# Construction of Online Teaching Mode of College Sports Based on Neural Network Technology

Authors: Qiao Chen; Jun Xia; Ping He
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

Online teaching is becoming familiar in recent days for bridging the educational gap and long-distance coverage. The online teaching modes provide better visualization and representation of different subjects, including sports and physical education classes. Data-to-representation provides a better teaching model without interrupting the knowledge-based performance. For leveraging this feature, this article introduces a Conveyance-dependent Teaching Mode (CTM) paradigm. The proposed paradigm employs neural learning to improve the performance of different online sports sessions. This learning assigns rewards for different training modes based on student assessment and data representation. The flawless data and student performance assessments are balanced for improving teaching modes. The reward is updated using the sports curriculum, teacher recommendations, and student interests. Based on the assessment, the rewards are identified using the activation function. This generates the rewards for different sports training sessions improving the data accuracy. Further metrics such as representation, analysis rate, and time factors are verified to improve the performance of the proposed paradigm.


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## Body

## 1. Introduction

Online teaching modes provide help and advice to the students by teachers via Internet services. Various online teaching modes are available for both school and college students. An online teaching mode improves communication skills, language skills, and interest and provides a better understating of subjects [1]. Online teaching modes encourage self-regulated learning that improves the capabilities of students. Modes such as reading, auditory, kinesthetic, and visual are available for online teaching. Reading modes focus on the reading and writing of students by proving proper presentations and worksheets via Internet services [2]. The auditory model mainly focuses on the oral skills that produce a certain set of data for the learners [3]. Kinesthetic modes provide advice and information to develop senses and decision-making processes for the students. Visual modes provide information for the students giving proper visual effects and details. Teachers first set up a working station and notes to teach students via online teaching modes. Teachers provide various sets of notes and worksheets for college students that maximize their understanding skills of students [4, 5].Sports education uses an instructional model and curriculum improving the physical education skills of students. Online sports education provides various services and teaching techniques for sports education students [6]. The main aim of sports education is to improve the social interaction and communication skills of students. Online sports teaching provides various sports education models (SEM) for the students. Online sports teaching models are the most effective strategy that is used to improve the performance rate of sports education [7]. Online teaching modes such as prerecorded video lectures, online whiteboards, interaction sessions, presentations, and flipped classrooms are available. Teachers need many materials and skills to interact with the students via an online connection [8]. Teachers schedule certain things to improve student skills over sports. Teachers set the goals and conduct online training sessions for the students. The backpropagation (BP) technique is mostly used for online sports teaching processes that analyze the performance rate of sports education students. BP evaluates the physical skills of students that produce a feasible set of data for the online teaching process. BP improves the effectiveness and feasibility of the online sports education system [9, 10].A neural network (NN) also known as an artificial neural network (ANN) is a set of algorithms that are used to analyze the relationship among the dataset. NN mimics the human brain to perform particular tasks in an application [11]. NN is widely used for the online sports education system. NN improves the overall performance rate and reliability of the online sports education system. NN is used in the online sports education system to identify the patterns and features that produce an optimal set of data for the teaching process [12]. Various NN techniques are available for the online sports education system. The BP neural network approach is commonly used in the sports education system. The feature extraction process is used in BP to find out the important features that are presented in the education system [13]. Extracted data are used as an input for the classification process. The classification process improves the feasibility rate of the online sports education system. The convolutional neural network (CNN) approach is also used in the online sports education system. Segmentation and identification process are used in CNN classifying the important patterns for the teaching process. The segmentation process identifies the features and produces extracted features for the teachers. Teachers utilize the data to improve teaching and skills that enhance the efficacy of the online sports education system [14, 15]. Based on the above discussion, the major contribution of the work is enlisted as follows:(i)
Design and development of CTM paradigm aim to improve the performance of different online sports sessions(ii)
Assessment is based on different training modes in correlation with student assessment and data representation(iii)
An effective activation function has been developed to produce flawless data and student performance assessments for improving teaching modes(iv)
Numerical comparison has been made based on sports curriculum, teacher recommendations, and student interests


