# An Evaluation Approach for Physical Education Teaching Practice Quality Using Stochastic Simulation Algorithm

Authors: Changlei Zhou; Tingting Ma
Journal: Mobile Information Systems
 (2022)

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## Abstract

In order to increase the precision of the quality assessment of institution physical education, as well as teaching practice, the assessment system of institution physical education, as well as teaching practice quality, based on stochastic simulation algorithm, is put forward. Then, we construct the constraint parameter model of institution physical education, as well as teaching practice quality assessment, and analyze the hierarchical structure characteristics of institution physical education, as well as teaching practice quality. Subsequently we standardize the data fusion through the developmental characteristic analysis approach of teaching quality assessment and analyze the confidence level of institution physical education, as well as teaching practice quality, in order to construct frequent item set parameters. Moreover, we adopted the combination analysis approach of support and confidence level to analyze the correlation pattern and identify the characteristics, so as to recognize the characteristic analysis of institution physical education, as well as teaching practice quality level. The random simulation and association rule decision-making approaches are implemented to assess the quality level of institution physical education, as well as teaching practice, and the random simulation dynamic detection and the maximum matching degree analysis approach are implemented to assess the quality level of institution physical education, as well as teaching practice. The simulation outcomes confirm that the precision of this approach in evaluating the quality of institution physical education, as well as teaching practice, is considerably high, and the quality level of institution physical education, as well as teaching practice, is significantly increased.


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## Body

## 1. Introduction

The quality assessment system of college sports practice teaching tries to combine students’ teaching assessment data and data mining knowledge with computer technology and establish a complete set of data analysis model of school sports practice teaching. By analyzing the characteristics of college sports practice teaching data and combining data mining algorithm, it can comprehend the assessment and decision-making of institution sports practice teaching, which has a good application value, in particular, in the reform of college sports practice teaching. In the assessment of institution physical education, as well as teaching practice, the original knowledge parameter analysis is adopted to obtain the original knowledge related to the assessment system of classroom teaching quality, and a new assessment system of institution physical education, as well as teaching practice, with new rules is produced, so that the assessment outcomes are more scientific and systematic, and the decisive factors that affect the classroom teaching quality are really found, which can react on teaching in practice, promote the development of institution physical education, as well as teaching practice activities, and increase the management level and decision-making level of institution physical education, as well as teaching practice [1].With the development of the transformation in institution physical education, as well as teaching practice, sophisticated requests are made known for the quality level of institution physical education, as well as teaching practice [2–4]. Combining with the quantitative analysis approaches, in this paper, we (i) appraise the quality of institution physical education, as well as teaching practice, and (ii) build a quantitative regression analysis prototype for the quality of classroom and its assessment of institution physical education, as well as teaching practice, and make quantitative analysis on the quality of institution physical education, as well as teaching practice, through the early big data sampling outcomes. The main purpose is to increase the ability of accurate assessment of institution physical education, as well as teaching practice quality [5]. The research on the assessment approach of physical education practice teaching quality in schools and institutions of higher education has engrossed prodigious consideration. The assessment of institution physical education, as well as teaching practice quality, is based on the analysis of the constraint parameters of institution physical education, as well as teaching practice quality level; through fuzzy identification and statistical information analysis approaches, the integration of institution physical education, as well as teaching practice quality level is processed, and the correlation characteristic detection model of institution physical education, as well as teaching practice quality, assessment is constructed to increase the ability of institution physical education, as well as teaching practice quality assessment [6].The determination of classroom teaching assessment index system, which is the key factor affecting classroom teaching quality in institution physical education, as well as teaching practice assessment, is a very important aspect. It can ensure the objectivity and fairness of institution physical education, as well as teaching practice quality assessment, more fully discover the problems existing in institution physical education, as well as teaching practice, and increase the quality of institution physical education, as well as teaching practice [7]. How to reasonably determine an effective assessment and computation index system of institution physical education, as well as teaching practice, and how to express, analyze, explain, publish, and use the teaching assessment outcomes can stimulate the interest of teachers and pupils, pay attention to and participate in institution physical education, as well as teaching practice assessment, and make institution physical education, as well as teaching practice assessment, play a greater role, which is a new subject worth studying at present. The traditional assessment approach of institution physical education, as well as teaching practice, has indeed played a certain role in teaching reform and classroom teaching quality enhancement. However, due to students’ randomness in the process of grading, students of different majors have different inclinations to different courses, and some human factors interfere; the assessment outcomes of institution physical education, as well as teaching practice, cannot truly replicate the actual teaching effect of college physical education practice [8].Therefore, combining with the actual situation of classroom teaching quality assessment organized by colleges and institutions of higher education, this paper uses the association rules approach of data mining to design the database of the college sports practice teaching assessment and discover the key elements that might potentially disturb the assessment outcomes of college sports practice teaching, so as to deliver a foundation for teachers to upsurge teaching level and classroom teaching quality. Simultaneously, the data mining technology is implemented in the administration system of institution physical education, as well as teaching practice, combined with the big data and fast information processing, to realize the optimization decision of institution physical education, as well as teaching practice assessment [9].This paper puts forward an assessment based index system of institution physical education, as well as teaching practice quality, based on stochastic simulation algorithm. The constraint parameter model of institution physical education, as well as teaching practice quality assessment, is constructed, the hierarchical structure characteristics of institution physical education, as well as teaching practice quality, and integrating the data standardization by the approach of developing characteristic analysis of teaching quality assessment are analyzed, and the level of institution physical education, as well as teaching practice quality, by combining random simulation dynamic detection and maximum matching degree analysis is assessed. To end with, the simulation test analysis and outcomes express the greater performance of this approach in cultivating the assessment ability of institution physical education, as well as teaching practice quality. The major contributions of our research are as given below:(1)
We constructed the constraint parameter model of institution physical education, as well as teaching practice quality assessment, and analyze the hierarchical structure characteristics of institution physical education, as well as teaching practice quality.(2)
We standardized the data fusion through the developmental characteristic analysis approach of teaching quality assessment.(3)
We analyzed the confidence level of institution physical education, as well as teaching practice quality, and constructed frequent item set parameters and adopted the combination analysis approach of support and confidence level to analyze the correlation pattern and identify the characteristics, so as to comprehend the characteristic analysis of institution physical education, as well as teaching practice quality level.The remaining part of this manuscript is systematized in the subsequent manner. In Section2, we deliberate the constrained parameters and model construction of physical education practice teaching quality assessment in schools, academies, and institutions of higher education. In Section 3, an optimization model of the assessment model of institution physical education, as well as teaching practice quality, is suggested. Simulations study and the obtained outcomes are elaborated in Section 4. To finish with, Section 5 completes this article and recommends possible directions for future work.


