# A Fuzzy Neural Network-Based Evaluation Method for Physical Education Teaching Management in Colleges

Authors: Bo Zhao; Yanjin Liu
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

A novel Fuzzy Neural Network (FNN) teaching quality assessment model of physical education (PE) is presented at colleges and universities to enhance the validity of PE teaching quality evaluation. It is being done to enhance the accuracy of quality evaluations of PE instruction. In the first phase, out of 4 aspects of teaching material, teaching method, teaching attitude, and teaching effect, a multi-index assessment process of university physical education teacher performance based on the analytic hierarchy process (AHP) is created. The effectiveness of college PE instructors is assessed using this approach. The FNN model is used to develop a teaching quality assessment model for college PE courses. The FNN’s parameter is the score data, and the FNN’s output vector is equipped with better college PE (excellent, good, average, and low). In terms of assessing the instructional excellence of PE courses in colleges and universities, FNN has been proven to have superior classification accuracy, specificity, sensitivity, andF1 score when compared to other methods. When compared to other countries, this is the case. The proposed approaches resulted in a score of 96% for accuracy, 95% for specificity, 90% for sensitivity, and an F1 score of 94% for performance. The effectiveness of the proposed approach is shown by comparing the outcomes to those of standard physical education teaching strategies.


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## Body

## 1. Introduction

A new physical education curriculum has been proposed for colleges and universities as a consequence of a recent change in our country’s educational system. There have also been fresh modifications in content, aims, and teaching techniques that have progressively become the focus of modern physical education employees, as well as my country’s present college and university physical education programs. Teaching quality may be further improved if physical education instructors’ subjective initiative is given full play and an assessment and management system is established. After a research study, doing a full assessment as well as statistical approaches such as AHP has been the standard approach to evaluation in the field of physical education for a significant amount of time [1, 2]. Develop and execute a novel teaching approach that is both effective and practical, rather than just following the status quo. It has grown easier to teach physical education with the continued growth of the new curriculum reform, and students’ experiences in class have gotten more enriching. Creating an efficient classroom is one of the primary aims of physical education instructors in today’s classrooms. Figure 1 depicts a teaching and learning approach for physical education. Instructors need to consider the student’s implementation of instructional design choices while deciding on how to continue with the session. If a student is not able to accomplish the work presented or it is too tough, they will not learn the task. Good instructors continually assess what pupils can and cannot do and then modify their teaching accordingly.Figure 1
Physical education teaching and learning model.Social progress has prompted an upsurge in expectations. Being physically and intellectually fit, adjusting to today’s fast-paced environment, and fostering remarkable talents are all vital tasks that universities must take on to integrate with society. In school PE, school physical education is an essential component. All of these subjects are intertwined in the teaching of physical fitness in schools. As a condition for proper school physical education, a well-equipped facility is essential [3, 4]. Physical education departments at regular universities and colleges are becoming a study topic on how to create an education excellence monitoring scheme that functions in line with present circumstances, the educational legislation of development, and the real scenario. This system must work for all of these factors. It has significance in theory and can be put to good use in the real world. This research may also serve as a reference for the development of an excellent control scheme for PE programs at universities and colleges, as determined [5, 6].In colleges and universities, the initial preparation and monitoring techniques used for PE have had several drawbacks. Because of this, it is critical for the discipline’s future enlargement and expansion as well as its worldwide competitiveness to have a rigorous technique for evaluating the excellence of PE teaching at colleges and universities. It is therefore possible to construct an assessment system for PE that is technical, standardized, rational, and successful by drawing on a range of theoretical underpinnings. College sports training units may be monitored to give a theoretical and practical basis. Finding a management model that works best for college and university athletics is a significant division of ensuring the lasting sustainability of physical education. In today’s colleges and universities, developing physical education standards that match the demands of today’s students and cultivating remarkable sports abilities that are in line with those needs is of tremendous theoretical and practical importance. It conducted experiments with people that varied in their goals and motivation, their origins and perspectives, their interpretive and teaching abilities, their sets of skills, and so on. The lack of information concerning criteria and options, as well as the lack of focus during paired comparisons and discussions, provides AHP with the potential for doubt. The order of elements in the data collection does not hold together when choices are added or removed. To examine the efficacy of various knowledge transmission methods, the proposed Fuzzy Neural Network-based evaluation method was used to create an AHP methodology. It has been determined how successful the four methods are by using an AHP-based multi-index assessment system of college PE with experimentally available data on teaching content, teaching technique, teaching attitude, and teaching effect. For collegiate physical education classes, an assessment methodology based on the FNN model is constructed. This data is sent into the FNN, which outputs the quality of a student’s college physical education experience as one of four possible categories: excellent, good, and terrible.