### 1.1. Related Works

W. H. Kim and J. H. Kim [16] introduced an individualized artificial intelligence (AI) tutor using a developmental learning network (DLN) for the education system. DLN is used here to identify the learning channels of students that provide necessary information for further process. Both sequence and frequency are used in DLN to classify the preference of learners. AI is used here to maximize the understanding capabilities of learners in the learning process. The proposed individualized AI tutor method improves the performance and learning skills of learners.Arciniegas et al. [17] proposed an artificial-intelligence- (AI-) based student monitoring system for the learning environment in the education system. A recurrent neural network (RNN) algorithm is used here to identify the facial expression of students while teaching. RNN provides an appropriate set of data to AI that enhance the teaching methods and techniques for students. The proposed monitoring system is mostly used in various educational environments that provide the necessary set of data for teachers. The proposed monitoring system maximizes the efficiency and performance rate of students.Kastrati et al. [18] proposed a new automatic supervised framework for aspect-based sentiment analysis processes on the massive open online course (MOOC). In MOOC, weakly supervised annotation-based information is stored providing optimal detail about weak students. The proposed method gets information from weakly supervised annotation and finds out the information regarding students. The proposed framework produces a feasible set of data for the sentiment analysis process that reduce the latency rate in the identification process. The proposed framework improves the effectiveness and performance of the system.Duan [19] introduced an augmented reality (AR) technology-based online volleyball remote teaching system. AR captures the volleyball coach’s techniques and skills and then stores that in computer storage space. Both hardware and software provide necessary services to online sports learners. The captured data are sent to monitor with a certain set of tools and equipment. Software modules provide data to online learners that enhance the efficiency of the online teaching system. The proposed teaching system improves the skills and performance rate of sports education students.Camiré et al. [20] proposed a validation method for coaching life skills in sports questionnaires (CLSS-Q). The proposed method is mainly used to validate the coaching techniques of coaches that provide an appropriate set of data for the questionnaire. CLSS-Q conducts validation and a developmental process that identifies the important features and patterns of coaches. The proposed method improves the reliability and feasibility of CLSS-Q, which improves the sports skills of students.Xie [21] introduced a real-time monitoring system for big data sports teaching systems. The big data analysis process provides an appropriate set of data for a monitoring system. Big data is used here to manage a large amount of data that are captured by the sports teaching system. Wearable devices and gadgets are used in a real-time monitoring system that produces an actual set of data which are related to sports students. The proposed method improves the efficiency and feasibility of the monitoring system.Liu et al. [22] proposed a decision support system using artificial intelligence (AI) technology for the 5G network assessment process. AI is used here to provide various techniques to improve the teaching, learning, reading, and writing skills of students. The 5 G network assessment process is a complicated task to perform improving the educational capabilities of students via an Internet connection. The proposed method maximizes the accuracy rate in a decision-making process that enhances the performance rate of the system.Zhang et al. [23] introduced a deep-belief-network- (DBN-) based learning style classification approach for the large-scale online education system. The learning style model is implemented here to identify the patterns and styles of learning based on previously recorded data. DBN is used in the proposed approach to find out the learning styles of individual students that provide an optimal set of data for the further teaching process. The proposed method achieves a high accuracy rate in learning style classification that enhances the performance of the online education system.Fang [24] proposed a new analysis method for the backpropagation (BP) neural network model for school physical education management systems. A fuzzy c-means algorithm is used here to analyze the information that is stored in a management system. The fusion method is used here to find out the characteristics and features that are presented in sports physical data. BP improves the effectiveness rate of the fusion method that provides an optimal set of data for an information management system. The proposed analysis process improves the feasibility and efficiency of the physical sports information management system.Zou [25] introduced a sports development strategy based on web technology and a data mining approach. Both web technology and data mining are used here to improve the performance rate in the sports physical education system. Data mining is used here to analyze the important set of data that is necessary for the sports development process. The reconstruction algorithm is implemented in the proposed method to eliminate unnecessary noise and data from the analysis process. Experimental results show that the proposed method enhances the effectiveness and reliability of the sports physical education system.Bao and Yu [26] proposed a quality evaluation method for hybrid physical online and offline systems. The proposed method is mainly used for the evaluation process that evaluates the quality of teaching techniques which are necessary to improve students’ sports skills. A fuzzy comprehensive evaluation algorithm is used here to evaluate the quality of teaching indexes and produce a proper set of data for the evaluation process. The proposed method achieves a better accuracy rate in an evaluation process that enhances the efficiency and quality of hybrid online and offline teaching systems.Li et al. [27] introduced a new strategy to improve football teaching techniques by using the artificial intelligence (AI) approach. The proposed method is mainly used to identify the quality of football teaching techniques that are provided to sports students. AI is used here to provide a feasible set of teaching skills via an Internet connection. Virtual reality (VR) is also used here to provide an appropriate set of data for coaches to improve teaching skills. The proposed method increases the efficiency and effectiveness of the education system by improving football coaching techniques.


### 1.2. The Proposed Conveyance-Dependent Teaching Mode

The series progress and maturity identification of college students based on Internet and NN technology improved the development of sports and physical education teaching systems using the network. Switching to online sports teaching has been an accessed alternative to conventional sports teaching practices, providing activities obtaining the college student to directly interact with their teachers. Physical education and sports training involve the practice of sports and physical activities in equipped places and the testing of sports and physical practices in the space under sports teacher supervision. It maximizes the student learning capacity based on data-to-representation teaching practices through online sessions. At the same time, NN technology is applied to sports teaching in the online environment, breaking the conventional way of teaching and creating a new teaching model. This teaching strengthens the teachers’ and students’ communication, and overcoming the errors can provide timely feedback data in sports teaching, which is a two-way process of teaching and learning. In Figure1, the proposed CTM is illustrated.Figure 1
The proposed CTM.The online sessions are used for bridging the long-distance coverage and educational gap based on different training modes providing better visualization and representation of different subjects containing sports and physical education classes. The knowledge-based performance depends on data-to-representation and provides better sports teaching without any interruptions. Amid the challenges in neural learning paradigms, college student assessment and reward assigning are the available online sports sessions’ performance satisfying different training modes of classes. The assessment from college students’ online sessions requires different training modes. Therefore, regardless of the college student’s interests, teacher recommendation is an important consideration. The proposed CTM method is noticed in this consideration to provide data representation and student assessment based on college students through a NN paradigm. In this proposal, errors are considerable for flawless data and student performance assessments are balanced for augmenting different teaching modes with the available data representations.Online sports teaching is based on their different online sessions and teaching modes through NN technology. The proposed CTM paradigm functions between different training modes and assessments. In this proposed work, the teaching modes and activation function for the available sports curriculum, teacher recommendations, and student interests are easy for satisfying high data accuracy output for the different training modes. Further, the online teaching model of college sports is to minimize time factors and analysis rate while increasing the teacher recommendations. They propose different training modes based on two functions, namely assigning rewards and teacher recommendations. This training mode generates the rewards for different sports training sessions based on different subjects, including sports and physical education classes to handle different training modes. The online teaching mode of college sports for students is keen on this objective, as shown in the following:(1a)maxi∈sTModes∀AS=RWsuch thatmini∈ASFSTr∀RWwhereFSTr=αAS−αRW.Therefore,(1b)mini∈sSTAS∀s∈RW.In (1a) and (1b), the variables TModes, AS, RW, and s represent training modes, student assessment, and rewards assignment based on online sessions of a ith student ST, providing visualization and representation, respectively. Based on the second student representation, the variables FSTrαAS, αRW represent the function of student sports training, activation function based on student assessment, and reward assigning. The data-to-representation minimizes the traditional sports teaching practices without interrupting knowledge-based performance using student assessment STAS. If ST=1,2,…,st illustrates the set of students, then the number of online sessions allocated in the time factor tf is RW×tf. The online sessions conducted by the teacher are st×RW. From the overall online sports teaching mode of st×RW, tf×RW are the available online sessions and students based on assigning rewards. The activation function is based on students’ interests and teacher recommendations using the previous knowledge-based performance of the different training modes. In this sports session, the student assessment and data representation are prominent to identify interruptions in online teaching. The data representation in the student assessment DR of the STint student interest is analyzed. The time factor and analysis rate needed for student assessment are the assisting metrics for improving online sports and physical education teaching. The data representation of the reward assigned is based on the current STint in sports; training is estimated using NN technology. After that, depending upon the data-to-representation process, the student’s interest is the augmenting factor. As per the student assessment evaluation, the output and process are the pursuing instance for defining different training modes. The output of the activation function and the available online sessions based on training modes are prominent in the following session.Case 1.
Continuous Session Assessment.Analysis 1.
In this analysis, the online sessions and student interests ofRW×tf for all STint based on DR is the consideration factor in this proposed online sports teaching. The probability of teaching evaluation ρSTAS sequentially is computed as(2)ρSTAS=1−ρTModesi−1∀i∈tf.
Such that(3)ρTModes=1−RW∈STintRW∈tf.
Equations (2) and (3) follow the continuous online sports teaching through the idle probability of student interests STint such that there is no additional data representation and the sports curriculum is as computing in equation (1a). Therefore, student assessment based on the online teaching mode of college sports for ρSTAS is as follows:(4)STASSTint=1AS−RW+1+ρSTASi∀i∈tf.
Hence, the student assessment depending onSTint is mentioned as in (4) which achieved both the constraint st×RW and RW×tf ensuring online sports teaching model output with interrupts through previous knowledge-based performance. The different training modes of assigning RW are to minimize the interruptions during teacher recommendations for both st×RW>RW×tf, and the student assessment is descriptive using the sports curriculum. Therefore, the flawless data and student performance assessments st>tf and ρTModes are less to satisfy (1a) and (1b). Similarly, the result of Case 1 is the prolonging ρTModes and the analysis rate, time factor outputs in interrupts. A continuous session assessment process is hence presented in Figure 2.
The training modes are evenly distributed for the assigned sessions for assessment and data representations (refer to Figure2). The processes are performed for varying time factors. It generates different rewards for continuous and end-of-session estimation. This process performs the assignment process leveraging different training modes.Figure 2
Continuous session assessment.Case 2.
Reward assigning.Analysis 2.
In reward assigning analysis, the activation function ofst>tf is high and hence is the data-to-representation-based sports teaching model of training modes at different periods. Along with the time factor based on STint, the data representation and student assessment are the consideration here. The probability of time factor ρtf is computed as(5)ρtf=ρSTAS∗STASSTint.AS−RWρTModes−AS−RW/STintDR/αRWPRassign.i,where(6)PRassign=∫0tfDRtf−1+1−DRtf−1.dDR.
Instead,(7)PRassign∈STASSTint=∫1RWDRtf−1∗ρTModesαRW1−ρSTAStf−1dRW.
In (5)–(7), the variable PRassign is used to represent the process of assigning a reward to the students at different time intervals. Based on the training modes and teacher recommendations, the occurred interrupts in online teaching based on STint in the sports is an interruption. The reward assigning process as in (6) requires more data representation, sports curriculum, and teacher recommendations and thereby increasing the analysis rates and time factors. In the above analysis of Case 1 and Case 2, the interruptions occurred during online sports training practices based on st>tf and STint outcome and analysis rate are the important consideration factors. This factor is considerably used through NN learning, addressing the problems in assigning rewards for different training modes. The online sessions provide the teacher recommendation for the available students to mitigate the interruptions and time factors. The reward assigning process using the NN process is illustrated in Figure 3.
The inputs are analyzed forρTandρST∀ sessions in identifying rewards. The rewards are assigned for STintandρTmodes such that the hidden layer is prevented from generating multiple assignments. Depending on the hidden layer output, the recommendation and mode assignments are performed. This mode assignment (new) is performed for the new session and time factor (refer to Figure 3).Figure 3
Reward assigning process.