## 2. Constrained Parameters and Model Design of Physical Education Practice Teaching Quality Assessment in Schools and Institutions of Higher Education

### 2.1. Principles for Selecting Assessment Indexes of Institution Physical Education, as well as Teaching Practice Quality

In order to realize the excellence assessment of institution physical education, as well as teaching practice, the constraint parameter system of institution physical education, as well as teaching practice quality assessment, is constructed by combining adaptive learning algorithm, and the quantitative characteristics of institution physical education, as well as teaching practice quality, are analyzed. The assessment approach and scale of institution physical education, as well as teaching practice quality, are obtained, and the structural expression parameters of bilingual translation are constructed by structure. Through the quantitative index characteristics analysis of institution physical education, as well as teaching practice quality assessment, the big data evolution cluster analysis approach is adopted. To realize the process integration of institution physical education, as well as teaching practice quality assessment, according to certain institution physical education, as well as teaching practice quality objectives, it is a necessary condition for teaching assessment to establish a scientific assessment index or index system [10]. Without proper index or index system, we believe that institution physical education, as well as teaching practice quality assessment, cannot be carried out.The index or index system of physical education practice teaching excellence assessment in schools and institutions of higher education is one or a series of variable names that reflect the quantitative individualities and features of the whole teaching practice. In fact, the assessment index system for physical education practice teaching quality in colleges and institutions of higher education replicates the quantitative features of the whole teaching process, and in fact it has homogeneous measurability and comprehensive quantity.When reflecting the quantitative characteristics of the whole teaching process, the assessment indexes of physical practice teaching quality in colleges and institutions of higher education can be divided into quantitative indexes and quality indexes, which are also called extension indexes and connotation indexes.The establishment of the assessment index system of teaching must meet the following constraints and conditions:(a)
Clarity: the objectives of each index are clear and clearly expressed, which can reflect the overall assessment objectives.(b)
Completeness: under the condition of fully considering each part of the overall assessment goal, each index can partially reflect the overall assessment goal of institution physical education, as well as teaching practice quality, making the whole index system comprehensive and systematic.(c)
Independence: the indexes in the index system are closely linked, both complementary and independent, which makes the index system more suitable for physical education practice teaching quality in colleges and institutions of higher education constitute an organic whole.(d)
Feasibility: each index is practical, and the calculation approach is scientific, clear and standard, completely testable, and as simple and operable as possible [11].


### 2.2. Selection Approach of Assessment Index

An important aspect of the scientificity of the assessment index system, in particular, designed for physical education practice teaching quality in colleges and institutions of higher education is its completeness. In other words, we can say that the assessment index system which is established for the physical education practice teaching in colleges and institutions of higher education must be able to reflect the teaching quality comprehensively and without omission. The selection of assessment index is divided into two stages: (i) the creation of initial index system and (ii) the selection of index system.The first stage is the formation of the initial index system for the education quality. In order to find a comprehensive initial index system, the “affinity diagram” approach in institution physical education, as well as teaching practice management technology, is advisable. First of all, please ask experienced teaching management experts, teachers, and students at the front line of teaching to put forward assessment indicators, and record all the indicators that everyone thinks of one by one. Then, the indicators put forward by students, experts, and teachers are classified into two categories and classified according to the content, so as to reach the initial useable level; that is, they are quite systematic, measurable, comparable, complete, and concise. Finally, the opinions of the three parties are deeply investigated, and therefore the initial index systems for teachers and students are settled up, respectively.The second stage is the selected initial index system. The initial index system questionnaire is distributed to the corresponding experts, teachers, and students, so that they can independently select important indexes as the final assessment indexes of institution physical education, as well as teaching practice.


