=∑k=1M∑i=1NpwixikyxikT.Then again, giving reserving administration forces specific expenses on network foundation suppliers, in numerous written works, the store energy cost is viewed as a significant expense and has gotten broad consideration from the scholastic local area. This model predominantly considers the energy utilization of reserving, including content storing energy utilization and content reaction energy utilization [13]. However, although the user selection scheme reduces interference by dividing highly correlated users according to the correlation coefficient, the idea is still based on two-user scenarios and empirical analysis [14]. As shown in Figure 3, the architecture model of wireless network virtualization is shown.Figure 3
Architectural model for wireless network virtualization.Among them, SP stands for service provider, MNO stands for mobile network operator, MVNO stands for mobile virtual network operator, MVNP stands for mobile virtual network provider, and InP stands for infrastructure provider.To make remote asset the board meet the advancement needs of remote organization virtualization, wireless resource management technology needs to be able to reasonably configure and manage different virtual networks. This paper studies radio resource management from the perspective of spectrum slicing in wireless network virtualization. In particular, in view of the qualities of remote organization virtualization innovation, each virtual organization has its own prerequisites, such as focusing on higher speed, lower delay, and better stability. To achieve these requirements, the number of resource blocks allocated per virtual network plays an important role [15]. On the off chance that a virtual organization is dispensed an enormous number of asset impedes, its presentation can be better ensured generally. In any case, since the absolute number of asset blocks in the framework is restricted, or at least, it is not limitless, it merits focusing on the most proficient method to sensibly designate a suitable number of asset blocks for a long-time organization. The exploration on this issue will assist the framework with further developing the use pace of framework assets and give a more adaptable and powerful designation technique for the requirements of various virtual organizations.


## 3.3. Investigation and Analysis Methods of Teaching Environment Evaluation

During the time spent the poll review, we effectively ingest the ideas and assessments given by the current specialists on the assessment record arrangement of actual schooling showing climate and counsel the applicable writing again to form the assessment file arrangement of actual training climate in customary schools and colleges [16]. Then, at that point, the assessment markers at all levels are planned into polls, and the really measurable boundaries of the review are discussed as follows.


### 3.3.1. Coefficient of Variation

The coefficient of variation is the ratio of the standard deviation of each metric to the weighted mean. The clearer the coefficient of variation, the more consistent the expert opinion. In general, experts may disagree if the coefficient of variation is 25% or higher. SupposeXij represents the ith expert and the jth index score. Currently, there are n experts and M indices in the scale as follows:
(16)Mj=1n∑i=1nXij,Sj=1n−1∑i=1nXij−Mj.The formula for calculating the coefficient of variation is as follows:(17)Vj=SjMj.The coefficient of variety of the record isVj, the standard deviation of the list is Sj, and the number juggling mean of the file is Mj. The more modest the worth of Vj, the higher the coordination level of specialists on the j file.


### 3.3.2. Coordination Coefficient

The coordination level of specialists for a specific record can be reflected by the size of the coordination coefficientW, and there are two calculation formulas. When experts do not give the same evaluation to each indicator, the formula is expressed as follows:
(18)W=12∑j=1ndj2nm2n2−1.When experts have the same evaluation of each indicator, the formula is expressed as follows:(19)W=12∑j=1ndj2m2n3−n−m∑i=1mTi,where Ti=∑L=1LtL3−tL is the number of identical score groups in the i expert score, L=1,2,⋯,L, and tL is the number of identical scores in the L group. The coordination coefficient is addressed by W, the complete number of specialists is addressed by m, the quantity of pointers is addressed by n, and d is addressed by the distinction between every marker level and the mean of the amount of n pointer levels. Ti is called the correction coefficient, and dj represents the difference between the sum of the importance scores of the ith index and the average value of the sum of the importance scores of each index. The value of W is between (0, 1). The larger the value of W, the higher the coordination level. After 2 to 3 rounds of adjustment, the value of W generally fluctuates around 0.5.The adjusted degree of significance test should use the rank consistency test (nonparametric test) [17]. If P>0.05, this paper can consider that the expert opinion rating is unreliable and the result is not ideal. P<0.05 indicates that the expert opinion rating is credible. K. Pearson X2 tested the importance of the degree of adjustment with the formula:
(20)X2=1mnn+1−1/n−1∑i=1mTi∑j=1ndj2.The foundation of the assessment record of actual schooling showing climate in normal universities and colleges has a generally excellent impact on the development of understudies’ advantage in actual training and the improvement of class environment. In this way, while deciding markers, the accompanying standards should be completely controlled: the rule of objectivity, the rule of possibility, the guideline of scientificity, and the rule of extensiveness.