### 1.3. Assigning Rewards Using Activation Function

In assigning rewards using teacher recommendations, the consideration for different student assessment and training modes depends on sports and physical education classes. This proposed model is used for a better teaching model in both flawless data and student assessment performance sequences. Case1 and Case 2 analyses are based on assigning rewards through training modes using a NN learning paradigm. The process of reward assigning depends on different training modes for performing the student’s interest and teacher recommendations at the time of online sports teaching. Instead, the cases for activation functions differ, which follow teacher recommendations through training modes. The reward is assigned to the students based on their interest in sports by computing STint through accurate probability and sports sessions for providing additional teaching. The first teacher recommendation depends on min-max training modes TR and PRassign is given as(8a)PRassign,TR=AS−DRαAS×1STint−STASSTint+1,where(8b)STint=∑i∈tfSTASSTinti−ρSTASi.In (8a) and (8b), the teacher recommendations depend on different training modes of college students for Case 1 in ρtf and STASSTint. Here, the chances of achieving the series data representation in different training modes are as shown in (9)ρtftfTR=12π2STint.RW−ρTModes×ASπ,where(10)AS=maxρTModes×RWSTASSTint−ρTModes×RW.Therefore, the teacher’s recommendation is1−ρTModes×RW/STASSTint−ρTModes×RW and these training modes are based on the online sessions requiring time factor for the knowledge-based performance of RW assigning. The exceeding RW is RW∗PRassign,TR where the probability of student assessment requires sequences and hence the analysis rate is high. There are two possible instances for augmenting data accuracy as per (8a). This teacher recommendation for online sports teaching is given as(11)1−TRi−1=−RW+ρTModes2π2STint∀i∈tf.In (11), the recommendation based on student interest in sports demanding online sessions is either of π or RW. In both cases, if ρTModes=0, then π=RW=AS is the maximum student assessment condition and if ρTModes=1, AS=RW−STint or AS=RW. Therefore, the assessment of AS=RW is a precise outcome for the online sports teaching practices for all the college students as given in (1a) and (1b). The reliable teacher recommendation in this scenario is available based on STint, where the data-to-representation process provides sports curriculum and hence the time factor and analysis rate are compact as in the above equation. The proposed model defines the student assessment and flawless data along with different training modes for the teacher recommendation of RW. The student assessment factor of ρTModes∗STint and 2STintAS2 from the pursuing sports sessions tf∈RW is discussed, respectively. The student performance assessment process is based on their interest RW,AS and AStf−1,RWtf based on different time factors from the training modes. The probability of ρTModes and ρSTAS and ρtf is the equating factor for both instances of activation function as represented above. The student performance assessment for RW,AS and AStf−1,RWtf is updating rewards based on sports curriculum, students’ interest, and teacher recommendations as given in(12)STASSTint=STint−ρTModes∗RWSTint+ρSTASRW∀AS=RWSTint−ρTModes∗RWSTint+ρSTAS+ρtf−ρTModesRW∀AS<RW.In (12), the student performance assessment based on ρSTAS+ρtf−ρTModes is an idle probability identified using STASSTint. The activation function-based assessment is portrayed in Figure 4.Figure 4
Activation function-based assessment.The learning outputs (recommendation and mode assignment) are assessed for theRW assigned ∀ single and continuous sessions. If the ρSTAs exists, then the activation function based on ρtftf/TR∀STASSTint is initiated. This generates max1−TR improving the training modes. The further process relies on ρtf for improving ρRassign,TR such that recommendation is high (Figure 4). Therefore, the student interest in sports training makes the remaining students and unknown neighbors go for recommending the online sessions of college sports which is the process until the rewards are identified in the training modes. Therefore, the online teaching model of college sports is based on student assessment for all tf∈RW increasing both STAS and DR∀i∈RW. The occurred interruptions are the saturation of student performance assessment and reward assigning process until RW updating ∈tf is again trained sessions with a prolonging time factor. The process of student performance assessment with the teacher recommendation is alone analyzed for mentioned cases. This consecutive process maximizes the data accuracy and reduces the interrupts and analysis rate.