### 2.3. Constraint Parameters of Physical Education Practice Teaching Quality Assessment in Colleges and Institutions of Higher Education

Using data mining approach, the data set of institution physical education, as well as teaching practice quality, is constructed. According to the data storage format of institution physical education, as well as teaching practice quality distribution database, the objects implemented for mining include transaction, relational, multimedia, and some object-oriented databases, a data warehouse, text data source, and a working World Wide Web [12]. At present, the data sources implemented for data mining are mainly transactional databases, relational databases, data warehouses, and the World Wide Web. By combining and sorting the sample level distribution sequence of the assessment index of institution physical education, as well as teaching practice quality level, and through quantitative regression analysis, the mathematical description of the constraint optimization target of institution physical education, as well as teaching practice quality level assessment, is obtained as follows in the objective function:(1)minFx=f1x,f2x,...,fnxs.t.gix≤0or≥0,i=1,2,…,n,hjx=0,j=1,2,…,m,where fixi=1,2,…,n characterizes the objective function, while gix represents the inequality constraint condition. Similarly, hjx is the correlation statistical constraint condition that formulates the objective. It should be noted that in this paper we introduce the fuzzy detection technology of characteristics distribution of institution physical education, as well as teaching practice, and appraise the quality level of institution physical education, as well as teaching practice.Definition 1.
The dominating set of institution physical education, as well as teaching practice quality assessment,x∗ dominates x, the decision variable of institution physical education, as well as teaching practice quality assessment, which satisfies the following: all fix∗≤fix, and there is at least one and only one fix∗<fjx, where i, j  = 1,2, …, n; at this time, the self-correlated fuzzy state dominating set of institution physical education, as well as teaching practice quality assessment, satisfies local convergence.Definition 2.
The Pareto optimal solution is given as follows. For the discriminant statisticX∗∈S of institution physical education, as well as teaching practice quality level assessment, if and only if there exists a boundary constraint explanation X∈S, entire inequalities fiX∗≤fiX are recognized, such that i=1,2,…,n; within the distribution range of institution physical education, as well as teaching practice quality level, there is one i, which makes the characteristic distribution of institution physical education, as well as teaching practice quality assessment, satisfy the strict inequality fiX∗<fiX. At this time, the statistic X∗ of institution physical education, as well as teaching practice quality level assessment, is a multiobjective optimization problem. By obtaining the Pareto optimal solution of the objective function of institution physical education, as well as teaching practice quality level assessment, the convergence of the assessment model can be satisfied [13].
According to the above definition, the constraint function of institution physical education, as well as teaching practice quality assessment, based on stochastic simulation algorithm model is given. Initialize the characteristic parameters of institution physical education, as well as teaching practice quality level assessment, and revise the redundant vector set in the conclusion, and get the optimal constraint index parameter of institution physical education, as well as teaching practice quality level assessment, asPbest, and the ambiguity function of institution physical education, as well as teaching practice quality level assessment, is determined using(2)Vijg+1=Vijg+c1r1ijgPbestijg−xijg+c2r2ijgGbestjg−xijg.
Setting up a learning model for the assessment of the quality level of institution physical education, as well as teaching practice, through the fuzzy mathematical model, the quantity of nodesN and the amount of vector elements in the distribution of the quality level of institution physical education, as well as teaching practice, and the autocorrelation characteristic distribution vector of the assessment of the quality level of institution physical education, as well as teaching practice, are obtained:(3)xt=x0t,x1t,…,xk−1tT.
Combined with the association rule mining approach, the formal distribution feature set of the association rule mining problem is given, and the weighted vector of the assessment of institution physical education, as well as teaching practice quality level, is obtained using the velocity function of the PSO.(4)Gbestig+1=argPbestijminfPbestijg+1.
It should be noted that, based on load balancing scheduling, a constraint parameter model for appraising the excellence level of institution physical education, as well as teaching practice, is established [14, 15].


### 2.4. Big Data Mining of Institution Physical Education, as well as Teaching Practice Quality Assessment

Based on the analysis approach of developmental characteristics of teaching quality assessment, the hierarchical structure characteristics of institution physical education, as well as teaching practice quality, are analyzed and the data are standardized and fused. The correlation factor betweenXi and Xj of institution physical education, as well as teaching practice quality level characteristics, is described as the similarity between the two characteristic quantities of institution physical education, as well as teaching practice quality level, which can be expressed mathematically as follows:(5)lXi,Xj=Xi−Xj.The distance similarity level indicates the degree of difference in the assessment of institution physical education, as well as teaching practice quality level. Through local convergence learning, the optimized weight subsetWOi=1N−m−a and the fuzzy parameter distribution subset Wfinal=WH,WC,WO of institution physical education, as well as teaching practice quality level assessment, are obtained. After optimizing the distribution structure of institution physical education, as well as teaching practice quality, it is expressed as follows:(6)WOi=1N−m−a=xOii=1N−m−a.Note that ifNf/N<δ, then, by using big data mining technology, the sample attribute set of assessment index of institution physical education, as well as teaching practice quality, is recorded as follows:(7)minimize12w2+C∑i=1nξi+ξi∗subjecttoyi−w′Φxi+b≤ε−ξiw′Φxi+b−yi≤ε−ξi∗ξi,ξi∗≥0,i=1,2,…,n;C>0.On the basis of the collaborative optimization approach, the optimal solution of institution physical education, as well as teaching practice quality fusion, is obtained, and the adaptive weighted expression of institution physical education, as well as teaching practice quality level assessment, is as follows:(8)Kmin=βKpoly+1−βKrbf,β∈0,1,where Kpoly=x⋅xi+12 is the kernel function of the quality control of institution physical education, as well as teaching practice, KRBF=exp−γx−xi2 is the fitness kernel function of the initial assessment of institution physical education, as well as teaching practice quality, and β is the dynamic adjustment weight coefficient of institution physical education, as well as teaching practice quality. If the maximum number of iterations of institution physical education, as well as teaching practice quality assessment, is reached, the optimal value of institution physical education, as well as teaching practice quality level assessment, is expressed mathematically as follows:(9)SwarmBehavior:ifNfN<δ&&Yc<Yi,Xi⟶next=Xi+rand⋅step⋅Xc−XiXc−Xi,elsegotosearch.According to the above description, this paper analyzes the confidence level of institution physical education, as well as teaching practice quality level, constructs frequent item set parameters and samples features, and adopts the combination analysis approach of support and confidence level to analyze correlation patterns and identify features, such as to comprehend the feature analysis of institution physical education, as well as teaching practice quality level [14].