## 3.3.1. Coefficient of Variation

The coefficient of variation is the ratio of the standard deviation of each metric to the weighted mean. The clearer the coefficient of variation, the more consistent the expert opinion. In general, experts may disagree if the coefficient of variation is 25% or higher. SupposeXij represents the ith expert and the jth index score. Currently, there are n experts and M indices in the scale as follows:
(16)Mj=1n∑i=1nXij,Sj=1n−1∑i=1nXij−Mj.The formula for calculating the coefficient of variation is as follows:(17)Vj=SjMj.The coefficient of variety of the record isVj, the standard deviation of the list is Sj, and the number juggling mean of the file is Mj. The more modest the worth of Vj, the higher the coordination level of specialists on the j file.


## 3.3.2. Coordination Coefficient

The coordination level of specialists for a specific record can be reflected by the size of the coordination coefficientW, and there are two calculation formulas. When experts do not give the same evaluation to each indicator, the formula is expressed as follows:
(18)W=12∑j=1ndj2nm2n2−1.When experts have the same evaluation of each indicator, the formula is expressed as follows:(19)W=12∑j=1ndj2m2n3−n−m∑i=1mTi,where Ti=∑L=1LtL3−tL is the number of identical score groups in the i expert score, L=1,2,⋯,L, and tL is the number of identical scores in the L group. The coordination coefficient is addressed by W, the complete number of specialists is addressed by m, the quantity of pointers is addressed by n, and d is addressed by the distinction between every marker level and the mean of the amount of n pointer levels. Ti is called the correction coefficient, and dj represents the difference between the sum of the importance scores of the ith index and the average value of the sum of the importance scores of each index. The value of W is between (0, 1). The larger the value of W, the higher the coordination level. After 2 to 3 rounds of adjustment, the value of W generally fluctuates around 0.5.The adjusted degree of significance test should use the rank consistency test (nonparametric test) [17]. If P>0.05, this paper can consider that the expert opinion rating is unreliable and the result is not ideal. P<0.05 indicates that the expert opinion rating is credible. K. Pearson X2 tested the importance of the degree of adjustment with the formula:
(20)X2=1mnn+1−1/n−1∑i=1mTi∑j=1ndj2.The foundation of the assessment record of actual schooling showing climate in normal universities and colleges has a generally excellent impact on the development of understudies’ advantage in actual training and the improvement of class environment. In this way, while deciding markers, the accompanying standards should be completely controlled: the rule of objectivity, the rule of possibility, the guideline of scientificity, and the rule of extensiveness.