## 1.1. Related Works

W. H. Kim and J. H. Kim [16] introduced an individualized artificial intelligence (AI) tutor using a developmental learning network (DLN) for the education system. DLN is used here to identify the learning channels of students that provide necessary information for further process. Both sequence and frequency are used in DLN to classify the preference of learners. AI is used here to maximize the understanding capabilities of learners in the learning process. The proposed individualized AI tutor method improves the performance and learning skills of learners.Arciniegas et al. [17] proposed an artificial-intelligence- (AI-) based student monitoring system for the learning environment in the education system. A recurrent neural network (RNN) algorithm is used here to identify the facial expression of students while teaching. RNN provides an appropriate set of data to AI that enhance the teaching methods and techniques for students. The proposed monitoring system is mostly used in various educational environments that provide the necessary set of data for teachers. The proposed monitoring system maximizes the efficiency and performance rate of students.Kastrati et al. [18] proposed a new automatic supervised framework for aspect-based sentiment analysis processes on the massive open online course (MOOC). In MOOC, weakly supervised annotation-based information is stored providing optimal detail about weak students. The proposed method gets information from weakly supervised annotation and finds out the information regarding students. The proposed framework produces a feasible set of data for the sentiment analysis process that reduce the latency rate in the identification process. The proposed framework improves the effectiveness and performance of the system.Duan [19] introduced an augmented reality (AR) technology-based online volleyball remote teaching system. AR captures the volleyball coach’s techniques and skills and then stores that in computer storage space. Both hardware and software provide necessary services to online sports learners. The captured data are sent to monitor with a certain set of tools and equipment. Software modules provide data to online learners that enhance the efficiency of the online teaching system. The proposed teaching system improves the skills and performance rate of sports education students.Camiré et al. [20] proposed a validation method for coaching life skills in sports questionnaires (CLSS-Q). The proposed method is mainly used to validate the coaching techniques of coaches that provide an appropriate set of data for the questionnaire. CLSS-Q conducts validation and a developmental process that identifies the important features and patterns of coaches. The proposed method improves the reliability and feasibility of CLSS-Q, which improves the sports skills of students.Xie [21] introduced a real-time monitoring system for big data sports teaching systems. The big data analysis process provides an appropriate set of data for a monitoring system. Big data is used here to manage a large amount of data that are captured by the sports teaching system. Wearable devices and gadgets are used in a real-time monitoring system that produces an actual set of data which are related to sports students. The proposed method improves the efficiency and feasibility of the monitoring system.Liu et al. [22] proposed a decision support system using artificial intelligence (AI) technology for the 5G network assessment process. AI is used here to provide various techniques to improve the teaching, learning, reading, and writing skills of students. The 5 G network assessment process is a complicated task to perform improving the educational capabilities of students via an Internet connection. The proposed method maximizes the accuracy rate in a decision-making process that enhances the performance rate of the system.Zhang et al. [23] introduced a deep-belief-network- (DBN-) based learning style classification approach for the large-scale online education system. The learning style model is implemented here to identify the patterns and styles of learning based on previously recorded data. DBN is used in the proposed approach to find out the learning styles of individual students that provide an optimal set of data for the further teaching process. The proposed method achieves a high accuracy rate in learning style classification that enhances the performance of the online education system.Fang [24] proposed a new analysis method for the backpropagation (BP) neural network model for school physical education management systems. A fuzzy c-means algorithm is used here to analyze the information that is stored in a management system. The fusion method is used here to find out the characteristics and features that are presented in sports physical data. BP improves the effectiveness rate of the fusion method that provides an optimal set of data for an information management system. The proposed analysis process improves the feasibility and efficiency of the physical sports information management system.Zou [25] introduced a sports development strategy based on web technology and a data mining approach. Both web technology and data mining are used here to improve the performance rate in the sports physical education system. Data mining is used here to analyze the important set of data that is necessary for the sports development process. The reconstruction algorithm is implemented in the proposed method to eliminate unnecessary noise and data from the analysis process. Experimental results show that the proposed method enhances the effectiveness and reliability of the sports physical education system.Bao and Yu [26] proposed a quality evaluation method for hybrid physical online and offline systems. The proposed method is mainly used for the evaluation process that evaluates the quality of teaching techniques which are necessary to improve students’ sports skills. A fuzzy comprehensive evaluation algorithm is used here to evaluate the quality of teaching indexes and produce a proper set of data for the evaluation process. The proposed method achieves a better accuracy rate in an evaluation process that enhances the efficiency and quality of hybrid online and offline teaching systems.Li et al. [27] introduced a new strategy to improve football teaching techniques by using the artificial intelligence (AI) approach. The proposed method is mainly used to identify the quality of football teaching techniques that are provided to sports students. AI is used here to provide a feasible set of teaching skills via an Internet connection. Virtual reality (VR) is also used here to provide an appropriate set of data for coaches to improve teaching skills. The proposed method increases the efficiency and effectiveness of the education system by improving football coaching techniques.


