# Investigation on the Use of Virtual Reality in the Flipped Teaching of Martial Arts Taijiquan Based on Deep Learning and Big Data Analytics

Authors: Zhang HanLiang; Zhang LiNa
Journal: Journal of Sensors
 (2022)

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## Abstract

Flipping classroom teaching of martial arts “Taijiquan” is a contemporary teaching method that primarily uses information technology tools to accomplish the natural fusion of information technology and education and teaching. Digital teaching materials influence the conventional classroom. The conventional teaching approach of teacher instruction and student learning is no longer satisfactory to students. Virtual reality (VR) technology and physical education integration and development have currently emerged as a new trend, but research on its application to martial arts is still in its theoretical stages and lacks concrete application countermeasures and schemes, necessitating further investigation and study. This paper intends to investigate the use of virtual reality technology in martial arts education. The architecture of virtual reality based on the deep learning algorithm is suggested. The student dataset is collected, and they are split into two groups: control and study groups. Traditional teaching is provided to the control group, and deep learning-based VR-assisted teaching is provided to the study group using the Deep Binary Hashed Convolutional Neural Network (DBH-CNN). Statistical analysis is done using Student’st-test, logistic regression analysis, and analysis of variance (ANOVA). According to the study findings, the majority of students see virtual reality technology-assisted martial arts education favorably, and their passion for studying martial arts has greatly increased.


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## Body

## 1. Introduction

Convenience and impact offered by information technology coexist in the rapidly evolving, ever-changing 21st century. The flipped classroom model has attracted the attention of researchers in the field of education both at home and abroad in recent years. Because of the advent of the flipped classroom, the conventional method of instruction has been turned on its head. Compared to other fields of study, physical education stands out as a very practical subject. The flipped classroom is a new kind of classroom that has emerged as a result of the information society and offers promising new approaches to advancing education reform in the United States. Studies in various fields have shown that the flipped classroom model is more effective than standard lecture-style instruction [1]. Taijiquan is a kind of Chinese martial art that emphasizes health, self-defense, and the avoidance of illness via a combination of physical training and mental concentration, drawing on the knowledge and spirit of China’s long history of social production. Taijiquan instruction has progressed over the years, but it has run across challenges along the way. These challenges include the monotony of a single teaching topic, the inflexibility of a single teaching approach, and the lack of consistency in technical action speaking. To master Taijiquan, one must have a firm knowledge of its technical rules. This allows for a deeper comprehension of its meaning, which in turn has a beneficial effect on one’s physical well-being, cultural achievements, and sense of self. By developing students’ individualized learning, the pedagogical practice of flipping classrooms takes into account students’ physical and mental characteristics, personality hobbies, and other factors, allowing for more time for teachers and students to interact and thereby increasing students’ engagement in class. The flipped classroom paradigm is based on the idea that teachers should be students first. This approach to education emphasizes student autonomy, collaboration, and customization. Supportive better education may help pupils develop their creative skills. The drab and boring techniques often used to teach physical education are ineffective in getting pupils excited about learning. This is why it is crucial to revamp the way that universities and colleges teach physical education. Taijiquan carries with it a wealth of historical and cultural significance. Through the use of media technologies, students are more likely to engage in the teaching methods of interaction, sharing, and group discussion to build the learning theme of body sensation from boxing theory [2]. Wan [3] states that in a self-study setting, students share their goals for the class and discuss challenges they have had speaking with professors to better prepare for class and get more practice with classroom teaching abilities. To successfully address students’ concerns about failing to keep up with class material, the flipping classroom model has been developed. To maximize classroom efficiency, teachers often use particular strategies to get students engaged. Stop the passive learning that has plagued classroom instruction for so long. By coordinating their preclass activities based on their independent study, students are better prepared to make the most of their classroom time, and instructors are less likely to feel overwhelmed by their students’ demands for individual attention during class. The relative or ambiguous nature of technical norms is, of course, also a hallmark of traditional Chinese culture. To achieve “normative mastery” of Taijiquan’s technical motions, one must have a firm grip on the “degree” of the standard at which it is held [4]. Inverting the conventional classroom structure and fusing the martial arts education model is also proposed. College martial arts programs use a grading system to help students build self-esteem, learn to put their health and martial arts first, become more proficient in these areas, become more engaged in class, and learn to train themselves. With its distinctive cultural meaning and action attractiveness, as well as its liveliness, the martial arts grade system is liked by students and provides them with an excellent basis for a lifetime of physical activity. To a certain extent, the pedagogical strategy of “flipping the classroom” [5], which is aimed at increasing students’ motivation and engagement in a class by introducing them to new ideas and concepts, parallels the overall objective of collegiate martial arts instructors. By infusing a martial arts curriculum and inverting the traditional classroom structure, we can better serve our students. Three key facets illustrate how the flip classroom pedagogical approach and the martial arts segment teaching activity are integrated: preclass, in-class, and after-school modules, and they are all interconnected. Because martial arts are often taught in a classroom setting, instructors must be well prepared for each lesson. The promotion of martial arts courses is quite light at universities. Many people who train in martial arts are not fully informed about what is expected of them. Many college students, like many adults, have not trained consistently or undertaken standardized exercise training. Their martial arts techniques, however, are not conventional. Standardized behavior, martial arts expertise, and power control are all emphasized in the various belts of martial education. This means that the instructor has to provide thorough explanations and demonstrations of each martial arts technique being taught. Enhance not just pupils’ technical martial arts skills but also their mental and spiritual flexibility. According to Zhang [6], the shift away from conventional teacher evaluation’s focus on results at the expense of the process would help move China’s physical education system in a more positive direction, especially for students with less of a background in sports. Participation in the love of sports may promote students’ physical health and foster their intellectual development. A “flip classroom” strategy for teaching is shown in Figure 1.Figure 1
Flip teach model flow.As a result, the following learning goals are established for the martial arts section: First, the spirit of martial arts, give students homework so they may learn about the history and culture of martial arts; second, the action basics of martial arts show students how to perform basic techniques and drills; and third, let students compete in tournaments. Play the instructional video, break down the movement in slow motion and explain the evaluation criteria for the martial arts grading system, then go through the basics of the action in front of your class, and have a conversation about it. By doing so, trainees may learn the foundational techniques of martial arts more thoroughly [7]. For educators’ prioritization of class time for lesson planning, create useful instructional movies and instruct students by having them enjoy the video, analyze it, dissect its essential elements, and then replicate its presentation on their own. Make a picture plan and a decomposition plan for the three views (front, left, and top) to see how they break down. For preclass work that students should be doing, allow students to see Bruce Lee’s seminal martial arts film, “Jingwumen,” as well as the martial arts component of the exam, “2016 Taijiquan Competition,” during a flip teaching lesson to help them grasp the martial arts ethos. Allow students to see the film to better comprehend the nature of martial arts, the significance of martial arts, and the criteria for martial arts systems. The educator arranges small and large group opportunities for pupils to talk, do, and get feedback. Using the flipped classroom model, students first view the necessary videos for the martial arts component of the exam section to grasp the concept of movement imitation. A classroom-flipping system that uses martial arts grades is examined. Teacher assessment, student self-evaluation, and peer and community evaluation are the common constituents [8]. Students are dissatisfied with the traditional teaching model of teacher instruction and student learning. Virtual reality (VR) technology and physical education integration and development are a new trend, but research on its application to martial arts is yet in its theoretical phases and lacks real application countermeasures and plans, requiring an additional study. The purpose of this paper is to look at the possibility of using VR for teaching martial arts. It is proposed that a deep learning algorithm forms the basis of VR’s underlying architecture. DBH-CNN approaches for effective image retrieval are designed to train hash functions that map-related images to binary codes in the Hamming space that are semantically associated while maintaining similarity. A deep CNN can automatically learn fundamental filters and hierarchically combine them to allow the description of latent ideas for pattern recognition.