## 4. Poll Survey Based on the Construction of Physical Education Teaching Indicators Based on 5G

### 4.1. Development Method of Physical Education Environment Evaluation Index System

This study mainly adopts the Delphi method to select evaluation indicators and finally obtains a specific sports environment evaluation index system. Taking the construction arrangement of actual schooling climate components in schools and colleges got in the past review as the premise. It sums up partitions and thinks about the significant pointers that influence the actual instruction climate in schools and colleges as indicated by the standards of the foundation of the assessment framework and the past exploration experience. It frames the origination record of actual schooling showing climate in schools and colleges, that is to say, the first round of master surveys and meetings in the assessment file arrangement of actual training showing climate in universities and colleges. As per the origination marks of the actual training climate in schools and colleges, this paper decides the three-layer design of the complete ecological assessment framework (the principal level pointers are partitioned into actual climate, mental climate, and institutional climate) and the more significant variables under the three-layer structure [18]. The idea markers cover the principal, second, and third level pointers, and the number is 3, 6, and 12 separately. The planned framework is displayed in Table 2.Table 2
Theoretical file framework for the assessment of actual training showing climate in schools and colleges.
First-level indicatorSecondary indicatorsThree-level indicatorA1 physical environmentB1 natural environmentC1 air qualityC2 temperature, humidityC3 light intensityB2 facility environmentC5 environmental greening rateC6 noise aroundC7 athletics venue facilitiesA2 psychological environmentB4 relationshipsC14 teacher-student relationsC15 student-student relationsC16 teacher-teacher relationsB5 teaching organizational environmentC19 teaching ideasC20 teaching objectivesC21 teaching contentA3 institutional environmentB9 school rules and regulationsC36 school teaching management organizationC37 teaching staffC38 course offering formB10 classroom rules and regulationsC40 teaching disciplineC41 teaching styleC42 instructional controlWhile leading the master poll study, as indicated by the extent of the school sports climate assessment record framework, endeavors ought to be made to choose specialists with significant scholastic foundation, broad knowledge, and familiarity with many fields according to the principle of representativeness and authority. In this paper, 30 college teachers and experts were surveyed by means of a questionnaire survey [19]. The basic situation is shown in Figure 4.Figure 4
Fundamental circumstance of specialists reviewed on the assessment list of actual instruction showing climate in schools and colleges.This study selected three representative universities in Liaoning Province of China, namely, Liaoning University, Liaoning Normal University, and Dalian University of Technology. A total of 45 teacher questionnaires were distributed, and 40 were recovered, with a recovery rate of 88.89%. Among them, 40 were effective, with an effective rate of 88.89%. 300 questionnaires were distributed to students, a total of 294 were recovered, with a recovery rate of 98%, and 284 were valid, with a 96.6% response rate. The questionnaire is shown in Figure5.Figure 5
List of distribution and recovery statistics of teacher and student questionnaires.A total of 7 first-level indicators were initially selected, namely, interpersonal environment, emotional environment, organizational environment, information environment and public opinion environment, safety environment, and health environment [20]. The importance of each indicator is confirmed by statistics and analysis of the results of the second round of expert questionnaires. The results of the survey are shown in Figure 6.Figure 6
Statistical table of the second round of expert survey on primary indicators.According to the statistical results, it can be seen that in the evaluation indicators of the soft environment of physical education teaching in colleges and universities, the importance of each indicator is not the same. To figure out the significance of every marker, the choice of the significance of every pointer by specialists is counted, and its significance is communicated as a rate. The bigger the rate esteem, the higher the significance of the marker. The results of the expert survey showed that the first six indicators were recognized by experts, while the percentages of the last indicator of health and environment importance and comparative importance were both low. Experts believe that the definition of health and environmental indicators is inaccurate and the content is duplicated with the indicators, and it is recommended to modify or delete them. According to the relevant literature review according to an expert opinion, the definition of healthy environment is rather vague, so this indicator is deleted [21].The analysis of the evaluation results covers the comparative analysis of the total scores of the physical education environment of the three schools and the comparative analysis of the three aspects of the physical environment, the psychological environment, and the institutional environment. Figure7 shows a relative investigation of the all-out score of the actual instruction climate in the three schools and colleges.Figure 7
Comparison of total score results.It can be seen that the physical education teaching environment in colleges and universities in Liaoning generally meets the needs of physical education teaching in colleges and universities. At the same time, college teachers and students believe that the physical education teaching environment in colleges and universities will have a great impact on physical education teaching, and are relatively satisfied with the physical education teaching environment in their schools, but there is still a certain gap from the expected physical education teaching environment [22].Following the second round of experts’ statistical analysis of the indicator selection results and the supplementary revision of expert opinions, the second round of expert questionnaires was designed on this basis, and the third round of mathematical statistics was conducted. The selection of the number of rounds and indicators was finalized in the expert survey. In the third round of expert survey results, the key indicators were again statistically analyzed, and the results are shown in Figure8.Figure 8
The third round of indicators’ data statistics table.It tends to be seen that in the three schools and colleges PE showing climate assessment list framework, the level of significance of the principal level markers is unique. As a whole, it can be seen that the three indicators of material environment, psychological environment, and cultural institutional environment have been consistently recognized by experts. The recognition degree is shown in Figure9, and this data can be used as a scientific evaluation index.Figure 9
Results of the second round of expert surveys.The physical education environment is the basic condition to create a physical education atmosphere and carry out physical education activities. It involves a variety of factors, among which there are “mixed advantages and disadvantages”, and it is very necessary to formulate scientific indicators for its evaluation. Physical education system is first of all a complete dynamic system. During the time spent deciding the assessment pointers, it is found that there is no clear boundary between many indicators, and there is no standard for distinguishing them, so it is difficult to screen and classify them. Among the three first-level indicators, the material environment index has the highest weight value, which means that experts attach the most importance to it, followed by the cultural system environment index and the psychological environment index. Among the material climate factors, the heaviness of the showing office climate in the material climate is 0.6796, which assumes a main part in the development of the material climate.