## 1.2. The Proposed Conveyance-Dependent Teaching Mode

The series progress and maturity identification of college students based on Internet and NN technology improved the development of sports and physical education teaching systems using the network. Switching to online sports teaching has been an accessed alternative to conventional sports teaching practices, providing activities obtaining the college student to directly interact with their teachers. Physical education and sports training involve the practice of sports and physical activities in equipped places and the testing of sports and physical practices in the space under sports teacher supervision. It maximizes the student learning capacity based on data-to-representation teaching practices through online sessions. At the same time, NN technology is applied to sports teaching in the online environment, breaking the conventional way of teaching and creating a new teaching model. This teaching strengthens the teachers’ and students’ communication, and overcoming the errors can provide timely feedback data in sports teaching, which is a two-way process of teaching and learning. In Figure1, the proposed CTM is illustrated.Figure 1
The proposed CTM.The online sessions are used for bridging the long-distance coverage and educational gap based on different training modes providing better visualization and representation of different subjects containing sports and physical education classes. The knowledge-based performance depends on data-to-representation and provides better sports teaching without any interruptions. Amid the challenges in neural learning paradigms, college student assessment and reward assigning are the available online sports sessions’ performance satisfying different training modes of classes. The assessment from college students’ online sessions requires different training modes. Therefore, regardless of the college student’s interests, teacher recommendation is an important consideration. The proposed CTM method is noticed in this consideration to provide data representation and student assessment based on college students through a NN paradigm. In this proposal, errors are considerable for flawless data and student performance assessments are balanced for augmenting different teaching modes with the available data representations.Online sports teaching is based on their different online sessions and teaching modes through NN technology. The proposed CTM paradigm functions between different training modes and assessments. In this proposed work, the teaching modes and activation function for the available sports curriculum, teacher recommendations, and student interests are easy for satisfying high data accuracy output for the different training modes. Further, the online teaching model of college sports is to minimize time factors and analysis rate while increasing the teacher recommendations. They propose different training modes based on two functions, namely assigning rewards and teacher recommendations. This training mode generates the rewards for different sports training sessions based on different subjects, including sports and physical education classes to handle different training modes. The online teaching mode of college sports for students is keen on this objective, as shown in the following:(1a)maxi∈sTModes∀AS=RWsuch thatmini∈ASFSTr∀RWwhereFSTr=αAS−αRW.Therefore,(1b)mini∈sSTAS∀s∈RW.In (1a) and (1b), the variables TModes, AS, RW, and s represent training modes, student assessment, and rewards assignment based on online sessions of a ith student ST, providing visualization and representation, respectively. Based on the second student representation, the variables FSTrαAS, αRW represent the function of student sports training, activation function based on student assessment, and reward assigning. The data-to-representation minimizes the traditional sports teaching practices without interrupting knowledge-based performance using student assessment STAS. If ST=1,2,…,st illustrates the set of students, then the number of online sessions allocated in the time factor tf is RW×tf. The online sessions conducted by the teacher are st×RW. From the overall online sports teaching mode of st×RW, tf×RW are the available online sessions and students based on assigning rewards. The activation function is based on students’ interests and teacher recommendations using the previous knowledge-based performance of the different training modes. In this sports session, the student assessment and data representation are prominent to identify interruptions in online teaching. The data representation in the student assessment DR of the STint student interest is analyzed. The time factor and analysis rate needed for student assessment are the assisting metrics for improving online sports and physical education teaching. The data representation of the reward assigned is based on the current STint in sports; training is estimated using NN technology. After that, depending upon the data-to-representation process, the student’s interest is the augmenting factor. As per the student assessment evaluation, the output and process are the pursuing instance for defining different training modes. The output of the activation function and the available online sessions based on training modes are prominent in the following session.Case 1.
Continuous Session Assessment.Analysis 1.
In this analysis, the online sessions and student interests ofRW×tf for all STint based on DR is the consideration factor in this proposed online sports teaching. The probability of teaching evaluation ρSTAS sequentially is computed as(2)ρSTAS=1−ρTModesi−1∀i∈tf.
Such that(3)ρTModes=1−RW∈STintRW∈tf.
Equations (2) and (3) follow the continuous online sports teaching through the idle probability of student interests STint such that there is no additional data representation and the sports curriculum is as computing in equation (1a). Therefore, student assessment based on the online teaching mode of college sports for ρSTAS is as follows:(4)STASSTint=1AS−RW+1+ρSTASi∀i∈tf.
Hence, the student assessment depending onSTint is mentioned as in (4) which achieved both the constraint st×RW and RW×tf ensuring online sports teaching model output with interrupts through previous knowledge-based performance. The different training modes of assigning RW are to minimize the interruptions during teacher recommendations for both st×RW>RW×tf, and the student assessment is descriptive using the sports curriculum. Therefore, the flawless data and student performance assessments st>tf and ρTModes are less to satisfy (1a) and (1b). Similarly, the result of Case 1 is the prolonging ρTModes and the analysis rate, time factor outputs in interrupts. A continuous session assessment process is hence presented in Figure 2.
The training modes are evenly distributed for the assigned sessions for assessment and data representations (refer to Figure2). The processes are performed for varying time factors. It generates different rewards for continuous and end-of-session estimation. This process performs the assignment process leveraging different training modes.Figure 2
Continuous session assessment.Case 2.
Reward assigning.Analysis 2.
In reward assigning analysis, the activation function ofst>tf is high and hence is the data-to-representation-based sports teaching model of training modes at different periods. Along with the time factor based on STint, the data representation and student assessment are the consideration here. The probability of time factor ρtf is computed as(5)ρtf=ρSTAS∗STASSTint.AS−RWρTModes−AS−RW/STintDR/αRWPRassign.i,where(6)PRassign=∫0tfDRtf−1+1−DRtf−1.dDR.
Instead,(7)PRassign∈STASSTint=∫1RWDRtf−1∗ρTModesαRW1−ρSTAStf−1dRW.
In (5)–(7), the variable PRassign is used to represent the process of assigning a reward to the students at different time intervals. Based on the training modes and teacher recommendations, the occurred interrupts in online teaching based on STint in the sports is an interruption. The reward assigning process as in (6) requires more data representation, sports curriculum, and teacher recommendations and thereby increasing the analysis rates and time factors. In the above analysis of Case 1 and Case 2, the interruptions occurred during online sports training practices based on st>tf and STint outcome and analysis rate are the important consideration factors. This factor is considerably used through NN learning, addressing the problems in assigning rewards for different training modes. The online sessions provide the teacher recommendation for the available students to mitigate the interruptions and time factors. The reward assigning process using the NN process is illustrated in Figure 3.
The inputs are analyzed forρTandρST∀ sessions in identifying rewards. The rewards are assigned for STintandρTmodes such that the hidden layer is prevented from generating multiple assignments. Depending on the hidden layer output, the recommendation and mode assignments are performed. This mode assignment (new) is performed for the new session and time factor (refer to Figure 3).Figure 3
Reward assigning process.