### 1.1. Contributions of the Study

The following are the contributions of the study:(i)
After collecting the student dataset, we next randomly assigned half to the control group and half to the research group(ii)
Comparatively, the control group receives traditional teaching, while the experimental group is given virtual reality (VR) teaching based on deep learning and the use of a Deep Binary Hashed Convolutional Neural Network (DBH-CNN)(iii)
Student’st-tests, regression analyses, and analyses of variance (ANOVA) are used for statistical analysisThe remaining work is divided into the following sections: Section2, which includes the literature review and problem statement; Section 3, which includes the proposed methodology; Section 4, which includes the result and discussion; and Section 5, which includes the conclusion.


## 1.1. Contributions of the Study

The following are the contributions of the study:(i)
After collecting the student dataset, we next randomly assigned half to the control group and half to the research group(ii)
Comparatively, the control group receives traditional teaching, while the experimental group is given virtual reality (VR) teaching based on deep learning and the use of a Deep Binary Hashed Convolutional Neural Network (DBH-CNN)(iii)
Student’st-tests, regression analyses, and analyses of variance (ANOVA) are used for statistical analysisThe remaining work is divided into the following sections: Section2, which includes the literature review and problem statement; Section 3, which includes the proposed methodology; Section 4, which includes the result and discussion; and Section 5, which includes the conclusion.


## 2. Literature Review

Virtual reality (VR) has been the subject of much research by academics interested in its potential in the classroom Table1. The martial arts served an indispensable function in the classroom because of their position as a transmitter of national cultural patrimony via sport. They discussed the challenges and innovative methods for successful martial arts education in higher education from many perspectives. For the first time, there was a theoretical foundation for how best to teach martial arts in academic settings [9]. To better educate, Zhang et al. [10] presented their system’s architecture and execution. The sentence to be change as a "The VR technology was incorporated into the system to improve student engagement and classroom efficiency. The findings demonstrate that virtual reality technology has the potential to alter the standard method of instruction, pique students’ curiosity in schoolwork, and enhance the standard of education in Korea [11]. The study’s findings paved the way for more VR technology use in the classroom. In their presentation of a calculus course in “virtual reality,” Bos et al. [12] detail how lectures might be adapted for use in VR and AR hardware systems. The primary goal of Pu and Yang [13] was to integrate VR technology into courses on political and ideological theory in higher education. In the study, students served as participants and were split into two groups: the experimental and the control. Groups A1 and B2 engaged in VR classroom learning reported 8.2% and 8.1% increases in political theory class interest, correspondingly [14]. The implications of this cutting-edge technology have not been adequately explored or explained [15]. Studying the psychological and social aspects of working together in a virtual environment was the subject of this empirical study. We piloted the use of a collaborative immersive virtual environment (CIVE) for instruction in two hypsography-related situations, the creation of which was the product of a multidisciplinary team. Using convolutional neural networks (CNNs), Kamel et al. [16] present a system called Tai Chi for teaching Tai Chi based on position estimation. Because of the limitations of conventional teaching techniques like one-to-many tutorials and video viewing, we designed our solution to address this issue. The VR-supported hybrid flipped classroom paradigm introduced and studied by Xiao and Hong [17] may provide a three-dimensional virtual learning scenario that mimics a user’s physical presence in a virtual or fictional setting, providing a novel cognitive experience. University professors’ and students’ perspectives on the notion of flipped learning and the realization of autonomous learning via flipped learning are presented by Antonova and Mernkov [18]. Survey findings indicate that student adaptation to flipped learning is challenging. The kids cannot learn on their own. To quantify the benefits of flipped classes in higher education, Salihu and Zayyanu [19] systematically reviewed 43 empirical studies of students’ cognitive, emotional, and interpersonal outcomes. In higher education, the flipped classroom model promotes a long-term, collaborative, and learner-centered classroom [20]. The implementation of flipped learning in a 14-week undergraduate biology course uses virtual worlds as self-learning medium and then explores how students responded to this method[21]. In the essay, a flipped learning experiment on preservice teacher preparation using remote learning is described [22]. To statistically examine 20 domestic and foreign experimental investigations of flipped classrooms, the meta-analysis approach was used [23]. To look at the outcomes of students’ academic performance and reactions to flipped classes, Pardimin et al. [24] develop a system that can publish English test papers, generate and administer a personal library of test papers, track students’ practice progress, and evaluate students’ performance. This system consists of a teacher, a student, and an administrator [25].Table 1S. no.ReferenceMethodsDrawbacks1Goehle [9]With an explanation of how the lecture was implemented in both virtual reality and an augmented reality hardware system, it gives a lesson for a typical Calculus subject that is based on “virtual reality.”The foundation of traditional education is interpersonal relationships and direct human communication. The connections between pupils and general human communication may suffer as a result.2Zhang et al. [10]The primary intention of this essay is to integrate virtual reality technology into college and university courses on political and ideological philosophy.Due to the small sample size and lack of expertise, the VR classroom suggested in this article could have certain disadvantages.4Bos et al. [12]The use of VR technology, programs, and material as a teaching and learning tool for geography is discussed.There are several restrictions when trainers display motions since martial arts routines are complicated.5Pu and Yang [13]To investigate the use of virtual reality in teaching martial arts.Virtual reality technology and physical education integration and development have emerged as a new trend, but research in the area of martial arts is still in the theoretical stage and lacks concrete application countermeasures and plans, necessitating additional investigation and study.6Šašinka et al. [15]Their multidisciplinary team created a special application for establishing a collaborative immersive virtual environment (CIVE) as a software solution for educational purposes.Fostering team cohesiveness while members of the team work remotely is one of the main difficulties of managing virtual teams.7Kamel et al. [16]Convolutional neural networks (CNNs) are the basis of the iTai-Chi, Tai Chi training system. Their methodology attempts to address the drawbacks of conventional teaching techniques like one-to-one tutorials and video viewing that result from a lack of enough precise feedback.Since there is inadequate data in the third dimension, many researchers struggle to estimate 3D VR in a single shot.8Xiao and Hong [17]A VR-supported hybrid flipped classroom model was developed, and its characteristics were examined. This model might provide a three-dimensional virtual learning environment that would imitate a user’s actual presence in an imagined or virtual setting, which would be a novel cognitive experience.There were challenges in education, such as the necessity for instructors and pupils to adapt to their new duties.9Antonova and Mernkov [18]It outlines students’ attitudes regarding the notion of autonomous learning achieved via flipped learning as well as university lecturers’ perspectives of the concept.The research found several issues with the flipped learning model’s adoption in higher education.10Jang and Kim [20]The 43 empirical studies of students’ cognitive, emotional, and interpersonal outcomes to quantify the impact of flipped classes in higher education.The flipped classroom is not a magic bullet, and how well students use the preclass time is a major factor in how beneficial it is.11Yano [21]The deployment of flipped learning in a 14-week undergraduate biology course employs virtual environments as self-learning media and then discusses how students responded to this strategy.Students may find it difficult to study independently utilizing these mediums, especially if the topic is new to them or contains abstract ideas.12Prokhorova et al. [22]In the essay, a flipped learning experiment on preservice teacher preparation using remote learning is described.The lack of efficient teaching techniques in distant learning led to instructor uncertainty and forced them to change their lesson plans, instructional strategies, and communication instruments.13Zhang et al. [23]To statistically examine 20 domestic and foreign experimental investigations of flipped classrooms, the meta-analysis approach was used.When a class is flipped, more preparation and effort must be spent on content development than in a conventional setting. This is especially true when course material or certain classroom activities are moved online.14Pardimin et al. [24]To look at the outcomes of students’ academic performance and reactions to flipped classes.The unconventional setting might be difficult for students who have never experienced flipped learning.15Jin [25]They develop a system that can publish English test papers, generate and administer a personal library of test papers, track students’ practice progress, and evaluate students’ performance. This system consists of a teacher, a student, and an administrator.These early studies have certain relative limitations in that they solely cover technology and explore little of the interaction between knowledge engineering and applied linguistics theory.Research into the topic shows that although many academics study the impact of education on students, very few investigate the effects of flipping the classroom while teaching martial arts. Some of the data preprocessing that machine learning generally involve is eliminated. These algorithms are capable of processing text and visual data that is unstructured. When compared to previous techniques, DBH-CNN’s major benefit is its ability to automatically and unassisted identify key characteristics.