## 2.1. Principles for Selecting Assessment Indexes of Institution Physical Education, as well as Teaching Practice Quality

In order to realize the excellence assessment of institution physical education, as well as teaching practice, the constraint parameter system of institution physical education, as well as teaching practice quality assessment, is constructed by combining adaptive learning algorithm, and the quantitative characteristics of institution physical education, as well as teaching practice quality, are analyzed. The assessment approach and scale of institution physical education, as well as teaching practice quality, are obtained, and the structural expression parameters of bilingual translation are constructed by structure. Through the quantitative index characteristics analysis of institution physical education, as well as teaching practice quality assessment, the big data evolution cluster analysis approach is adopted. To realize the process integration of institution physical education, as well as teaching practice quality assessment, according to certain institution physical education, as well as teaching practice quality objectives, it is a necessary condition for teaching assessment to establish a scientific assessment index or index system [10]. Without proper index or index system, we believe that institution physical education, as well as teaching practice quality assessment, cannot be carried out.The index or index system of physical education practice teaching excellence assessment in schools and institutions of higher education is one or a series of variable names that reflect the quantitative individualities and features of the whole teaching practice. In fact, the assessment index system for physical education practice teaching quality in colleges and institutions of higher education replicates the quantitative features of the whole teaching process, and in fact it has homogeneous measurability and comprehensive quantity.When reflecting the quantitative characteristics of the whole teaching process, the assessment indexes of physical practice teaching quality in colleges and institutions of higher education can be divided into quantitative indexes and quality indexes, which are also called extension indexes and connotation indexes.The establishment of the assessment index system of teaching must meet the following constraints and conditions:(a)
Clarity: the objectives of each index are clear and clearly expressed, which can reflect the overall assessment objectives.(b)
Completeness: under the condition of fully considering each part of the overall assessment goal, each index can partially reflect the overall assessment goal of institution physical education, as well as teaching practice quality, making the whole index system comprehensive and systematic.(c)
Independence: the indexes in the index system are closely linked, both complementary and independent, which makes the index system more suitable for physical education practice teaching quality in colleges and institutions of higher education constitute an organic whole.(d)
Feasibility: each index is practical, and the calculation approach is scientific, clear and standard, completely testable, and as simple and operable as possible [11].


## 2.2. Selection Approach of Assessment Index

An important aspect of the scientificity of the assessment index system, in particular, designed for physical education practice teaching quality in colleges and institutions of higher education is its completeness. In other words, we can say that the assessment index system which is established for the physical education practice teaching in colleges and institutions of higher education must be able to reflect the teaching quality comprehensively and without omission. The selection of assessment index is divided into two stages: (i) the creation of initial index system and (ii) the selection of index system.The first stage is the formation of the initial index system for the education quality. In order to find a comprehensive initial index system, the “affinity diagram” approach in institution physical education, as well as teaching practice management technology, is advisable. First of all, please ask experienced teaching management experts, teachers, and students at the front line of teaching to put forward assessment indicators, and record all the indicators that everyone thinks of one by one. Then, the indicators put forward by students, experts, and teachers are classified into two categories and classified according to the content, so as to reach the initial useable level; that is, they are quite systematic, measurable, comparable, complete, and concise. Finally, the opinions of the three parties are deeply investigated, and therefore the initial index systems for teachers and students are settled up, respectively.The second stage is the selected initial index system. The initial index system questionnaire is distributed to the corresponding experts, teachers, and students, so that they can independently select important indexes as the final assessment indexes of institution physical education, as well as teaching practice.


## 2.3. Constraint Parameters of Physical Education Practice Teaching Quality Assessment in Colleges and Institutions of Higher Education