## 1.3. Assigning Rewards Using Activation Function

In assigning rewards using teacher recommendations, the consideration for different student assessment and training modes depends on sports and physical education classes. This proposed model is used for a better teaching model in both flawless data and student assessment performance sequences. Case1 and Case 2 analyses are based on assigning rewards through training modes using a NN learning paradigm. The process of reward assigning depends on different training modes for performing the student’s interest and teacher recommendations at the time of online sports teaching. Instead, the cases for activation functions differ, which follow teacher recommendations through training modes. The reward is assigned to the students based on their interest in sports by computing STint through accurate probability and sports sessions for providing additional teaching. The first teacher recommendation depends on min-max training modes TR and PRassign is given as(8a)PRassign,TR=AS−DRαAS×1STint−STASSTint+1,where(8b)STint=∑i∈tfSTASSTinti−ρSTASi.In (8a) and (8b), the teacher recommendations depend on different training modes of college students for Case 1 in ρtf and STASSTint. Here, the chances of achieving the series data representation in different training modes are as shown in (9)ρtftfTR=12π2STint.RW−ρTModes×ASπ,where(10)AS=maxρTModes×RWSTASSTint−ρTModes×RW.Therefore, the teacher’s recommendation is1−ρTModes×RW/STASSTint−ρTModes×RW and these training modes are based on the online sessions requiring time factor for the knowledge-based performance of RW assigning. The exceeding RW is RW∗PRassign,TR where the probability of student assessment requires sequences and hence the analysis rate is high. There are two possible instances for augmenting data accuracy as per (8a). This teacher recommendation for online sports teaching is given as(11)1−TRi−1=−RW+ρTModes2π2STint∀i∈tf.In (11), the recommendation based on student interest in sports demanding online sessions is either of π or RW. In both cases, if ρTModes=0, then π=RW=AS is the maximum student assessment condition and if ρTModes=1, AS=RW−STint or AS=RW. Therefore, the assessment of AS=RW is a precise outcome for the online sports teaching practices for all the college students as given in (1a) and (1b). The reliable teacher recommendation in this scenario is available based on STint, where the data-to-representation process provides sports curriculum and hence the time factor and analysis rate are compact as in the above equation. The proposed model defines the student assessment and flawless data along with different training modes for the teacher recommendation of RW. The student assessment factor of ρTModes∗STint and 2STintAS2 from the pursuing sports sessions tf∈RW is discussed, respectively. The student performance assessment process is based on their interest RW,AS and AStf−1,RWtf based on different time factors from the training modes. The probability of ρTModes and ρSTAS and ρtf is the equating factor for both instances of activation function as represented above. The student performance assessment for RW,AS and AStf−1,RWtf is updating rewards based on sports curriculum, students’ interest, and teacher recommendations as given in(12)STASSTint=STint−ρTModes∗RWSTint+ρSTASRW∀AS=RWSTint−ρTModes∗RWSTint+ρSTAS+ρtf−ρTModesRW∀AS<RW.In (12), the student performance assessment based on ρSTAS+ρtf−ρTModes is an idle probability identified using STASSTint. The activation function-based assessment is portrayed in Figure 4.Figure 4
Activation function-based assessment.The learning outputs (recommendation and mode assignment) are assessed for theRW assigned ∀ single and continuous sessions. If the ρSTAs exists, then the activation function based on ρtftf/TR∀STASSTint is initiated. This generates max1−TR improving the training modes. The further process relies on ρtf for improving ρRassign,TR such that recommendation is high (Figure 4). Therefore, the student interest in sports training makes the remaining students and unknown neighbors go for recommending the online sessions of college sports which is the process until the rewards are identified in the training modes. Therefore, the online teaching model of college sports is based on student assessment for all tf∈RW increasing both STAS and DR∀i∈RW. The occurred interruptions are the saturation of student performance assessment and reward assigning process until RW updating ∈tf is again trained sessions with a prolonging time factor. The process of student performance assessment with the teacher recommendation is alone analyzed for mentioned cases. This consecutive process maximizes the data accuracy and reduces the interrupts and analysis rate.


## 2. Discussion

### 2.1. Case Study

The proposed CTM is analyzed from the information fetched from [28]. This website source provides free and paid online courses for business, education, sports, language, etc. This case study analyzes the data and its representation of “resilience,” “speed & power,” “endurance,” and “psychological features.” In Figure 5, the representation (overall) is presented.Figure 5
Data source representation.The levels and assessment-based ratings are designed for identifying user interests and teacher recommendations. Based on the assessments, new representations and training modes are introduced. The assessment model for the 4 considerations is illustrated with its associates in Figure6.Figure 6
Assessment model.The different sessions (from 1 to 8) for different topics are assessed using summary and query. Based on the assessment, rewards are provided. The rewards are fetched as ratings, comments, and suggestions from the users. Here, the data representation for the visibility is presented between users, positive feedback, and negative feedback. Both feedbacks are used for constructive purposes such as interface and access improvements. In Table1, the iterations and the activation function output for reward allocation for the 4 considerations are presented.Table 1
Activation and flow output for iterations.
IterationsTopicsResilienceSpeed and powerEndurancePsychological featuresActivationFlawActivationFlawActivationFlawActivationFlaw1000.4760.0950.0840.5220.1090.3730.2940.2352000.5610.0810.0920.490.1790.3490.3310.2293000.7450.080.1010.4860.2050.3030.360.2214000.7750.0720.1080.4670.2580.2740.3830.2015000.7830.070.1130.4430.2690.2740.390.1576000.8360.0620.1830.390.2710.2510.5410.1497000.8920.0580.1910.3710.2830.2310.5450.148000.9290.0280.1950.3140.4210.1840.6340.115The flow based on the activation function output is estimated for the varying sessions and a summary of results is provided. The summary- (assessment-) based rewards are high due to general information. In the case of query-like assessment, the user (student) interest varies due to which the flow results in many increases. However, this is supported using continuous neural learning. The process is maximized until the maximum accuracy is achieved. Based on the available data, the possible representations are portrayed in Figure7.Figure 7
Possible representations.The above kinds of representations are used for improving readability, teaching efficiency, and understanding of different sports activities. The above representation varies for different sessions and users such that a recent update is reflected in the representation.