### 2.1. Problem Statement

There are several issues with real classroom instruction right now. Consequently, it is still important to maintain a commitment to pedagogical innovation and correction and to actively adopt a variety of methods to guarantee the steady development of the pedagogical process. A more effective kind of education than the standard lecture format is one that emphasizes the development of students’ bodies and interests. To apply the flipped classroom method, it is recommended that college Taijiquan teachers use the Internet and other multimedia carriers. Ability building should focus on helping students improve their learning, thinking, and doing skills. By flipping their classrooms, educators may better grasp their students’ situations.


## 2.1. Problem Statement

There are several issues with real classroom instruction right now. Consequently, it is still important to maintain a commitment to pedagogical innovation and correction and to actively adopt a variety of methods to guarantee the steady development of the pedagogical process. A more effective kind of education than the standard lecture format is one that emphasizes the development of students’ bodies and interests. To apply the flipped classroom method, it is recommended that college Taijiquan teachers use the Internet and other multimedia carriers. Ability building should focus on helping students improve their learning, thinking, and doing skills. By flipping their classrooms, educators may better grasp their students’ situations.


## 3. Proposed Methodology

The deep learning algorithm is used to suggest how virtual reality should be built. The students are put into two groups: the control group and the study group. Traditional teaching is given to the control group, while Deep Binary Hashed Convolutional Neural Network-based VR-assisted deep learning teaching is given to the study group (DBH-CNN). Statistical analysis is performed using Student’st-tests, regression analysis, and analysis of variance. Figure 2 shows the proposed methodology.Figure 2
Proposed methodology.


### 3.1. Student Dataset

“Student datasets are collected from Nankai University, Tianjin, China. As a basis of the university’s clearance of this research, students in grades one through four were registered via online study platforms and groups recently formed for the new semester’s remote learning to participate in the study from 10 am February 15 to 10 am February 17, 2020. Table2 depicts the data description.”Table 2
Data description [26].
Total quantityMenWomen722229084314


### 3.2. Splitting of Groups

Here, the gathered data are separated into 2 teams, namely, the control group and the study group. The traditional education is provided to the students in the control group, which included 3522 participants, while the study group makes use of a Deep Binary Hashed Convolutional Neural Network (DBH-CNN) to get deep learning-based VR-based teaching which included 3700 participants.


#### 3.2.1. Traditional Teaching

Typically, in a traditional classroom setting, students strive to take notes as quickly as the instructor speaks. They are too busy attempting to write down the teacher’s words to stop and consider the lesson, which means they may miss some key aspects. By contrast, when students use video and other prepared material, they are in charge of their learning; they may pause, rewind, and skip through content at their convenience. Anyone who is learning English as a second language might benefit from taking the courses many times. Collaborative learning projects may also promote students’ socialization, collaboration, and cultural diversity, which in turn facilitates students’ ability to assist one another in learning and to work together regardless of their degrees of expertise. In-class activities including experiential exercises, team projects, problem sets, and assignments that were formerly given as individual homework allow teachers to spend more time focusing on fostering synthesis and exploring application.