Using data mining approach, the data set of institution physical education, as well as teaching practice quality, is constructed. According to the data storage format of institution physical education, as well as teaching practice quality distribution database, the objects implemented for mining include transaction, relational, multimedia, and some object-oriented databases, a data warehouse, text data source, and a working World Wide Web [12]. At present, the data sources implemented for data mining are mainly transactional databases, relational databases, data warehouses, and the World Wide Web. By combining and sorting the sample level distribution sequence of the assessment index of institution physical education, as well as teaching practice quality level, and through quantitative regression analysis, the mathematical description of the constraint optimization target of institution physical education, as well as teaching practice quality level assessment, is obtained as follows in the objective function:(1)minFx=f1x,f2x,...,fnxs.t.gix≤0or≥0,i=1,2,…,n,hjx=0,j=1,2,…,m,where fixi=1,2,…,n characterizes the objective function, while gix represents the inequality constraint condition. Similarly, hjx is the correlation statistical constraint condition that formulates the objective. It should be noted that in this paper we introduce the fuzzy detection technology of characteristics distribution of institution physical education, as well as teaching practice, and appraise the quality level of institution physical education, as well as teaching practice.Definition 1.
The dominating set of institution physical education, as well as teaching practice quality assessment,x∗ dominates x, the decision variable of institution physical education, as well as teaching practice quality assessment, which satisfies the following: all fix∗≤fix, and there is at least one and only one fix∗<fjx, where i, j  = 1,2, …, n; at this time, the self-correlated fuzzy state dominating set of institution physical education, as well as teaching practice quality assessment, satisfies local convergence.Definition 2.
The Pareto optimal solution is given as follows. For the discriminant statisticX∗∈S of institution physical education, as well as teaching practice quality level assessment, if and only if there exists a boundary constraint explanation X∈S, entire inequalities fiX∗≤fiX are recognized, such that i=1,2,…,n; within the distribution range of institution physical education, as well as teaching practice quality level, there is one i, which makes the characteristic distribution of institution physical education, as well as teaching practice quality assessment, satisfy the strict inequality fiX∗<fiX. At this time, the statistic X∗ of institution physical education, as well as teaching practice quality level assessment, is a multiobjective optimization problem. By obtaining the Pareto optimal solution of the objective function of institution physical education, as well as teaching practice quality level assessment, the convergence of the assessment model can be satisfied [13].
According to the above definition, the constraint function of institution physical education, as well as teaching practice quality assessment, based on stochastic simulation algorithm model is given. Initialize the characteristic parameters of institution physical education, as well as teaching practice quality level assessment, and revise the redundant vector set in the conclusion, and get the optimal constraint index parameter of institution physical education, as well as teaching practice quality level assessment, asPbest, and the ambiguity function of institution physical education, as well as teaching practice quality level assessment, is determined using(2)Vijg+1=Vijg+c1r1ijgPbestijg−xijg+c2r2ijgGbestjg−xijg.
Setting up a learning model for the assessment of the quality level of institution physical education, as well as teaching practice, through the fuzzy mathematical model, the quantity of nodesN and the amount of vector elements in the distribution of the quality level of institution physical education, as well as teaching practice, and the autocorrelation characteristic distribution vector of the assessment of the quality level of institution physical education, as well as teaching practice, are obtained:(3)xt=x0t,x1t,…,xk−1tT.
Combined with the association rule mining approach, the formal distribution feature set of the association rule mining problem is given, and the weighted vector of the assessment of institution physical education, as well as teaching practice quality level, is obtained using the velocity function of the PSO.(4)Gbestig+1=argPbestijminfPbestijg+1.
It should be noted that, based on load balancing scheduling, a constraint parameter model for appraising the excellence level of institution physical education, as well as teaching practice, is established [14, 15].


## 2.4. Big Data Mining of Institution Physical Education, as well as Teaching Practice Quality Assessment

Based on the analysis approach of developmental characteristics of teaching quality assessment, the hierarchical structure characteristics of institution physical education, as well as teaching practice quality, are analyzed and the data are standardized and fused. The correlation factor betweenXi and Xj of institution physical education, as well as teaching practice quality level characteristics, is described as the similarity between the two characteristic quantities of institution physical education, as well as teaching practice quality level, which can be expressed mathematically as follows:(5)lXi,Xj=Xi−Xj.The distance similarity level indicates the degree of difference in the assessment of institution physical education, as well as teaching practice quality level. Through local convergence learning, the optimized weight subsetWOi=1N−m−a and the fuzzy parameter distribution subset Wfinal=WH,WC,WO of institution physical education, as well as teaching practice quality level assessment, are obtained. After optimizing the distribution structure of institution physical education, as well as teaching practice quality, it is expressed as follows:(6)WOi=1N−m−a=xOii=1N−m−a.Note that ifNf/N<δ, then, by using big data mining technology, the sample attribute set of assessment index of institution physical education, as well as teaching practice quality, is recorded as follows:(7)minimize12w2+C∑i=1nξi+ξi∗subjecttoyi−w′Φxi+b≤ε−ξiw′Φxi+b−yi≤ε−ξi∗ξi,ξi∗≥0,i=1,2,…,n;C>0.On the basis of the collaborative optimization approach, the optimal solution of institution physical education, as well as teaching practice quality fusion, is obtained, and the adaptive weighted expression of institution physical education, as well as teaching practice quality level assessment, is as follows:(8)Kmin=βKpoly+1−βKrbf,β∈0,1,where Kpoly=x⋅xi+12 is the kernel function of the quality control of institution physical education, as well as teaching practice, KRBF=exp−γx−xi2 is the fitness kernel function of the initial assessment of institution physical education, as well as teaching practice quality, and β is the dynamic adjustment weight coefficient of institution physical education, as well as teaching practice quality. If the maximum number of iterations of institution physical education, as well as teaching practice quality assessment, is reached, the optimal value of institution physical education, as well as teaching practice quality level assessment, is expressed mathematically as follows:(9)SwarmBehavior:ifNfN<δ&&Yc<Yi,Xi⟶next=Xi+rand⋅step⋅Xc−XiXc−Xi,elsegotosearch.According to the above description, this paper analyzes the confidence level of institution physical education, as well as teaching practice quality level, constructs frequent item set parameters and samples features, and adopts the combination analysis approach of support and confidence level to analyze correlation patterns and identify features, such as to comprehend the feature analysis of institution physical education, as well as teaching practice quality level [14].