### 4.2. Sports Environment Evaluation and Physical Exercise Orientation

Table3 shows the relationship insights between the actual climate appraisal and the heading of actual development conduct of understudies of various sexes. The table shows that physical environment assessments in boys and girls have a reasonably significant positive relationship with the direction of their physical motor behavior [23]. It shows that in both young men and young ladies, their actual exercise conduct direction will be impacted by the games’ climate, and working on understudies’ assessment of the games’ climate is helpful for working on the energy of understudies to take part in sports.Table 3
Correlation statistics between different students’ evaluation of sports environment and physical exercise behavior orientation.
Gender categoryCorrelation performanceNatural environmentPhysical educationSports associationsOverall environmental assessment scoreBoys’ exercise behaviorPearson0.3350.360∗∗0.1100.321∗∗Significance0.0670.0000.1790.000Girls’ exercise behaviorPearson0.3540.346∗∗0.287∗∗0.305∗∗Significance0.0620.0000.0040.000There is a huge positive relationship between young men’s actual exercise conduct direction and actual schooling climate assessment, sports exercises and contest climate assessment, sports rules and guidelines, and sports climate assessment and sports data assessment. Interestingly, young men’s actual exercise conduct direction was not altogether connected with common habitat evaluation and ecological appraisal of sports affiliations. At present, the development of sports clubs in most schools is not sound, and the contents of club activities are single, resulting in fewer students participating in the activities of sports clubs.There is a huge positive relationship between young ladies’ actual exercise conduct direction and their actual schooling climate assessment, sports organization environment evaluation, sports activity and competition environment evaluation, and sports regulations and management-related environment evaluation. However, its correlation with natural environment, sports facilities, and sports information environment evaluation is not significant. Physical education environment, sports competition environment, and sports rules and regulations and management environment have relatively significant influence on male and female students’ physical exercise behavior orientation. Schools can improve students’ evaluation of relevant indicators by optimizing relevant indicators, which can promote students’ physical exercise behavior orientation to a certain extent.Table4 is obtained from the correlation analysis of the physical exercise behavior orientation of students in different disciplines in three colleges and universities and the evaluation of related indicators of sports environment. It may be seen very well that there is a huge positive relationship between the actual exercise conduct direction of understudies of various disciplines and their games’ climate assessment. The behavioral orientation of physical exercise of liberal arts, science, and engineering students is more significantly related to other disciplines of physical environment. The behavioral orientation of physical exercise of students of arts and engineering is highly correlated with the evaluation of sports environment.Table 4
Measurements on the relationship between the assessment of sports climate and the direction of actual exercise by understudies of various disciplines in schools and colleges.
Gender categoryCorrelation performanceNatural environmentPhysical educationSports associationsOverall environmental assessment scoreLiberal arts exercise behavior orientationPearson0.228∗0.311∗0.499∗0.578∗∗Significance0.0330.0210.0010.000Science exercise behavior orientationPearson0.2460.348∗∗0.388∗∗0.472∗∗Significance0.0580.0310.0210.000