### 1.1. Contribution of This Research

2,263 materials from 2000–2018 that were all about “sport communication” using a Web of Science search.A fuzzy neural networks model is used to develop a teaching quality assessment model for college PE courses.AHP is used to create a multi-index assessment process of university physical education for teacher performance.GoldSA-FNN is used to train dataset initialization for physical education teaching quality evaluation method.The remainder part of this research is structured as Section2-related works with a problem statement, Section 3-suggested methodology, Section 4-result and discussion, and Section 5-conclusion.


## 1.1. Contribution of This Research

2,263 materials from 2000–2018 that were all about “sport communication” using a Web of Science search.A fuzzy neural networks model is used to develop a teaching quality assessment model for college PE courses.AHP is used to create a multi-index assessment process of university physical education for teacher performance.GoldSA-FNN is used to train dataset initialization for physical education teaching quality evaluation method.The remainder part of this research is structured as Section2-related works with a problem statement, Section 3-suggested methodology, Section 4-result and discussion, and Section 5-conclusion.


## 2. Related Works

Flores et al. [7] constructed an index weight that was produced using a pretty precise index method and the conventional AHP. This avoids the problem of indicators seeming excessively tiny due to their disparate weights. To assure the validity of the data, they collect genuine resources based on huge samples. Tripathi et al. [7] and Shahabaz et al. [8] used Bayesian classification, which was followed by an explanation of the naïve classifier and a list of classification instances. These actual data used in the experiment show that categorization presentation is of good quality, accurateness is elevated, and Bayesian classification technology may be employed in teaching assessment. The technique utilized removes direct human factors’ influence, and the technical reference supplied is adequate. The weight determination method proposed by Cao et al. [9] is an AHP, which makes assessment outcomes more rational and scientific as well as proves the method’s usefulness. Proper selection of mathematical approaches in teaching assessment helps assure sensible findings. To enhance their teaching, teachers must comprehend students’ situations in all areas. Li et al. [10] and Li [11] developed comprehensive and easy assessment principles and a college teaching index system based on research findings from different colleges’ evaluations of teaching. This mostly includes 10 criteria in four areas. Weights are calculated using appropriate assessment procedures; focused empirical analysis is performed on chosen samples; and lastly, thorough evaluation findings are provided. Han [12] showed that a scientific assessment of college teaching gives greater criticism, incentive, and direction correspondingly. Using AI, a model for evaluating college PE quality was created. This methodology measures the teacher evaluation index as input and instructional impact as output. Teaching is dynamic, making complete evaluation difficult. As a novel technology, artificial neural networks have nonlinear processing, adaptive learning, and great fault tolerance. Liu et al. [13] illustrated an AI “Neural Network Back-Propagation (BP)” algorithm and stress difficulty is used to evaluate undergraduate education quality. Bai [15] achieved the current state of college students’ English learning (EL) adaptability supported by AI; this study investigates and analyses college students’ EL adaptability supported by AI and proposes strategies to improve students’ adaptability. Zhen and Hu [16] concluded the connection latencies in university English Internet instruction, network difficulties in college English classrooms, college students’ enthusiasm for studying English, and college students’ time spent learning English are eliminated. Song et al. [17] described how convolutional neural networks (CNNs) are used in this research to identify and evaluate the risk of sports medicine disorders using a deep-learning model that incorporates the convolutional neural network. Fang [18] determined the study focused on the creation of a teaching quality assessment model that incorporates machine learning theory as well as research into the preprocessing of evaluation indicators and the building of a support vector machine evaluation model. Liu [19] investigates the English majors’ demands for business English intercultural communicative competence to give a reference for fostering students’ intercultural communicative competence. The estimates reveal that modifications to increase information sharing skills in business English classes are effective, drawing students’ joy, nourishing their intercultural communication skills, and enhancing their ability to conduct business talks. Liu et al. [20] presented a technique in which a node’s transmission preference is calculated via a fuzzy analysis of its mobile and social network neighbors. By collecting and comparing nodes’ transmission preferences, an appropriate message delivery choice can be determined, and the feedback mechanism ensures that data transmission is both steady and sustainable. Kwan and Cai [21] described the four distinct classes of fuzzy neurons and proposed the architecture and learning technique for a four-layer feed-forward fuzzy neural network (FNN). When applied to the recognition of skewed and distorted training patterns, the developed four-layer FNN shows great potential.