## 2.1. Case Study

The proposed CTM is analyzed from the information fetched from [28]. This website source provides free and paid online courses for business, education, sports, language, etc. This case study analyzes the data and its representation of “resilience,” “speed & power,” “endurance,” and “psychological features.” In Figure 5, the representation (overall) is presented.Figure 5
Data source representation.The levels and assessment-based ratings are designed for identifying user interests and teacher recommendations. Based on the assessments, new representations and training modes are introduced. The assessment model for the 4 considerations is illustrated with its associates in Figure6.Figure 6
Assessment model.The different sessions (from 1 to 8) for different topics are assessed using summary and query. Based on the assessment, rewards are provided. The rewards are fetched as ratings, comments, and suggestions from the users. Here, the data representation for the visibility is presented between users, positive feedback, and negative feedback. Both feedbacks are used for constructive purposes such as interface and access improvements. In Table1, the iterations and the activation function output for reward allocation for the 4 considerations are presented.Table 1
Activation and flow output for iterations.
IterationsTopicsResilienceSpeed and powerEndurancePsychological featuresActivationFlawActivationFlawActivationFlawActivationFlaw1000.4760.0950.0840.5220.1090.3730.2940.2352000.5610.0810.0920.490.1790.3490.3310.2293000.7450.080.1010.4860.2050.3030.360.2214000.7750.0720.1080.4670.2580.2740.3830.2015000.7830.070.1130.4430.2690.2740.390.1576000.8360.0620.1830.390.2710.2510.5410.1497000.8920.0580.1910.3710.2830.2310.5450.148000.9290.0280.1950.3140.4210.1840.6340.115The flow based on the activation function output is estimated for the varying sessions and a summary of results is provided. The summary- (assessment-) based rewards are high due to general information. In the case of query-like assessment, the user (student) interest varies due to which the flow results in many increases. However, this is supported using continuous neural learning. The process is maximized until the maximum accuracy is achieved. Based on the available data, the possible representations are portrayed in Figure7.Figure 7
Possible representations.The above kinds of representations are used for improving readability, teaching efficiency, and understanding of different sports activities. The above representation varies for different sessions and users such that a recent update is reflected in the representation.


## 3. Comparative Analysis

This subsection presents the comparative analysis for the metrics representation, analysis rate, time factor, and accuracy. The sessions are varied from 2 to 22 based on the data augmentation. In this analysis, the methods PEM-BPNN [24], LSCA [23], and HTQ-FCEM [26] are considered with the proposed CTM.


### 3.1. Representation

Figure8 presents the comparative analysis of representation for the existing and proposed methods. The data representation is augmented using varying Tmodes and RW over the varying sessions. In the first probability estimation of ρTModes, the requirements for ρtf∀ρRassign is validated. Depending on the ρRassign,TR, the NN imposes further sessions. In the validation based on STint based on the analysis rate, the data requirements are high as the requirements are high fetching and processing becomes high. The analyzed data are provided for representation such that As is improved. On the contrary case of ρSTAS, the RW plays a vital role in determining ρtftf/TR such that 1−TR is maximized. Therefore, two different combinations namely ρT and ρST∀STAS (before activation) are induced for leveraging the data representations. The representations are further augmented using the activation function ∀max1−TR provided that new models are assigned. This assignment relies on the fact that RW updates are fetched at different end-of-sessions.Figure 8
Representation comparisons.


### 3.2. Analysis Rate

The comparative analysis for data analysis rate is presented in Figure9. The varying sessions are grouped under assessments or rewards or even suggestions. This is required for identifying evaluation and mode generation probabilities. Both probabilities are required for leveraging data analysis for representation through STAS and STint. The ρtf also manages different representation modes for improving the PRassign,TR analysis. However, PRassign is the first analysis required for satisfying ρSTAS such that RW∀ the initial mode is analyzed. In the neural learning process, the maximum possible analysis ∀PRassign classified in (6) and (7) is performed. Depending on the RW∀ modes as a learning output, the activation function is called. This activation function is performed to combinations on ρtftf/TR such that STAS is estimated for ρTModes. Therefore, the analysis rate is augmented based on recommended sessions and available data. Furthermore, the data representations increase the analysis of the recommended training mode or even the negative feedback-based data outputs. This further improves the available data allocation and analysis iterations over the frequent representations.Figure 9
Analysis rate comparisons.


### 3.3. Time Factor

The proposed CTM achieves less time factor for the varying sessions and analysis rate. In the first probability condition ofρSTAS, the time factor is preserved by assigning ρT∀RW∈ sessions. However, if a STAS is violated by the ρTModes, then PRassign as in (6) and (7) is modified based on TR as in (8a). The learning recurrence for the nonrecommendations is pursued for preventing less 1−TR. Therefore, the ρTf requirement is slightly increased in the point. Depending on the learning recommendation, the consecutive mode and data representations are pursued based on ρtftf/TR such that the activation function is used for maximizing further analysis within the allocated time interval. Further analysis is performed using RW and factors provided across different ρTModes. The allocation for the sessions (previous) is discarded for maximizing the analysis within the expelled ρTf time. This is unanimously pursued by the varying sessions and analysis rates in confining the time factor (refer to Figure 10).Figure 10
Time factor comparisons.