### 4.3. Actual Education and Evaluation under 5G Technology

The appearance of the data innovation time in the 21st century and the improvement of science and innovation have advanced the fast improvement of showing climate research results. This stage entered the peak period of research, and multimedia teaching methods, network environment, and distance teaching began to be applied to teaching and directly promoted the transition from traditional teaching to modern teaching. Therefore, it also broadens the research scope of teaching environment. It can be seen that the research on teaching environment in China started relatively late, and a very mature research system has not yet been formed. Lately, the enhancement of showing content and the improvement of showing strategies have widened the examination extent of showing climate and have accomplished specific outcomes.Lately, the utilization of instructive innovation in the organization climate has given full play to the job of superb instructors and instructive assets. It makes it workable for educators to utilize PC network correspondence innovation to communicate different instructive data like mixed media, text, and pictures, and work on the nature of schooling.The design advantage of diagnosing and evaluating students’ learning effects in the network environment lies in the transformation of students’ learning methods. In the classroom, students move from passive repetitive motor skill practice to active learning and acquisition of skills, and students use a variety of expertise to creatively build their own skill learning methods and learn how to learn. This develops students’ ability to think independently, analyze problems, and solve problems as they develop motor skills. In multimedia CAI, question design is very important for the diagnosis and evaluation of educational practice. This is also the core content of the application of computer CAI-assisted teaching in tennis professional and technical courses. Through teaching practice, it finds that students’ technical problems (individual and overall wrong technical actions) are recorded as digital.The student’s operation video data is imported into the teaching courseware, which is used as the problem material for students to analyze. Simultaneously, the CAI programming capacity and the movement investigation programming are utilized for examination and correlation. This timely feedback mechanism to applied media will help students to correct their understanding and incorrect technical movements in practice [24]. Whether it is improving feedback or encouraging feedback, it can help students deepen correct motor skills and form regular understanding and memory. These theories are compatible with the principles of motor skill formation. This innovative approach optimizes the teaching process.In the tennis practice class, the separation of test and teaching is implemented in accordance with the spirit of the document issued by the school’s Academic Affairs Office. From Figure10, the professional skill evaluation of 161 tennis students is shown: 150 forehands were excellent and 11 were good. 120 players were excellent in backhand draw and 41 players were good. 90 were excellent in serving skills, 51 were good, and 20 were qualified. 106 volleys were excellent, 40 were good, and 15 were qualified. 110 people in high-voltage technology are excellent; 51 people are good.Figure 10
Skill evaluation chart for tennis students.In the 5G climate, schools and colleges can completely tackle the troubles looked by understudies in the learning system through logical examination of mixed media courseware, joined with an assortment of unique specialized activities, along these lines advancing understudies’ dynamic learning capacity. All the more critically, 5G innovation can change the conventional, single, and abstract assessment strategy for actual training instructing in schools and colleges, subsequently advancing the speed of showing change in actual instruction innovation courses.