## 3. Optimization of the Assessment Model of Institution Physical Education, as well as Teaching Practice Quality

### 3.1. Quantitative Analysis of the Quality of Institution Physical Education, as well as Teaching Practice

The fuzzy correlation constraint approach is implemented to dynamically assess the quality of institution physical education, as well as teaching practice, and the fuzzy correlation constraint parameter model of institution physical education, as well as teaching practice quality, is extracted, and the descriptive statistical sequence between quality distribution statesXi and Xj is obtained as xt0+iΔt, i=0,1,…,N−1, and the optimization model of dynamic assessment of institution physical education, as well as teaching practice quality, is as follows:(10)X=s1,s2,…sKn=xn,xn−τ,…,xn−m−1τ.Conjoining the approaches that are used for parameter analysis and for panel parameter exploration, this paper identifies the fuzzy parameters of institution physical education, as well as teaching practice quality assessment, and optimizes the weight subsetWOi=1N−m−a, and the characteristic subset of institution physical education, as well as teaching practice quality distribution, is Wfinal=WH,WC,WO. Bestowing to the fuzzy feature distribution set of institution physical education, as well as teaching practice quality assessment, the fuzzy state feature distribution subset can be expressed as follows:(11)WOi=1N−m−a=xOii=1N−m−a.IfNf/N<δ, xOi is implemented to indicate the perceived range of institution physical education, as well as teaching practice quality assessment, and visual indicates the maximum iteration number of institution physical education, as well as teaching practice quality assessment, so that fsi=fx1,fx2,…,fxn, the fuzzy subspace scheduling prototype for assembling and establishing an effective institution physical education, as well as teaching practice quality level distribution, is Pni=pk|prkj=1,k=1,2,…,m, and the index sample attribute set of institution physical education, as well as teaching practice quality assessment, is recorded as follows:(12)λ=11+α∂S/∂t2k^μt+1=k^μt+Qt+1×∂F^μ/Mg∂t−∂S∂tk^μt.Based on the two values of support and confidence, a fuzzy information fusion model is established to assess the quality of institution physical education, as well as teaching practice, and the adaptive weighted expression of institution physical education, as well as teaching practice quality assessment, is obtained using the two following calculation formulas:(13)fTat=12πσt−ta0e−lnt−ta0−μ2/2σ2,t>ta0>0,0,亷余.(14)FTt=∫ta0t12πσt−ta0e−lnt−ta0−μ2/2σ2dt.Let the membership degree of the first class of the current concept set of physical education practice teaching quality distribution in colleges and institutions of higher education be recorded asCF=F,Q,n,RT1,RT2,RW. If it reaches the maximum iteration times of particle swarm optimization, the optimal value of physical education practice teaching excellence assessment in schools and institutions of higher education can be obtained, and the fuzzy scheduling function of physical education practice teaching excellence assessment in schools and institutions of higher education can be obtained using the three following equations, respectively:(15)pjt+1=a1pjt+a2pgta1+a2,(16)mbestt+1=1n∑j=1npjt,(17)Xjt+1=pjt+1±β×mbestt+1−Xjt×ln1ujt+1.In the above equations,Xjt is the fuzzy rule set of institution physical education, as well as teaching practice quality assessment, after the titeration [13, 16].


### 3.2. Quantitative Assessment of Physical Education Practice Teaching Quality in Schools and Institutions of Higher Education

The random simulation and association rule decision-making approaches are implemented to assess the quality level of institution physical education, as well as teaching practice [17]. Combined with random simulation dynamic detection and maximum matching analysis approach, the adaptive optimization model of institution physical education, as well as teaching practice quality level, is as follows [18, 19]:(18)Mv=w1∑i=1m×nHi−Si+Mhw2∑i=1m×nSi−Vi+w3∑i=1m×nVi−Hi.In (18), note that the distribution function of the association rules for institution physical education, as well as teaching practice quality assessment, is Mh, and the assessment model of institution physical education, as well as teaching practice quality, is given by(19)GD=∑i=1ndi2n.Whendi  = 0, the convergence formula of institution physical education, as well as teaching practice quality assessment, is mathematically expressed as follows:(20)DM=de+db+∑i=1n−1di−∑i−1n−1di/n−1de+db+n−1∑i=1n−1di/n−1,where de is the extreme point in the quality distribution set Ss of institution physical education, as well as teaching practice, and db is the edge dynamic optimization function. To sum up, combined with the random simulation dynamic detection and maximum matching analysis approach, the quality level of the institution physical education, as well as teaching practice, is appraised [20, 21].