#### 3.2.2. Application of Virtual Reality Technology

Virtual reality (VR) is a relatively new technology that merges many established ones, including computer graphics, sensor technology, and artificial intelligence technology. The computer simulation system creates a dynamic three-dimensional scene for the user to interact with, and this scene has innate interactive, interactive, and theoretical qualities due to the user’s reliance. It is possible to get multimodal input in the form of sight, sound, and touch. As a result of the deep learning algorithm being used in the creation of interactive teaching activities, not only can the demands of differentiated instruction be met, but also instructors’ ability to carry out their work effectively is enhanced. Learning activities in a virtual reality-based interactive classroom may be broken down into two categories: those that take place during class time and those that take place outside of class time, according to the classroom’s specific features. Through the application of VR technology, our real-world settings may be transformed into 3D immersive learning spaces that seamlessly merge the physical and digital realms. Flipped classrooms provide instructors the freedom to include interactive learning activities both within and outside of the traditional classroom while still delivering the course material. By analyzing the data collections of various learners and further extracting valuable learning patterns, smart learning environments could create new and more effective learning models that they could then use to make suggestions and recommendations to learners over time, possibly even during their future careers.


### 3.3. Deep Binary Hashed Convolutional Neural Network (DBH-CNN)

In this part, we provide the overall architecture for binary hashing, and then, we discuss the DBH-CNN approach we suggest for binary hashing. It is posed as a classification issue with a strict binary restriction. Finally, we show how the back-propagation method may be used to train the network to minimize a loss function.


#### 3.3.1. Binary Hashing

The goal of hashing methods is to find small binary code portrayals of data that are different enough from each other to be able to tell them apart. There have been different ideas for how to use hashing to figure out the projection matrix. Most of the older methods were limited by similar tools, nonlinear depictions that could not be scaled, or hand-crafted features that were not very good. It has recently been suggested to learn both nonlinear representations of pictures and basic binary codes simultaneously using hashing algorithms based on deep learning. Specifically, we propose a CNN-based hashing technique that incorporates a binary representation layer between the fully connected and classification layers. To ensure that the learned binary representations of the data maintain semantic similarity, we “point-by-point” train the network using a collection of pictures and the labels that correspond with them. Discrete value systems that are useful for classification have been included in the loss function to push the binary layer closer to them. This is because the network would be trained to provide consistent binary representations for identically tagged input.


#### 3.3.2. DBH-CNN

The proposed DBH-CNN is composed of the following layers: convolution, max-pooling, nonlinear transformations, fully connected layers, an LBE, and a classification layer. We forward propagate over the network and then quantize the output of the LBE layer to get the binary representations forX in the RGB space. As with regular layers, the output of one hidden layer is sent into the next. For this reason, we derive the l-th layer’s output in this way:
(1)Rl=hqlRl−1+gl.Thel-th layer is learned projection matrix and bias. The proposed network’s LBE layer also generates the following results:
(2)RL−1=hqL−1RL−2gL−1.


#### 3.3.3. Relaxation

Since the CNN is trained on considerably smaller batches of data, it is debatable whether or not the optimality of binary codes generated by the CNN using the same method is warranted if the whole training set is used to optimize the binary codes directly. By using the back-propagation technique and stochastic gradient descent (SGD), the network may be trained and improved. We modify its loss function and include a quantization requirement to aid it in learning more accurate binary representations. With our approach, a deep CNN simultaneously learns the feature representation and the binary coding. A complete optimization issue is tackled by using the back-propagation technique.


#### 3.3.4. Backpropagation

All through the training process, the functional form gradients concerning a system’s variables must be calculated. It is necessary to calculate partial derivatives to determine gradients∂Exn/∂qLji and ∂Eχn/∂gLj for a single training example xn and then recover the overall gradients by averaging over all the training examples. So, to calculate the loss function’s gradient relative to the output layer’s parameters, we do the following:
(3)∂Exn∂qLji=∂Eς∂aLj∂aLj∂qLji=biδjL,∂Exn∂gLj=∂Eς∂gLj∂aLj∂gLj=δjL.For the nonlinear functionh, the derivatives are denoted by h′·. With the use of the basic function of variables, we can calculate the error as
(4)δjL=f^–f.When expressed as a vector, we get(5)δL=y^–y.Afterward, we determine the goal function’s gradients relative to the LBE layer’s settings as follows:(6)∂Exn∂qjiL−1=∂Eς∂ajL−1∂ajL−1∂qjiL−1+γ2∂EQ∂ajL−1∂ajL−1∂qjiL−1,=RiL−2ϑjL−1+γ2ϱj=RiL−2δjL−1,∂Exn∂cjL−1=∂Eς∂ajL−1∂ajL−1∂cjL−1+γ2∂Eq∂ajL−1∂ajL−1∂cjL−1=ϑjL−1+γ2ϱj=δjL−1,where δjL−1 is categorization loss (j) and quantification loss (percent) make up the mistake at the jth neuron of the LBE layer. The vector representation of the loss stored procedure elements is calculated with the help of the basic function of derivatives.
(7)ϑL−1=qδL⊙1−tanh2aL−1,ϱ=−2b−RL−1⊙1−tanh2aL−1,where ϑL−1 denotes the Hadamard product. To calculate the gradient for the remaining layers, we may use the same method as before:
(8)∂Exn∂wjil=zil−1δjl,∂Exn∂cjil=δjl.where δjl is to get the jth part of the lth layer’s error vector; you would do something like:
(9)δl=qlTδl⊙h′al.Gradient descent is then used to update the parameters for each training sample in minibatches (Mb) as follows:(10)qjil=qjil−α∑n=1Mb∂Exn∂qjil,(11)gjil=gjil−α∑n=1Mb∂Exn∂gjil.Algorithm 1 proposes a complete learning algorithm for the proposed DBH-CNN which paces knowledge acquisition.Algorithm 1: DBH-CNN algorithm.
Input: Training examplesxnwiththeircorrespondinglabelsyn, mini-batch sizeMbof training examples, learning rateα,several iterations T, parameterγ.Output:Wl,cllL=1for t=1,2,……,T dofor each training example,xninMbdoSetz0=xnFor l =1,2,3,…,L doPerform feedforward computation forz1using(1) for other layers, and (2) andbn=tanhaL−1forLBElayer.endfor l=L,L-1,…1 doComputer the gradients according to ((3))- (9).endfor l=L,L-1,……….,1 doUpdate the weights and biases according to (10) and (11)endendendReturn:wl,cll−1L


### 3.4. Statistical Analysis

“Student’st-test, analysis of variance (ANOVA), and logistic regression analysis are the three methods that are used in statistical analysis “.