## 3. Proposed Methodology

A systematic framework of physical education quality evaluation indicators is needed for objective assessment. To create the teaching index and assessment index, the standard approach for evaluating teaching quality involves two categories: comprehensive teaching content and comprehensive student academic accomplishment. The feasibility, representativeness, and independence of the indicators used in classic evaluation systems are the cornerstones of the above-mentioned types of classic evaluation system indicators. Selecting assessment indicators that are dependable, operative, and scientific is a primary concern when applying the idea of less should not be more. This research builds an AHP-based multi-index assessment system of collegiate PE from 4 dimensions: teaching content, teaching technique, teaching attitude, and teaching effect, which are depicted in Figure2.Figure 2
The quality of physical education is measured by an index.The corpus of relevant literature is rapidly expanding. Unless otherwise specified, the literature covered in this article is current as of July 2018. “The China National Knowledge Infrastructure (CNKI) has 2,863 records” from 1982 to 2018 on “sport communication (tiyuchuanbo)” Based on a Web of Science topic search for “sport communication” between 2000 and 2018, 2,263 documents were found. It is crucial to stay on top of quickly changing literature, not only because new findings emerge from a variety of areas but also because discoveries have the potential to substantially alter communal knowledge, as specified by Wei et al. [22].


### 3.1. Fuzzy Neural Network (FNN)

The FNN theories focus on the expertise and experience of specialists who understand how physical education works in different parameters. The major elements of the FNN strategy are based mostly on expert expertise.(1)aqc=bqc−minpbpcminpbpc−minpbpc.To restore the information to its previous form.(2)bqc=⋃aqc=aqcmaxpbpc−minpbpc+minpbpc.The following is how these data are routed through the FNN. The following operations are done first from the input layer to the hidden state:(3)I˜qls=∑c=1CH˜clsaqc,(4)n˜qls=I˜qls−θ˜Is=Iql1s−θI3s,Iql2s−θI2s,Iql3s−θI1s,(5)s˜qls=11+e−˜cellswhere hsel is the relationship weight among effort node c and hidden-layer node l; l = 1∼L ϴsl.On the output node, the concealed layer’s inputs are gathered.(6)I˜jk=∑l=1Lh˜lk×s˜ql.Then, the network outputzq = zq1, zq2, zq3 is evaluated as(7)Zj=11+e−n˜QZwhere(8)n˜QZ=I˜QZ−θZ˜.All variables and parameters in the suggested method are expressed as FNN or are estimated by them. FNN with a single convolutional whereby all hyperparameters can be fuzzy and complex transition functions are used. We want to hypothetically maximize the quantities of fuzzy parameters without having to solve the neurolinguistic programming (NLP) issue while ensuring that all calculations are included in the fuzzy forecasts. Rather than fuzzifying all variables at once, this study uses an autonomous fuzzification strategy, in which each parameter is fuzzified separately. This research is significant because it is a necessary step in building an exact FNN, as it sets the groundwork for constructing a precise FNN with numerous. We take together networking front components, endpoint side components, and based user components as shown in Figure3.Figure 3