## 4.1. Development Method of Physical Education Environment Evaluation Index System

This study mainly adopts the Delphi method to select evaluation indicators and finally obtains a specific sports environment evaluation index system. Taking the construction arrangement of actual schooling climate components in schools and colleges got in the past review as the premise. It sums up partitions and thinks about the significant pointers that influence the actual instruction climate in schools and colleges as indicated by the standards of the foundation of the assessment framework and the past exploration experience. It frames the origination record of actual schooling showing climate in schools and colleges, that is to say, the first round of master surveys and meetings in the assessment file arrangement of actual training showing climate in universities and colleges. As per the origination marks of the actual training climate in schools and colleges, this paper decides the three-layer design of the complete ecological assessment framework (the principal level pointers are partitioned into actual climate, mental climate, and institutional climate) and the more significant variables under the three-layer structure [18]. The idea markers cover the principal, second, and third level pointers, and the number is 3, 6, and 12 separately. The planned framework is displayed in Table 2.Table 2
Theoretical file framework for the assessment of actual training showing climate in schools and colleges.
First-level indicatorSecondary indicatorsThree-level indicatorA1 physical environmentB1 natural environmentC1 air qualityC2 temperature, humidityC3 light intensityB2 facility environmentC5 environmental greening rateC6 noise aroundC7 athletics venue facilitiesA2 psychological environmentB4 relationshipsC14 teacher-student relationsC15 student-student relationsC16 teacher-teacher relationsB5 teaching organizational environmentC19 teaching ideasC20 teaching objectivesC21 teaching contentA3 institutional environmentB9 school rules and regulationsC36 school teaching management organizationC37 teaching staffC38 course offering formB10 classroom rules and regulationsC40 teaching disciplineC41 teaching styleC42 instructional controlWhile leading the master poll study, as indicated by the extent of the school sports climate assessment record framework, endeavors ought to be made to choose specialists with significant scholastic foundation, broad knowledge, and familiarity with many fields according to the principle of representativeness and authority. In this paper, 30 college teachers and experts were surveyed by means of a questionnaire survey [19]. The basic situation is shown in Figure 4.Figure 4
Fundamental circumstance of specialists reviewed on the assessment list of actual instruction showing climate in schools and colleges.This study selected three representative universities in Liaoning Province of China, namely, Liaoning University, Liaoning Normal University, and Dalian University of Technology. A total of 45 teacher questionnaires were distributed, and 40 were recovered, with a recovery rate of 88.89%. Among them, 40 were effective, with an effective rate of 88.89%. 300 questionnaires were distributed to students, a total of 294 were recovered, with a recovery rate of 98%, and 284 were valid, with a 96.6% response rate. The questionnaire is shown in Figure5.Figure 5
List of distribution and recovery statistics of teacher and student questionnaires.A total of 7 first-level indicators were initially selected, namely, interpersonal environment, emotional environment, organizational environment, information environment and public opinion environment, safety environment, and health environment [20]. The importance of each indicator is confirmed by statistics and analysis of the results of the second round of expert questionnaires. The results of the survey are shown in Figure 6.Figure 6
Statistical table of the second round of expert survey on primary indicators.According to the statistical results, it can be seen that in the evaluation indicators of the soft environment of physical education teaching in colleges and universities, the importance of each indicator is not the same. To figure out the significance of every marker, the choice of the significance of every pointer by specialists is counted, and its significance is communicated as a rate. The bigger the rate esteem, the higher the significance of the marker. The results of the expert survey showed that the first six indicators were recognized by experts, while the percentages of the last indicator of health and environment importance and comparative importance were both low. Experts believe that the definition of health and environmental indicators is inaccurate and the content is duplicated with the indicators, and it is recommended to modify or delete them. According to the relevant literature review according to an expert opinion, the definition of healthy environment is rather vague, so this indicator is deleted [21].The analysis of the evaluation results covers the comparative analysis of the total scores of the physical education environment of the three schools and the comparative analysis of the three aspects of the physical environment, the psychological environment, and the institutional environment. Figure7 shows a relative investigation of the all-out score of the actual instruction climate in the three schools and colleges.Figure 7
Comparison of total score results.It can be seen that the physical education teaching environment in colleges and universities in Liaoning generally meets the needs of physical education teaching in colleges and universities. At the same time, college teachers and students believe that the physical education teaching environment in colleges and universities will have a great impact on physical education teaching, and are relatively satisfied with the physical education teaching environment in their schools, but there is still a certain gap from the expected physical education teaching environment [22].Following the second round of experts’ statistical analysis of the indicator selection results and the supplementary revision of expert opinions, the second round of expert questionnaires was designed on this basis, and the third round of mathematical statistics was conducted. The selection of the number of rounds and indicators was finalized in the expert survey. In the third round of expert survey results, the key indicators were again statistically analyzed, and the results are shown in Figure8.Figure 8
The third round of indicators’ data statistics table.It tends to be seen that in the three schools and colleges PE showing climate assessment list framework, the level of significance of the principal level markers is unique. As a whole, it can be seen that the three indicators of material environment, psychological environment, and cultural institutional environment have been consistently recognized by experts. The recognition degree is shown in Figure9, and this data can be used as a scientific evaluation index.Figure 9
Results of the second round of expert surveys.The physical education environment is the basic condition to create a physical education atmosphere and carry out physical education activities. It involves a variety of factors, among which there are “mixed advantages and disadvantages”, and it is very necessary to formulate scientific indicators for its evaluation. Physical education system is first of all a complete dynamic system. During the time spent deciding the assessment pointers, it is found that there is no clear boundary between many indicators, and there is no standard for distinguishing them, so it is difficult to screen and classify them. Among the three first-level indicators, the material environment index has the highest weight value, which means that experts attach the most importance to it, followed by the cultural system environment index and the psychological environment index. Among the material climate factors, the heaviness of the showing office climate in the material climate is 0.6796, which assumes a main part in the development of the material climate.