#### 3.4.1. Student’st-Test

Student’st-test is used to prove the hypothesis that there is no variance between the 3 groups. It is used for several circumstances, including the following.To get to a conclusion whether a test indicates (as an estimate of a group mean) differs significantly from a certain group mean.(12)T=W−pLI,where W is the sample mean, p is the population mean, and LI is the standard error of the mean
(13)S=W1−W2LIY1−W2,wherein W1−W2 signifies the distinction.It is determined to predict the data shown from the two variable samples differ considerably. Whenever variables are measured on the same participants during a drug, a pairedt-test is commonly used.The pairedt-test equation is
(14)n=mLIm,where m stands for the overall mean and LI stands for the standard error of the variance. To evaluate component variations, the t-test can be utilized. The t-test employs the proportion of deviations.


#### 3.4.2. Logistic Regression Model

The proportional odds (PO) framework, which is the most widely employed logistic design, has been developed.The groups could be sorted naturally whereas if response parameterY was ordinal, like “fitness condition good/moderate/bad.” The polytomous logistic regression model could be used, but it does not employ the sorting data. The use of cumulative possibilities, cumulative odds, and cumulative logits becomes a technique to compensate for the sorting. Those numbers have been specified in (g+1)-sorted groups.
(15)QB≤i=q1+⋯+qi,(16)B≤i=QB≤i1−QB≤i=q1+⋯+qiqi+1+⋯+qg+1,(17)B≤i=lnQB≤i1−QB≤i,i=1,⋯,g.Regarding ordinal response information, the cumulative logistic representation is specified as(18)logitB≤i=αi+β1A1+⋯+βikAk,i=1,⋯,g.We get multiple model equations (g) and one logistic variable (β) for every group/covariate pair.As a result, the generalized cumulative logistic regression method has a lot of variables. In other circumstances, though, a parsimonious method becomes feasible. If the logistic parameters are independent ofi, every covariate has only a single common variable. As a result, the cumulative odds were calculated as follows:
(19)oddsB≤i=expαiexpβ1A1+⋯+βkAk,i=1,⋯,g.Such that, them odds for every cut-off group i vary exclusively in terms of the intercepts, implying that the odds were proportionate.When the ordinal resultB gets associated with an underpinning underlying continuous variable, for instance, whenever B is indeed a categorized continuous variable like age ranges or cash economic classes, the somewhat strict proportional odds hypothesis could be particularly fitting. Ordinal variables that have been evaluated by an investigator, on the other hand, are another essential sort of ordinal variable. Regarding biomedical analysis, these factors are common.


#### 3.4.3. ANOVA Test

Analysis of variance (ANOVA) is a statistical technique used to break down aggregate reports of variability into more manageable subsets for use in follow-up analyses. When there are three or more data sets, a one-way ANOVA is performed to determine the correlation between the variables. The traditional analysis of variance (ANOVA)F-statistic is the percentage of the null model with the anthropic principle average sums of squares to the whole model. The parameters are calculated using the least-squares approach, with all variances being identical. This may be expressed as
(20)M=NSbetweenNSerror,where
(21)NSbetween=∑i=1joiB¯i−B¯2j−1m(22)NSerror=∑i=1j∑k=1oiBik−B¯i2h−j.The Welch-test-statistic is defined as(23)X=∑i=1jxiB¯i−B~2/j−11+2j−2/j2−1∑i=1j1−xi/v2/hi−1,where Xj=n/t12, v=∑i=1jXi, and B=1/v̇∑i=1oXiYi is defined as
(24)m=j2−13∑i=1o1−xj/v2/hi−1.The Brown-Forsythe-test-statistic is defined as(25)M∗=∑i=1ohiB¯i−B2∑i=1o1−hi/hSi2,whenLo is factual, the allocation of M∗ is appropriate by a central M distribution with degrees of freedom o−1 and m, where m is defined as
(26)1m=∑i=1odi2hi−1,di=1−hi/hSi2∑i=1o1−hi/hSi2.To calculate the generalizedp value, the generalized p value is now computed as p=1-l, where r is the sample size.
(27)l=HIo−1,h−oh−oo−1t~ch1t12C1C2,⋯,Co−1,h2t22C1C2,⋯,Co−1,h3t321−C2C3,⋯,Co−1,⋯,h1to21−Co−1.The prediction is calculated concerning the separate Beta stochastic process in anF-distribution having Io−1,h−o.
(28)Ck~beta∑i=1khi−12,hk+1−12,k=1,2,⋯,o−1.Thep value is calculated by numerically integrating the anticipated value in the p value formula about the beta random variables.


## 3.1. Student Dataset

“Student datasets are collected from Nankai University, Tianjin, China. As a basis of the university’s clearance of this research, students in grades one through four were registered via online study platforms and groups recently formed for the new semester’s remote learning to participate in the study from 10 am February 15 to 10 am February 17, 2020. Table2 depicts the data description.”Table 2
Data description [26].
Total quantityMenWomen722229084314


## 3.2. Splitting of Groups

Here, the gathered data are separated into 2 teams, namely, the control group and the study group. The traditional education is provided to the students in the control group, which included 3522 participants, while the study group makes use of a Deep Binary Hashed Convolutional Neural Network (DBH-CNN) to get deep learning-based VR-based teaching which included 3700 participants.


### 3.2.1. Traditional Teaching

Typically, in a traditional classroom setting, students strive to take notes as quickly as the instructor speaks. They are too busy attempting to write down the teacher’s words to stop and consider the lesson, which means they may miss some key aspects. By contrast, when students use video and other prepared material, they are in charge of their learning; they may pause, rewind, and skip through content at their convenience. Anyone who is learning English as a second language might benefit from taking the courses many times. Collaborative learning projects may also promote students’ socialization, collaboration, and cultural diversity, which in turn facilitates students’ ability to assist one another in learning and to work together regardless of their degrees of expertise. In-class activities including experiential exercises, team projects, problem sets, and assignments that were formerly given as individual homework allow teachers to spend more time focusing on fostering synthesis and exploring application.