## 3.1. Quantitative Analysis of the Quality of Institution Physical Education, as well as Teaching Practice

The fuzzy correlation constraint approach is implemented to dynamically assess the quality of institution physical education, as well as teaching practice, and the fuzzy correlation constraint parameter model of institution physical education, as well as teaching practice quality, is extracted, and the descriptive statistical sequence between quality distribution statesXi and Xj is obtained as xt0+iΔt, i=0,1,…,N−1, and the optimization model of dynamic assessment of institution physical education, as well as teaching practice quality, is as follows:(10)X=s1,s2,…sKn=xn,xn−τ,…,xn−m−1τ.Conjoining the approaches that are used for parameter analysis and for panel parameter exploration, this paper identifies the fuzzy parameters of institution physical education, as well as teaching practice quality assessment, and optimizes the weight subsetWOi=1N−m−a, and the characteristic subset of institution physical education, as well as teaching practice quality distribution, is Wfinal=WH,WC,WO. Bestowing to the fuzzy feature distribution set of institution physical education, as well as teaching practice quality assessment, the fuzzy state feature distribution subset can be expressed as follows:(11)WOi=1N−m−a=xOii=1N−m−a.IfNf/N<δ, xOi is implemented to indicate the perceived range of institution physical education, as well as teaching practice quality assessment, and visual indicates the maximum iteration number of institution physical education, as well as teaching practice quality assessment, so that fsi=fx1,fx2,…,fxn, the fuzzy subspace scheduling prototype for assembling and establishing an effective institution physical education, as well as teaching practice quality level distribution, is Pni=pk|prkj=1,k=1,2,…,m, and the index sample attribute set of institution physical education, as well as teaching practice quality assessment, is recorded as follows:(12)λ=11+α∂S/∂t2k^μt+1=k^μt+Qt+1×∂F^μ/Mg∂t−∂S∂tk^μt.Based on the two values of support and confidence, a fuzzy information fusion model is established to assess the quality of institution physical education, as well as teaching practice, and the adaptive weighted expression of institution physical education, as well as teaching practice quality assessment, is obtained using the two following calculation formulas:(13)fTat=12πσt−ta0e−lnt−ta0−μ2/2σ2,t>ta0>0,0,亷余.(14)FTt=∫ta0t12πσt−ta0e−lnt−ta0−μ2/2σ2dt.Let the membership degree of the first class of the current concept set of physical education practice teaching quality distribution in colleges and institutions of higher education be recorded asCF=F,Q,n,RT1,RT2,RW. If it reaches the maximum iteration times of particle swarm optimization, the optimal value of physical education practice teaching excellence assessment in schools and institutions of higher education can be obtained, and the fuzzy scheduling function of physical education practice teaching excellence assessment in schools and institutions of higher education can be obtained using the three following equations, respectively:(15)pjt+1=a1pjt+a2pgta1+a2,(16)mbestt+1=1n∑j=1npjt,(17)Xjt+1=pjt+1±β×mbestt+1−Xjt×ln1ujt+1.In the above equations,Xjt is the fuzzy rule set of institution physical education, as well as teaching practice quality assessment, after the titeration [13, 16].


## 3.2. Quantitative Assessment of Physical Education Practice Teaching Quality in Schools and Institutions of Higher Education

The random simulation and association rule decision-making approaches are implemented to assess the quality level of institution physical education, as well as teaching practice [17]. Combined with random simulation dynamic detection and maximum matching analysis approach, the adaptive optimization model of institution physical education, as well as teaching practice quality level, is as follows [18, 19]:(18)Mv=w1∑i=1m×nHi−Si+Mhw2∑i=1m×nSi−Vi+w3∑i=1m×nVi−Hi.In (18), note that the distribution function of the association rules for institution physical education, as well as teaching practice quality assessment, is Mh, and the assessment model of institution physical education, as well as teaching practice quality, is given by(19)GD=∑i=1ndi2n.Whendi  = 0, the convergence formula of institution physical education, as well as teaching practice quality assessment, is mathematically expressed as follows:(20)DM=de+db+∑i=1n−1di−∑i−1n−1di/n−1de+db+n−1∑i=1n−1di/n−1,where de is the extreme point in the quality distribution set Ss of institution physical education, as well as teaching practice, and db is the edge dynamic optimization function. To sum up, combined with the random simulation dynamic detection and maximum matching analysis approach, the quality level of the institution physical education, as well as teaching practice, is appraised [20, 21].