The fuzzy neural network’s architecture.Input, fuzzification, inference, and defuzzification layers make up a fuzzy neural network, as shown in Figure3. The input and output for the bth neuron at the lth layer are denoted as Bb(l) and Ab(l), respectively. The following is a description of each layer’s function.The first layer (input layer): as a result, this layer has a total of 6 nodes. The information processing will not be carried out by the input layer.(9)Ab1=Bb1.The second layer (fuzzification layer): nodes in this layer are tasked with fuzzifying the input variables. Formula (10) and the membership function are used to produce fuzzification (9).(10)Bbs2=Ab1−⋋bs2/σbs2,(11)Abs2=expBbs2+gAb1The third layer (the inference layer) is responsible for matching premise rules and calculating fitness for each rule. Because each node represents a fuzzy rule, there are 162 nodes. The following is a list of each rule’s suitability:(12)Bb3=GnetworkoGterminali(13)Ab3=Bb3⊗YnThe fourth layer (defuzzification layer): we use a weighted average approach to generate the output of a fuzzy neural network.(14)B4=∑b=1162xbBb3,(15)A4=B4∑b=1162Bb3,wherexi denotes connection weight.


### 3.2. Gold SA-FNN is a Model for Assessing the Quality of College PE Teaching

Figure4 depicts the whole process of this study’s FNN-based teaching quality assessment approach for universities’ physical education departments. First, the dataset from the evaluation scheme for PE quality of teaching in universities and colleges is divided into training and test sets using a 4 : 1 ratio. Using the joint nerve optimized FNN model suggested in this paper, a model for assessing college and university physical education teaching is built. Joint nerve optimization optimizes weights and thresholds. For testing, insert it into the FNN model. The following is a step-by-step breakdown of the whole execution process:(i)
Stage 1: college and university PE rating data should be read, separated into training and test sets in a 4 : 1 ratio, and then normalized:(16)Ynew=Sh+y−yminymax−ymin×Sf−ShThere are two datasets:ynew represents the normalized data, and ymin and ymax reflect the lowest and maximum values in the ynew dataset. The normalized dataset has a minimum value (Sh) and a maximum value (Sh), with Sh being set to −1 and Sh being set to 1.(ii)
Stage 2: initialize the fundamental parameters of the joint nerve method, such ash and f, Vmax, P, and Q, establish the FNN network topology, and initialize the weighting and sensitivities.(iii)
Stage 3: use formula (13) to start the population of the joint nerve algorithm, and then use the FNN model to get weights and thresholds to start everyone in each population at the same place.(17)Tk=sfk+rand0,1×gfk−sfk.Tk is the beginning value of the individual, and sfk and lsfk are the upper and lower search limits, respectively, of the k individual.(iv)
Stage 4: calculatey1 and y2 for the joint section coefficients using formulae (14) and (15) as follows:(18)y1=h×1−τ+f×τ,(19)y2=h×τ+f×1−τ.(v)
Stage 5: calculate the optimal fitness value ACC for everyone in the population using formula (16)(20)maxO,uHOO=∑i=1ihooiI,OϵOmax,Cmin,uϵumax,umin,where ACC is the average of theK-fold cross-validation accuracy values, and acck is the average of the k-fold calculation accuracy values.(vi)
Stage 6: individual positions should be updated to reflect.(21)Tkv+1=Tkv×sinj1−j2×sinj1y1×Qvv−y2×Tvv.(vii)
Stage 7: calculate the calculator’s fitness value ACCnew and compare it to the previous generation’s ideal fitness value ACCbest for the members of the population whose positions have been modified. It is necessary to update the best fitness value to this iteration if ACCnew is greater than ACCbest to receive the most current fitness value. ACCbest should also be updated to reflect the user’s current location, but otherwise, it should remain static.(viii)
Stage 8: to establish whether an algorithm has reached its termination condition must check the current number of cyclest to see if it is more than Rmax. If it is, then stop all operations and output an ideal position and an optimal fitness value.(ix)
Stage 9: carry out a quality assessment of physical education instruction at colleges and universities based on the findings of stage 8’s output.Figure 4
Steps for assessment.