### 3.2.2. Application of Virtual Reality Technology

Virtual reality (VR) is a relatively new technology that merges many established ones, including computer graphics, sensor technology, and artificial intelligence technology. The computer simulation system creates a dynamic three-dimensional scene for the user to interact with, and this scene has innate interactive, interactive, and theoretical qualities due to the user’s reliance. It is possible to get multimodal input in the form of sight, sound, and touch. As a result of the deep learning algorithm being used in the creation of interactive teaching activities, not only can the demands of differentiated instruction be met, but also instructors’ ability to carry out their work effectively is enhanced. Learning activities in a virtual reality-based interactive classroom may be broken down into two categories: those that take place during class time and those that take place outside of class time, according to the classroom’s specific features. Through the application of VR technology, our real-world settings may be transformed into 3D immersive learning spaces that seamlessly merge the physical and digital realms. Flipped classrooms provide instructors the freedom to include interactive learning activities both within and outside of the traditional classroom while still delivering the course material. By analyzing the data collections of various learners and further extracting valuable learning patterns, smart learning environments could create new and more effective learning models that they could then use to make suggestions and recommendations to learners over time, possibly even during their future careers.


## 3.2.1. Traditional Teaching

Typically, in a traditional classroom setting, students strive to take notes as quickly as the instructor speaks. They are too busy attempting to write down the teacher’s words to stop and consider the lesson, which means they may miss some key aspects. By contrast, when students use video and other prepared material, they are in charge of their learning; they may pause, rewind, and skip through content at their convenience. Anyone who is learning English as a second language might benefit from taking the courses many times. Collaborative learning projects may also promote students’ socialization, collaboration, and cultural diversity, which in turn facilitates students’ ability to assist one another in learning and to work together regardless of their degrees of expertise. In-class activities including experiential exercises, team projects, problem sets, and assignments that were formerly given as individual homework allow teachers to spend more time focusing on fostering synthesis and exploring application.


## 3.2.2. Application of Virtual Reality Technology

Virtual reality (VR) is a relatively new technology that merges many established ones, including computer graphics, sensor technology, and artificial intelligence technology. The computer simulation system creates a dynamic three-dimensional scene for the user to interact with, and this scene has innate interactive, interactive, and theoretical qualities due to the user’s reliance. It is possible to get multimodal input in the form of sight, sound, and touch. As a result of the deep learning algorithm being used in the creation of interactive teaching activities, not only can the demands of differentiated instruction be met, but also instructors’ ability to carry out their work effectively is enhanced. Learning activities in a virtual reality-based interactive classroom may be broken down into two categories: those that take place during class time and those that take place outside of class time, according to the classroom’s specific features. Through the application of VR technology, our real-world settings may be transformed into 3D immersive learning spaces that seamlessly merge the physical and digital realms. Flipped classrooms provide instructors the freedom to include interactive learning activities both within and outside of the traditional classroom while still delivering the course material. By analyzing the data collections of various learners and further extracting valuable learning patterns, smart learning environments could create new and more effective learning models that they could then use to make suggestions and recommendations to learners over time, possibly even during their future careers.


## 3.3. Deep Binary Hashed Convolutional Neural Network (DBH-CNN)

In this part, we provide the overall architecture for binary hashing, and then, we discuss the DBH-CNN approach we suggest for binary hashing. It is posed as a classification issue with a strict binary restriction. Finally, we show how the back-propagation method may be used to train the network to minimize a loss function.


### 3.3.1. Binary Hashing

The goal of hashing methods is to find small binary code portrayals of data that are different enough from each other to be able to tell them apart. There have been different ideas for how to use hashing to figure out the projection matrix. Most of the older methods were limited by similar tools, nonlinear depictions that could not be scaled, or hand-crafted features that were not very good. It has recently been suggested to learn both nonlinear representations of pictures and basic binary codes simultaneously using hashing algorithms based on deep learning. Specifically, we propose a CNN-based hashing technique that incorporates a binary representation layer between the fully connected and classification layers. To ensure that the learned binary representations of the data maintain semantic similarity, we “point-by-point” train the network using a collection of pictures and the labels that correspond with them. Discrete value systems that are useful for classification have been included in the loss function to push the binary layer closer to them. This is because the network would be trained to provide consistent binary representations for identically tagged input.


### 3.3.2. DBH-CNN

The proposed DBH-CNN is composed of the following layers: convolution, max-pooling, nonlinear transformations, fully connected layers, an LBE, and a classification layer. We forward propagate over the network and then quantize the output of the LBE layer to get the binary representations forX in the RGB space. As with regular layers, the output of one hidden layer is sent into the next. For this reason, we derive the l-th layer’s output in this way:
(1)Rl=hqlRl−1+gl.Thel-th layer is learned projection matrix and bias. The proposed network’s LBE layer also generates the following results:
(2)RL−1=hqL−1RL−2gL−1.


### 3.3.3. Relaxation

Since the CNN is trained on considerably smaller batches of data, it is debatable whether or not the optimality of binary codes generated by the CNN using the same method is warranted if the whole training set is used to optimize the binary codes directly. By using the back-propagation technique and stochastic gradient descent (SGD), the network may be trained and improved. We modify its loss function and include a quantization requirement to aid it in learning more accurate binary representations. With our approach, a deep CNN simultaneously learns the feature representation and the binary coding. A complete optimization issue is tackled by using the back-propagation technique.