## 4.2. Sports Environment Evaluation and Physical Exercise Orientation

Table3 shows the relationship insights between the actual climate appraisal and the heading of actual development conduct of understudies of various sexes. The table shows that physical environment assessments in boys and girls have a reasonably significant positive relationship with the direction of their physical motor behavior [23]. It shows that in both young men and young ladies, their actual exercise conduct direction will be impacted by the games’ climate, and working on understudies’ assessment of the games’ climate is helpful for working on the energy of understudies to take part in sports.Table 3
Correlation statistics between different students’ evaluation of sports environment and physical exercise behavior orientation.
Gender categoryCorrelation performanceNatural environmentPhysical educationSports associationsOverall environmental assessment scoreBoys’ exercise behaviorPearson0.3350.360∗∗0.1100.321∗∗Significance0.0670.0000.1790.000Girls’ exercise behaviorPearson0.3540.346∗∗0.287∗∗0.305∗∗Significance0.0620.0000.0040.000There is a huge positive relationship between young men’s actual exercise conduct direction and actual schooling climate assessment, sports exercises and contest climate assessment, sports rules and guidelines, and sports climate assessment and sports data assessment. Interestingly, young men’s actual exercise conduct direction was not altogether connected with common habitat evaluation and ecological appraisal of sports affiliations. At present, the development of sports clubs in most schools is not sound, and the contents of club activities are single, resulting in fewer students participating in the activities of sports clubs.There is a huge positive relationship between young ladies’ actual exercise conduct direction and their actual schooling climate assessment, sports organization environment evaluation, sports activity and competition environment evaluation, and sports regulations and management-related environment evaluation. However, its correlation with natural environment, sports facilities, and sports information environment evaluation is not significant. Physical education environment, sports competition environment, and sports rules and regulations and management environment have relatively significant influence on male and female students’ physical exercise behavior orientation. Schools can improve students’ evaluation of relevant indicators by optimizing relevant indicators, which can promote students’ physical exercise behavior orientation to a certain extent.Table4 is obtained from the correlation analysis of the physical exercise behavior orientation of students in different disciplines in three colleges and universities and the evaluation of related indicators of sports environment. It may be seen very well that there is a huge positive relationship between the actual exercise conduct direction of understudies of various disciplines and their games’ climate assessment. The behavioral orientation of physical exercise of liberal arts, science, and engineering students is more significantly related to other disciplines of physical environment. The behavioral orientation of physical exercise of students of arts and engineering is highly correlated with the evaluation of sports environment.Table 4
Measurements on the relationship between the assessment of sports climate and the direction of actual exercise by understudies of various disciplines in schools and colleges.
Gender categoryCorrelation performanceNatural environmentPhysical educationSports associationsOverall environmental assessment scoreLiberal arts exercise behavior orientationPearson0.228∗0.311∗0.499∗0.578∗∗Significance0.0330.0210.0010.000Science exercise behavior orientationPearson0.2460.348∗∗0.388∗∗0.472∗∗Significance0.0580.0310.0210.000


## 4.3. Actual Education and Evaluation under 5G Technology

The appearance of the data innovation time in the 21st century and the improvement of science and innovation have advanced the fast improvement of showing climate research results. This stage entered the peak period of research, and multimedia teaching methods, network environment, and distance teaching began to be applied to teaching and directly promoted the transition from traditional teaching to modern teaching. Therefore, it also broadens the research scope of teaching environment. It can be seen that the research on teaching environment in China started relatively late, and a very mature research system has not yet been formed. Lately, the enhancement of showing content and the improvement of showing strategies have widened the examination extent of showing climate and have accomplished specific outcomes.Lately, the utilization of instructive innovation in the organization climate has given full play to the job of superb instructors and instructive assets. It makes it workable for educators to utilize PC network correspondence innovation to communicate different instructive data like mixed media, text, and pictures, and work on the nature of schooling.The design advantage of diagnosing and evaluating students’ learning effects in the network environment lies in the transformation of students’ learning methods. In the classroom, students move from passive repetitive motor skill practice to active learning and acquisition of skills, and students use a variety of expertise to creatively build their own skill learning methods and learn how to learn. This develops students’ ability to think independently, analyze problems, and solve problems as they develop motor skills. In multimedia CAI, question design is very important for the diagnosis and evaluation of educational practice. This is also the core content of the application of computer CAI-assisted teaching in tennis professional and technical courses. Through teaching practice, it finds that students’ technical problems (individual and overall wrong technical actions) are recorded as digital.The student’s operation video data is imported into the teaching courseware, which is used as the problem material for students to analyze. Simultaneously, the CAI programming capacity and the movement investigation programming are utilized for examination and correlation. This timely feedback mechanism to applied media will help students to correct their understanding and incorrect technical movements in practice [24]. Whether it is improving feedback or encouraging feedback, it can help students deepen correct motor skills and form regular understanding and memory. These theories are compatible with the principles of motor skill formation. This innovative approach optimizes the teaching process.In the tennis practice class, the separation of test and teaching is implemented in accordance with the spirit of the document issued by the school’s Academic Affairs Office. From Figure10, the professional skill evaluation of 161 tennis students is shown: 150 forehands were excellent and 11 were good. 120 players were excellent in backhand draw and 41 players were good. 90 were excellent in serving skills, 51 were good, and 20 were qualified. 106 volleys were excellent, 40 were good, and 15 were qualified. 110 people in high-voltage technology are excellent; 51 people are good.Figure 10
Skill evaluation chart for tennis students.In the 5G climate, schools and colleges can completely tackle the troubles looked by understudies in the learning system through logical examination of mixed media courseware, joined with an assortment of unique specialized activities, along these lines advancing understudies’ dynamic learning capacity. All the more critically, 5G innovation can change the conventional, single, and abstract assessment strategy for actual training instructing in schools and colleges, subsequently advancing the speed of showing change in actual instruction innovation courses.