## 4. Simulations, Tests, and Results

In order to study and examine the application performance of the suggested approach established for the assessment of institution physical education, as well as teaching practice quality, a simulation experiment is conducted. It is assumed that the sample number of statistical information of institution physical education, as well as teaching practice quality, is 3000, and the training sample number is 200 [22]. The minimum window distribution of fuzzy learning of institution physical education, as well as teaching practice quality, is Wmin=0.4, the maximum window threshold Wmax=0.9, the minimum similarity coefficient Cmin=1.5, and the maximum similarity coefficient is Cmax=2.0. The sample data of institution physical education, as well as teaching practice quality level, are normalized, and the probability of ambiguity cross distribution of college physical education practice courses is 0.1 ⟶ 0.3. Rendering to the aforementioned parameter settings, the statistical study data of institution physical education, as well as teaching practice quality, are exposed in Table 1.Table 1
The statistical examination of physical education practice teaching quality in schools and institutions of higher education.
AgePractical teaching inputPractical classroom teaching effect of physical educationStudent satisfaction levelRegression analysis value20110.6190.8880.6350.09420120.5290.7210.3020.29120130.1710.7900.6950.55020140.2800.1930.4730.88320150.6360.4150.2680.21220160.4210.4750.2720.40420170.6630.1040.2990.99820180.7440.8280.9880.35020190.3760.1890.7290.61520200.0990.6570.1850.46820210.1590.4920.7140.169According to the above regression analysis and statistical analysis outcomes, in association rules, the rules generated underneath the background of support confidence can be distributed into four categories: (i) the first category is the operational association rules, that is, instructions that users are interested in; (ii) the second category is redundancy rules; (iii) the third category is weak correlation rules; and (iv) the fourth category is secondary correlation rules [23]. The support-confidence framework is implemented to generate association rules. Moreover, whether the support-matching structure can mine the operational rules and decrease the creation of weak association rules are compared with each other. There are 22 attributes in the database, and each attribute can take several attribute values. In this database, each record contains 23 values, of which the first 22 are attribute values, and the last one is judgment value, and the judgment values are comestible and venomous, respectively. The determination of the association rule mining is to discover which characteristic values will clue to the comestible or venomous appearance. The following parameters are set to run the experiments.In the structure of support confidence, support = 0.4 and confidence = 0.7. Moreover, in the structure of support matching, support = 0.4 and matching = 0.6. Here, we take out two rules from the rules generated by the two approaches to make a comparison. First of all, look at this rule: (free, white, partial, one, no) = poisonous. The antecedents of the rule are the attribute value and judgment value. Confidence = 0.772 and sup = 0.401 in the framework of support confidence; match = 0.603 and sup = 0.401 in the framework of support matching, so the two approaches can also mine effective association rules. Look at the following rule: (white, partial, no) ⟶ toxic. Under the framework of support confidence, confidence = 0.723 and sup = 0.406. However, under the framework of support-matching degree, match = 0.726–0.175 = 0.551, which does not meet the minimum matching degree, so it is a weak correlation rule [24]. From a mathematical point of view, the correlation between the item sets is p (white, partial, no, toxic) − p (white, partial, no) × p (toxic). It can be seen that this rule is indeed a rule with poor correlation. Experimental outcomes confirm that the support-matching structure can not only mine the operational association rules but also diminish the generation of weak association instructions. In this way, it can be ensured that the instructions delivered to users have a great correlation, and thus the sample data output of institution physical education, as well as teaching practice quality assessment, is shown in Figure 1.Figure 1
Sample data of teaching quality assessment of sports practice courses. (a) Test object 1. (b) Test object 2.
(a)(b)Observing the data, as revealed in Figure1, as the input means merging the data from multiple data sources and normalizing the data, when mining the association analysis of students’ assessment, logical data is needed, so the data of students’ assessment form should be converted into Boolean representation, so as to assess the teaching quality of sports practice courses, and the assessment outcomes are shown in Figure 2.Figure 2
Assessment outcomes of teaching quality of sports practice courses.According to the analysis of Figure2, if the minimum confidence is set to 0.6 according to the assessment system of support and confidence, all the above association rules meet the conditions. However, if the assessment system of support and matching is implemented, the assessment of teaching quality is the best when the minimum matching degree min_match = 0.6. Test the reliability of teaching quality assessment by different approaches, and the association outcomes are given away in Figure 3. By closely analyzing the view of Figure 3, we know that the precision of college physical practice teaching quality assessment by this approach is high, and the quality level of college physical practice teaching is increased [25].Figure 3
Comparison of reliability test of teaching quality assessment.


## 5. Conclusions and Future Work

In this paper, the assessment system of institution physical education, as well as teaching practice quality, based on stochastic simulation algorithm is put forward. A quantitative regression analysis prototype for the eminence assessment of institution physical education, as well as teaching practice, is constructed, a quantitative analysis of institution physical education, as well as teaching practice quality, is carried out, the ability of accurate assessment of institution physical education, as well as teaching practice quality, is increased, and the horizontal distribution sequence of assessment index samples of institution physical education, as well as teaching practice quality, is combined and sorted. On the basis of the fuzzy mathematical model, a constraint parameter prototype for appraising the excellence level of institution physical education, as well as teaching practice, is established. Through the collaborative optimization approach, the optimal solution of institution physical education, as well as teaching practice quality fusion, is obtained, and the fuzzy information fusion model is established to gauge the excellence of institution physical education, as well as teaching practice. The random simulation and association rule decision approach are adopted to measure the eminence level of institution physical education, as well as teaching practice. The analysis shows that this approach has high precision and convergence in evaluating the quality level of institution physical education, as well as teaching practice.On the foundation of the research conducted in this paper, the assessment standard system of generating association rules and the algorithm of generating maximum frequent sets are basically realized to increase the assessment efficiency of institution physical education, as well as teaching practice quality. Furthermore, the application of data mining technology in college classroom for teaching quality and its appropriate assessment is preliminarily realized. However, there are still some open issues that need the researchers’ consideration and need to be studied further in the continuing part of this work.To begin with, in order to increase the mining quality of association rules in institution physical education, as well as teaching practice quality assessment, we should not only consider the efficiency of assessment system and association rule algorithm, because the usefulness of an assessment rule of institution physical education, as well as teaching practice quality, ultimately depends on the user’s feeling, and only the user can decide the validity and feasibility of the rule. Therefore, we should increase the system factors and combine the needs of users at the same time. For example, when mining data, some constraints can be artificially added, which can reduce the amount of data implemented by data mining algorithms and increase the quality of data. Moreover, in the research on the mining outcomes of association rules of institution physical education, as well as teaching practice quality assessment, it is found that there are still errors in the generated rules. The reason is that the five-level classification of the assessment data has not been realized, and the data set implemented may not be the best data set, which needs further research and investigation that we should account for in the future.



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*Source: 3103609-2022-08-29.xml*