### 3.3.4. Backpropagation

All through the training process, the functional form gradients concerning a system’s variables must be calculated. It is necessary to calculate partial derivatives to determine gradients∂Exn/∂qLji and ∂Eχn/∂gLj for a single training example xn and then recover the overall gradients by averaging over all the training examples. So, to calculate the loss function’s gradient relative to the output layer’s parameters, we do the following:
(3)∂Exn∂qLji=∂Eς∂aLj∂aLj∂qLji=biδjL,∂Exn∂gLj=∂Eς∂gLj∂aLj∂gLj=δjL.For the nonlinear functionh, the derivatives are denoted by h′·. With the use of the basic function of variables, we can calculate the error as
(4)δjL=f^–f.When expressed as a vector, we get(5)δL=y^–y.Afterward, we determine the goal function’s gradients relative to the LBE layer’s settings as follows:(6)∂Exn∂qjiL−1=∂Eς∂ajL−1∂ajL−1∂qjiL−1+γ2∂EQ∂ajL−1∂ajL−1∂qjiL−1,=RiL−2ϑjL−1+γ2ϱj=RiL−2δjL−1,∂Exn∂cjL−1=∂Eς∂ajL−1∂ajL−1∂cjL−1+γ2∂Eq∂ajL−1∂ajL−1∂cjL−1=ϑjL−1+γ2ϱj=δjL−1,where δjL−1 is categorization loss (j) and quantification loss (percent) make up the mistake at the jth neuron of the LBE layer. The vector representation of the loss stored procedure elements is calculated with the help of the basic function of derivatives.
(7)ϑL−1=qδL⊙1−tanh2aL−1,ϱ=−2b−RL−1⊙1−tanh2aL−1,where ϑL−1 denotes the Hadamard product. To calculate the gradient for the remaining layers, we may use the same method as before:
(8)∂Exn∂wjil=zil−1δjl,∂Exn∂cjil=δjl.where δjl is to get the jth part of the lth layer’s error vector; you would do something like:
(9)δl=qlTδl⊙h′al.Gradient descent is then used to update the parameters for each training sample in minibatches (Mb) as follows:(10)qjil=qjil−α∑n=1Mb∂Exn∂qjil,(11)gjil=gjil−α∑n=1Mb∂Exn∂gjil.Algorithm 1 proposes a complete learning algorithm for the proposed DBH-CNN which paces knowledge acquisition.Algorithm 1: DBH-CNN algorithm.
Input: Training examplesxnwiththeircorrespondinglabelsyn, mini-batch sizeMbof training examples, learning rateα,several iterations T, parameterγ.Output:Wl,cllL=1for t=1,2,……,T dofor each training example,xninMbdoSetz0=xnFor l =1,2,3,…,L doPerform feedforward computation forz1using(1) for other layers, and (2) andbn=tanhaL−1forLBElayer.endfor l=L,L-1,…1 doComputer the gradients according to ((3))- (9).endfor l=L,L-1,……….,1 doUpdate the weights and biases according to (10) and (11)endendendReturn:wl,cll−1L


## 3.3.1. Binary Hashing

The goal of hashing methods is to find small binary code portrayals of data that are different enough from each other to be able to tell them apart. There have been different ideas for how to use hashing to figure out the projection matrix. Most of the older methods were limited by similar tools, nonlinear depictions that could not be scaled, or hand-crafted features that were not very good. It has recently been suggested to learn both nonlinear representations of pictures and basic binary codes simultaneously using hashing algorithms based on deep learning. Specifically, we propose a CNN-based hashing technique that incorporates a binary representation layer between the fully connected and classification layers. To ensure that the learned binary representations of the data maintain semantic similarity, we “point-by-point” train the network using a collection of pictures and the labels that correspond with them. Discrete value systems that are useful for classification have been included in the loss function to push the binary layer closer to them. This is because the network would be trained to provide consistent binary representations for identically tagged input.


## 3.3.2. DBH-CNN

The proposed DBH-CNN is composed of the following layers: convolution, max-pooling, nonlinear transformations, fully connected layers, an LBE, and a classification layer. We forward propagate over the network and then quantize the output of the LBE layer to get the binary representations forX in the RGB space. As with regular layers, the output of one hidden layer is sent into the next. For this reason, we derive the l-th layer’s output in this way:
(1)Rl=hqlRl−1+gl.Thel-th layer is learned projection matrix and bias. The proposed network’s LBE layer also generates the following results:
(2)RL−1=hqL−1RL−2gL−1.


## 3.3.3. Relaxation

Since the CNN is trained on considerably smaller batches of data, it is debatable whether or not the optimality of binary codes generated by the CNN using the same method is warranted if the whole training set is used to optimize the binary codes directly. By using the back-propagation technique and stochastic gradient descent (SGD), the network may be trained and improved. We modify its loss function and include a quantization requirement to aid it in learning more accurate binary representations. With our approach, a deep CNN simultaneously learns the feature representation and the binary coding. A complete optimization issue is tackled by using the back-propagation technique.


## 3.3.4. Backpropagation

All through the training process, the functional form gradients concerning a system’s variables must be calculated. It is necessary to calculate partial derivatives to determine gradients∂Exn/∂qLji and ∂Eχn/∂gLj for a single training example xn and then recover the overall gradients by averaging over all the training examples. So, to calculate the loss function’s gradient relative to the output layer’s parameters, we do the following:
(3)∂Exn∂qLji=∂Eς∂aLj∂aLj∂qLji=biδjL,∂Exn∂gLj=∂Eς∂gLj∂aLj∂gLj=δjL.For the nonlinear functionh, the derivatives are denoted by h′·. With the use of the basic function of variables, we can calculate the error as
(4)δjL=f^–f.When expressed as a vector, we get(5)δL=y^–y.Afterward, we determine the goal function’s gradients relative to the LBE layer’s settings as follows:(6)∂Exn∂qjiL−1=∂Eς∂ajL−1∂ajL−1∂qjiL−1+γ2∂EQ∂ajL−1∂ajL−1∂qjiL−1,=RiL−2ϑjL−1+γ2ϱj=RiL−2δjL−1,∂Exn∂cjL−1=∂Eς∂ajL−1∂ajL−1∂cjL−1+γ2∂Eq∂ajL−1∂ajL−1∂cjL−1=ϑjL−1+γ2ϱj=δjL−1,where δjL−1 is categorization loss (j) and quantification loss (percent) make up the mistake at the jth neuron of the LBE layer. The vector representation of the loss stored procedure elements is calculated with the help of the basic function of derivatives.
(7)ϑL−1=qδL⊙1−tanh2aL−1,ϱ=−2b−RL−1⊙1−tanh2aL−1,where ϑL−1 denotes the Hadamard product. To calculate the gradient for the remaining layers, we may use the same method as before:
(8)∂Exn∂wjil=zil−1δjl,∂Exn∂cjil=δjl.where δjl is to get the jth part of the lth layer’s error vector; you would do something like:
(9)δl=qlTδl⊙h′al.Gradient descent is then used to update the parameters for each training sample in minibatches (Mb) as follows:(10)qjil=qjil−α∑n=1Mb∂Exn∂qjil,(11)gjil=gjil−α∑n=1Mb∂Exn∂gjil.Algorithm 1 proposes a complete learning algorithm for the proposed DBH-CNN which paces knowledge acquisition.Algorithm 1: DBH-CNN algorithm.
Input: Training examplesxnwiththeircorrespondinglabelsyn, mini-batch sizeMbof training examples, learning rateα,several iterations T, parameterγ.Output:Wl,cllL=1for t=1,2,……,T dofor each training example,xninMbdoSetz0=xnFor l =1,2,3,…,L doPerform feedforward computation forz1using(1) for other layers, and (2) andbn=tanhaL−1forLBElayer.endfor l=L,L-1,…1 doComputer the gradients according to ((3))- (9).endfor l=L,L-1,……….,1 doUpdate the weights and biases according to (10) and (11)endendendReturn:wl,cll−1L


## 3.4. Statistical Analysis

“Student’st-test, analysis of variance (ANOVA), and logistic regression analysis are the three methods that are used in statistical analysis “.