## 3.1. Fuzzy Neural Network (FNN)

The FNN theories focus on the expertise and experience of specialists who understand how physical education works in different parameters. The major elements of the FNN strategy are based mostly on expert expertise.(1)aqc=bqc−minpbpcminpbpc−minpbpc.To restore the information to its previous form.(2)bqc=⋃aqc=aqcmaxpbpc−minpbpc+minpbpc.The following is how these data are routed through the FNN. The following operations are done first from the input layer to the hidden state:(3)I˜qls=∑c=1CH˜clsaqc,(4)n˜qls=I˜qls−θ˜Is=Iql1s−θI3s,Iql2s−θI2s,Iql3s−θI1s,(5)s˜qls=11+e−˜cellswhere hsel is the relationship weight among effort node c and hidden-layer node l; l = 1∼L ϴsl.On the output node, the concealed layer’s inputs are gathered.(6)I˜jk=∑l=1Lh˜lk×s˜ql.Then, the network outputzq = zq1, zq2, zq3 is evaluated as(7)Zj=11+e−n˜QZwhere(8)n˜QZ=I˜QZ−θZ˜.All variables and parameters in the suggested method are expressed as FNN or are estimated by them. FNN with a single convolutional whereby all hyperparameters can be fuzzy and complex transition functions are used. We want to hypothetically maximize the quantities of fuzzy parameters without having to solve the neurolinguistic programming (NLP) issue while ensuring that all calculations are included in the fuzzy forecasts. Rather than fuzzifying all variables at once, this study uses an autonomous fuzzification strategy, in which each parameter is fuzzified separately. This research is significant because it is a necessary step in building an exact FNN, as it sets the groundwork for constructing a precise FNN with numerous. We take together networking front components, endpoint side components, and based user components as shown in Figure3.Figure 3
The fuzzy neural network’s architecture.Input, fuzzification, inference, and defuzzification layers make up a fuzzy neural network, as shown in Figure3. The input and output for the bth neuron at the lth layer are denoted as Bb(l) and Ab(l), respectively. The following is a description of each layer’s function.The first layer (input layer): as a result, this layer has a total of 6 nodes. The information processing will not be carried out by the input layer.(9)Ab1=Bb1.The second layer (fuzzification layer): nodes in this layer are tasked with fuzzifying the input variables. Formula (10) and the membership function are used to produce fuzzification (9).(10)Bbs2=Ab1−⋋bs2/σbs2,(11)Abs2=expBbs2+gAb1The third layer (the inference layer) is responsible for matching premise rules and calculating fitness for each rule. Because each node represents a fuzzy rule, there are 162 nodes. The following is a list of each rule’s suitability:(12)Bb3=GnetworkoGterminali(13)Ab3=Bb3⊗YnThe fourth layer (defuzzification layer): we use a weighted average approach to generate the output of a fuzzy neural network.(14)B4=∑b=1162xbBb3,(15)A4=B4∑b=1162Bb3,wherexi denotes connection weight.