## 5. Discussion

The utilization of 5G innovation in training and educating is progressively entering the “incorporation” stage from the “combination” stage. Be that as it may, the ebb and flow assessment research on the combination of data innovation and educating is more than a little flawed, and the cutting edge instructors are generally confused practically speaking. To advance the present circumstance, this exploration joins the assessments of experienced cutting edge instructors and solid specialists in this field and uses logical examination techniques to form the assessment record arrangement of homeroom educating under the climate of data innovation. Furthermore, it positions the significance of each file. It is trusted that such examination results will have directing importance for bleeding edge educators or typical understudies who are going to become instructors. Later on, researchers can keep on investigating how 5G innovation can be actually joined with instruction and educating in principle and practice, so 5G innovation can all the more likely serve training. There is an interaction and mutual influence between human sports behavior and the environment. The person’s actual exercise conduct is the consequence of the person’s drawn out transformation and choice to the outside climate and is the outer indication of changing his own actual practice exercises. School sports climate has many capacities like instruction, correspondence, consolation, and diversion. These capacities assume a significant part in advancing the development of understudies’ actual exercise conduct. Simultaneously, in order to advance the improvement and streamlining of the actual climate of universities and colleges, the actual exercise practice of understudies additionally continually advances necessities for the actual climate of schools and colleges. With the progress of society and the development of science and technology, the influence of the natural environment on the physical exercise behavior of students in the school sports environment has gradually weakened, and the investigation discovered that the counterfeit games’ social climate fundamentally affects the actual exercise conduct of understudies.


## 6. Conclusion

Through the confirmation of models in this paper, the file framework, standard framework, and weight element of the assessment configuration have high dependability and legitimacy, high scientificity, solid operability and practicability, and optimal impact. Therefore, this evaluation truly reflects the quality of the public physical education environment in colleges and universities and provides a reference for the construction of the public physical education environment in colleges and universities. As indicated by the laid out assessment model and assessment standard, this paper conducts experimental examination in three colleges. From the examination results, it shows that the assessment record framework is more sensible and plausible. Simultaneously, the complete score assessment of the actual training showing climate of the three schools and colleges has arrived at a decent level, to create a good atmosphere for spiritual display, and to straighten out the system atmosphere as the starting point to ensure the smooth progress of the training work. Simultaneously, educators and understudies in universities and colleges accept that the actual instruction showing climate in universities and colleges will straightforwardly influence the advancement of actual training instructing, and they are happy with the actual instruction showing climate in schools. Yet, there is a specific hole from the best actual instruction climate. Accordingly, enhancing the actual climate in schools and colleges ought to be founded on advancing the actual climate. Deeply, establishing a decent mental showing climate, and accepting streamlining the institutional climate as the establishment to guarantee the smooth advancement of actual training exercises.



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*Source: 8280468-2022-05-11.xml*