### 3.4. Accuracy

The comparative analysis for accuracy is presented in Figure11 for the varying sessions and analysis rates. The proposed CTM maximizes data representation accuracy regardless of the impacting ρtf. This is achieved by the NN process of classifying recommendations and modes based on RW. The two probability estimation factors (i.e.) ρtf and ρTModes are initially assessed for improving the accuracy across distinct sessions. It is unanimous based on current RW and ρtftf/TR such that the activation functions modify distinct variations in analysis. Therefore, the data analysis requirements are hiked by diminishing the time factor. The proposed CTM relies on the activation function for levering the analysis rate and validation for maximizing data representations. In the modes recommending process, the new session with RW and AS is performed without preventing representation. In the recurrent process, the deviations are suppressed for maximizing accuracy. The above comparisons are tabulated in Tables 2 and 3 for the sessions and analysis rate, respectively.Figure 11
Accuracy comparisons.Table 2
Comparison for sessions.
MetricsPEM-BPNNLSCAHTQ-FCEMCTMRepresentation (%)52.7465.4379.1393.393Analysis rate (sessions)3155638461102Time factor (s/analysis)0.6640.4220.2730.148Accuracy0.550.6850.8480.921Table 3
Comparison for analysis rate.
MetricsPEM-BPNNLSCAHTQ-FCEMCTMRepresentation (%)55.9469.6679.2593.159Time factor (s/analysis)0.6430.4570.3230.153Accuracy0.5550.6770.8390.919Findings: the proposed CTM achieves 13.81% high representation, 7.98% high analysis rate, 11.22% less time factor, and 11.3% high accuracy.Findings: the proposed CTM achieves 12.44% high representation, 6.43% less time factor, and 11.43% high accuracy.


## 3.1. Representation

Figure8 presents the comparative analysis of representation for the existing and proposed methods. The data representation is augmented using varying Tmodes and RW over the varying sessions. In the first probability estimation of ρTModes, the requirements for ρtf∀ρRassign is validated. Depending on the ρRassign,TR, the NN imposes further sessions. In the validation based on STint based on the analysis rate, the data requirements are high as the requirements are high fetching and processing becomes high. The analyzed data are provided for representation such that As is improved. On the contrary case of ρSTAS, the RW plays a vital role in determining ρtftf/TR such that 1−TR is maximized. Therefore, two different combinations namely ρT and ρST∀STAS (before activation) are induced for leveraging the data representations. The representations are further augmented using the activation function ∀max1−TR provided that new models are assigned. This assignment relies on the fact that RW updates are fetched at different end-of-sessions.Figure 8
Representation comparisons.


## 3.2. Analysis Rate

The comparative analysis for data analysis rate is presented in Figure9. The varying sessions are grouped under assessments or rewards or even suggestions. This is required for identifying evaluation and mode generation probabilities. Both probabilities are required for leveraging data analysis for representation through STAS and STint. The ρtf also manages different representation modes for improving the PRassign,TR analysis. However, PRassign is the first analysis required for satisfying ρSTAS such that RW∀ the initial mode is analyzed. In the neural learning process, the maximum possible analysis ∀PRassign classified in (6) and (7) is performed. Depending on the RW∀ modes as a learning output, the activation function is called. This activation function is performed to combinations on ρtftf/TR such that STAS is estimated for ρTModes. Therefore, the analysis rate is augmented based on recommended sessions and available data. Furthermore, the data representations increase the analysis of the recommended training mode or even the negative feedback-based data outputs. This further improves the available data allocation and analysis iterations over the frequent representations.Figure 9
Analysis rate comparisons.


## 3.3. Time Factor

The proposed CTM achieves less time factor for the varying sessions and analysis rate. In the first probability condition ofρSTAS, the time factor is preserved by assigning ρT∀RW∈ sessions. However, if a STAS is violated by the ρTModes, then PRassign as in (6) and (7) is modified based on TR as in (8a). The learning recurrence for the nonrecommendations is pursued for preventing less 1−TR. Therefore, the ρTf requirement is slightly increased in the point. Depending on the learning recommendation, the consecutive mode and data representations are pursued based on ρtftf/TR such that the activation function is used for maximizing further analysis within the allocated time interval. Further analysis is performed using RW and factors provided across different ρTModes. The allocation for the sessions (previous) is discarded for maximizing the analysis within the expelled ρTf time. This is unanimously pursued by the varying sessions and analysis rates in confining the time factor (refer to Figure 10).Figure 10
Time factor comparisons.


## 3.4. Accuracy

The comparative analysis for accuracy is presented in Figure11 for the varying sessions and analysis rates. The proposed CTM maximizes data representation accuracy regardless of the impacting ρtf. This is achieved by the NN process of classifying recommendations and modes based on RW. The two probability estimation factors (i.e.) ρtf and ρTModes are initially assessed for improving the accuracy across distinct sessions. It is unanimous based on current RW and ρtftf/TR such that the activation functions modify distinct variations in analysis. Therefore, the data analysis requirements are hiked by diminishing the time factor. The proposed CTM relies on the activation function for levering the analysis rate and validation for maximizing data representations. In the modes recommending process, the new session with RW and AS is performed without preventing representation. In the recurrent process, the deviations are suppressed for maximizing accuracy. The above comparisons are tabulated in Tables 2 and 3 for the sessions and analysis rate, respectively.Figure 11
Accuracy comparisons.Table 2
Comparison for sessions.
MetricsPEM-BPNNLSCAHTQ-FCEMCTMRepresentation (%)52.7465.4379.1393.393Analysis rate (sessions)3155638461102Time factor (s/analysis)0.6640.4220.2730.148Accuracy0.550.6850.8480.921Table 3
Comparison for analysis rate.
MetricsPEM-BPNNLSCAHTQ-FCEMCTMRepresentation (%)55.9469.6679.2593.159Time factor (s/analysis)0.6430.4570.3230.153Accuracy0.5550.6770.8390.919Findings: the proposed CTM achieves 13.81% high representation, 7.98% high analysis rate, 11.22% less time factor, and 11.3% high accuracy.Findings: the proposed CTM achieves 12.44% high representation, 6.43% less time factor, and 11.43% high accuracy.


## 4. Conclusion

This article discussed the working and performance of a CTM for improving the accuracy of the online sports teaching process. The proposed paradigm depends on a NN for identifying recommendations, interests, and rewards for improving the online sports teaching session’s efficiency. The training modes are identified and augmented based on NN and activation function outputs. This recommendation process augmented by the actual and updated curriculum, student interest, and training sessions is used for improved data representations. The data-to-representation is used for improving the presentation accuracy in visual training and session assessment. Each session is concluded using the reward and activation function for improving accuracy. Based on the NN outputs, further mode augmentation and sessions are prolonged for improving the data representation and reducing the time factor. The proposed CTM achieves 13.81% high representation, 7.98% high analysis rate, 11.22% less time factor, and 11.3% high accuracy for different sessions.



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*Source: 2718787-2022-09-06.xml*