## 3.2. Gold SA-FNN is a Model for Assessing the Quality of College PE Teaching

Figure4 depicts the whole process of this study’s FNN-based teaching quality assessment approach for universities’ physical education departments. First, the dataset from the evaluation scheme for PE quality of teaching in universities and colleges is divided into training and test sets using a 4 : 1 ratio. Using the joint nerve optimized FNN model suggested in this paper, a model for assessing college and university physical education teaching is built. Joint nerve optimization optimizes weights and thresholds. For testing, insert it into the FNN model. The following is a step-by-step breakdown of the whole execution process:(i)
Stage 1: college and university PE rating data should be read, separated into training and test sets in a 4 : 1 ratio, and then normalized:(16)Ynew=Sh+y−yminymax−ymin×Sf−ShThere are two datasets:ynew represents the normalized data, and ymin and ymax reflect the lowest and maximum values in the ynew dataset. The normalized dataset has a minimum value (Sh) and a maximum value (Sh), with Sh being set to −1 and Sh being set to 1.(ii)
Stage 2: initialize the fundamental parameters of the joint nerve method, such ash and f, Vmax, P, and Q, establish the FNN network topology, and initialize the weighting and sensitivities.(iii)
Stage 3: use formula (13) to start the population of the joint nerve algorithm, and then use the FNN model to get weights and thresholds to start everyone in each population at the same place.(17)Tk=sfk+rand0,1×gfk−sfk.Tk is the beginning value of the individual, and sfk and lsfk are the upper and lower search limits, respectively, of the k individual.(iv)
Stage 4: calculatey1 and y2 for the joint section coefficients using formulae (14) and (15) as follows:(18)y1=h×1−τ+f×τ,(19)y2=h×τ+f×1−τ.(v)
Stage 5: calculate the optimal fitness value ACC for everyone in the population using formula (16)(20)maxO,uHOO=∑i=1ihooiI,OϵOmax,Cmin,uϵumax,umin,where ACC is the average of theK-fold cross-validation accuracy values, and acck is the average of the k-fold calculation accuracy values.(vi)
Stage 6: individual positions should be updated to reflect.(21)Tkv+1=Tkv×sinj1−j2×sinj1y1×Qvv−y2×Tvv.(vii)
Stage 7: calculate the calculator’s fitness value ACCnew and compare it to the previous generation’s ideal fitness value ACCbest for the members of the population whose positions have been modified. It is necessary to update the best fitness value to this iteration if ACCnew is greater than ACCbest to receive the most current fitness value. ACCbest should also be updated to reflect the user’s current location, but otherwise, it should remain static.(viii)
Stage 8: to establish whether an algorithm has reached its termination condition must check the current number of cyclest to see if it is more than Rmax. If it is, then stop all operations and output an ideal position and an optimal fitness value.(ix)
Stage 9: carry out a quality assessment of physical education instruction at colleges and universities based on the findings of stage 8’s output.Figure 4
Steps for assessment.


## 4. Result and Discussion

An FNN-based methodology for assessing collegiate PE teaching was created. FNN’s output vector is college PE quality, and its learning algorithm is undergraduate scores. FNN is more accurate, specific, and sensitive than other approaches in evaluating college PE courses.For comparison, the proposed method FNN is compared with 4 existing methods.(1)
Deep Neural Network [23].(2)
Artificial Neural Network [24].(3)
Back Propagation Neural Network [25].(4)
“Deep Residual Network-Long Short-term Memory Network” [26].Figure5 shows a comparison chart of the convergence speeds of several methods, showing that DNN has a slower convergence pace and begins to converge after rounds of evolutionary algebra. A chart comparing the convergence speeds of ANN is shown in Figure 6. For adaptability, ANN’s delayed convergence rate starts to pay off after a few rounds.Figure 5
Chart comparing the convergence speeds of DNN.Figure 6
Chart comparing the convergence speeds of ANN.Figure7 illustrates the chart comparing the convergence speeds of BP-NN. After a few rounds, the delayed convergence rate of the BP-NN begins to show its worth in terms of flexibility. Figure 8 is a chart that compares the convergence speeds of several algorithms. According to this chart, the FNN has a higher convergence pace than the other algorithms, and it begins to converge when the number of iterations is equal to 5.Figure 7
Chart comparing the convergence speeds of BP-NN.Figure 8
Chart comparing the convergence speeds of FNN (proposed).Figure9 indicates the accuracy of proposed and existing methods. When a measurement is accurate, it means that it is close to the correct value or standard, but to study PE in colleges and universities, the term more specifically relates to how near a measurement is to its agreed-upon value. The capacity of a test to identify pupils to evaluate their knowledge of PE in colleges and universities using the FNN Model is what constitutes the test's level of specificity depicted in Figure 10.Figure 9
Evaluation of accuracy using suggested and existing methods.Figure 10
Evaluation of specificity using suggested and existing methods.Figure11 depicts the results of a sensitivity analysis, which analyses how college students participating in physical education programs in colleges and universities fare under a certain set of assumptions. The F1 score is calculated as the harmonic mean of the accuracy and recall portions of the test. It is a statistical measurement that is used in the process of rating performance. To put it another way, an F1 score represents an individual’s performance as a mean value. Figure 12 illustrates the F1 score for proposed and existing methods.Figure 11
Evaluation of sensitivity using suggested and existing methods.Figure 12
Evaluation ofF1 score for suggested and existing methods.A WeChat mobile application based on a deep neural network in physical education strategy research approach that integrates academic, experimental, and analytical is used to build the platform, and it becomes low in success rate. Multiple physical test indicators are compared between the control and experimental groups before and after the test to represent the low success of instruction after the modification in this paper’s physical education method. When it comes to achieving physical education’s aims, conventional physical education approaches are lacking in fresh concepts, as determined by Ba and Qi [23]. An existing artificial neural network (ANN) public dataset is used in this study to test a range of automated learning strategies. Artificial neural Networks are ineffective in these tests for learning in physical education colleges and universities as specified by Rivas et al. [24]. Students that go beyond their allotted study time will be penalized according to BP-NN. Using genuine academic data from specific colleges, predictor factors, and neural network settings, this study is predicted to provide a precise and accurate prediction model. There is a lower prediction and detection for physical education as specified by Prasetyawan et al. [25]. Learning may be used to automate student behavior analysis in colleges and universities, allowing instructors to master learning more quickly while providing data support for the following improvement of teaching design and execution. DRN-LSTM [26] is established based on a deep residual network, in which DRN-LSTM can lower in efficiently capture the temporal information of students’ activity. To address these difficulties, we built an FNN-based model for assessing the quality of college physical education training. The FNN takes the score data as an input and produces the college physical education quality level. In analyzing the quality of physical education courses at colleges and universities, FNN is more accurate, specific, sensitive, and F1 score than other techniques.


## 5. Conclusion

Based on the other neural network optimization model, this research suggests a teaching quality assessment model that may be used for topics related to physical education that are taught in colleges and universities. As a result of its high classification accuracy, specificity, and sensitivity, the fuzzy neural network model may be used to evaluate college physical education standards and so is appropriate for usage in academic institutions. We evaluated the accuracy with 96 percent, sensitivity with 90 percent, specificity with 95 percent, and finallyF1 score with 94 percent using the chart comparing convergence speeds for DNN, ANN, BP-NN, and FNN (proposed). Finally, the proposed has a better outcome for convergence speeds. This is because the joint neural network model compares favorably with existing models. The practice of teaching quality assessment generates novel ideas and strategies. However, because the effectiveness of college and university physical education is affected by several variables, the scope of this research is confined to looking at the impact of four first-level variables and twelve second-level variables on the quality assessment of physical education in colleges and universities. In the future, studies will be conducted on a greater number of parameters. The influence of quality assessment on the development of a model that is more accurate and applicable to real-world situations. Based on the results, the fuzzy neural network is superior to previous physical education modeling techniques in terms of effectiveness and overcomes their limitations. The training period of their suggested FNN is longer than that of existing work since it trains and relates to the work of others.



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