### 3.4.1. Student’st-Test

Student’st-test is used to prove the hypothesis that there is no variance between the 3 groups. It is used for several circumstances, including the following.To get to a conclusion whether a test indicates (as an estimate of a group mean) differs significantly from a certain group mean.(12)T=W−pLI,where W is the sample mean, p is the population mean, and LI is the standard error of the mean
(13)S=W1−W2LIY1−W2,wherein W1−W2 signifies the distinction.It is determined to predict the data shown from the two variable samples differ considerably. Whenever variables are measured on the same participants during a drug, a pairedt-test is commonly used.The pairedt-test equation is
(14)n=mLIm,where m stands for the overall mean and LI stands for the standard error of the variance. To evaluate component variations, the t-test can be utilized. The t-test employs the proportion of deviations.


### 3.4.2. Logistic Regression Model

The proportional odds (PO) framework, which is the most widely employed logistic design, has been developed.The groups could be sorted naturally whereas if response parameterY was ordinal, like “fitness condition good/moderate/bad.” The polytomous logistic regression model could be used, but it does not employ the sorting data. The use of cumulative possibilities, cumulative odds, and cumulative logits becomes a technique to compensate for the sorting. Those numbers have been specified in (g+1)-sorted groups.
(15)QB≤i=q1+⋯+qi,(16)B≤i=QB≤i1−QB≤i=q1+⋯+qiqi+1+⋯+qg+1,(17)B≤i=lnQB≤i1−QB≤i,i=1,⋯,g.Regarding ordinal response information, the cumulative logistic representation is specified as(18)logitB≤i=αi+β1A1+⋯+βikAk,i=1,⋯,g.We get multiple model equations (g) and one logistic variable (β) for every group/covariate pair.As a result, the generalized cumulative logistic regression method has a lot of variables. In other circumstances, though, a parsimonious method becomes feasible. If the logistic parameters are independent ofi, every covariate has only a single common variable. As a result, the cumulative odds were calculated as follows:
(19)oddsB≤i=expαiexpβ1A1+⋯+βkAk,i=1,⋯,g.Such that, them odds for every cut-off group i vary exclusively in terms of the intercepts, implying that the odds were proportionate.When the ordinal resultB gets associated with an underpinning underlying continuous variable, for instance, whenever B is indeed a categorized continuous variable like age ranges or cash economic classes, the somewhat strict proportional odds hypothesis could be particularly fitting. Ordinal variables that have been evaluated by an investigator, on the other hand, are another essential sort of ordinal variable. Regarding biomedical analysis, these factors are common.


### 3.4.3. ANOVA Test

Analysis of variance (ANOVA) is a statistical technique used to break down aggregate reports of variability into more manageable subsets for use in follow-up analyses. When there are three or more data sets, a one-way ANOVA is performed to determine the correlation between the variables. The traditional analysis of variance (ANOVA)F-statistic is the percentage of the null model with the anthropic principle average sums of squares to the whole model. The parameters are calculated using the least-squares approach, with all variances being identical. This may be expressed as
(20)M=NSbetweenNSerror,where
(21)NSbetween=∑i=1joiB¯i−B¯2j−1m(22)NSerror=∑i=1j∑k=1oiBik−B¯i2h−j.The Welch-test-statistic is defined as(23)X=∑i=1jxiB¯i−B~2/j−11+2j−2/j2−1∑i=1j1−xi/v2/hi−1,where Xj=n/t12, v=∑i=1jXi, and B=1/v̇∑i=1oXiYi is defined as
(24)m=j2−13∑i=1o1−xj/v2/hi−1.The Brown-Forsythe-test-statistic is defined as(25)M∗=∑i=1ohiB¯i−B2∑i=1o1−hi/hSi2,whenLo is factual, the allocation of M∗ is appropriate by a central M distribution with degrees of freedom o−1 and m, where m is defined as
(26)1m=∑i=1odi2hi−1,di=1−hi/hSi2∑i=1o1−hi/hSi2.To calculate the generalizedp value, the generalized p value is now computed as p=1-l, where r is the sample size.
(27)l=HIo−1,h−oh−oo−1t~ch1t12C1C2,⋯,Co−1,h2t22C1C2,⋯,Co−1,h3t321−C2C3,⋯,Co−1,⋯,h1to21−Co−1.The prediction is calculated concerning the separate Beta stochastic process in anF-distribution having Io−1,h−o.
(28)Ck~beta∑i=1khi−12,hk+1−12,k=1,2,⋯,o−1.Thep value is calculated by numerically integrating the anticipated value in the p value formula about the beta random variables.


## 3.4.1. Student’st-Test

Student’st-test is used to prove the hypothesis that there is no variance between the 3 groups. It is used for several circumstances, including the following.To get to a conclusion whether a test indicates (as an estimate of a group mean) differs significantly from a certain group mean.(12)T=W−pLI,where W is the sample mean, p is the population mean, and LI is the standard error of the mean
(13)S=W1−W2LIY1−W2,wherein W1−W2 signifies the distinction.It is determined to predict the data shown from the two variable samples differ considerably. Whenever variables are measured on the same participants during a drug, a pairedt-test is commonly used.The pairedt-test equation is
(14)n=mLIm,where m stands for the overall mean and LI stands for the standard error of the variance. To evaluate component variations, the t-test can be utilized. The t-test employs the proportion of deviations.


## 3.4.2. Logistic Regression Model

The proportional odds (PO) framework, which is the most widely employed logistic design, has been developed.The groups could be sorted naturally whereas if response parameterY was ordinal, like “fitness condition good/moderate/bad.” The polytomous logistic regression model could be used, but it does not employ the sorting data. The use of cumulative possibilities, cumulative odds, and cumulative logits becomes a technique to compensate for the sorting. Those numbers have been specified in (g+1)-sorted groups.
(15)QB≤i=q1+⋯+qi,(16)B≤i=QB≤i1−QB≤i=q1+⋯+qiqi+1+⋯+qg+1,(17)B≤i=lnQB≤i1−QB≤i,i=1,⋯,g.Regarding ordinal response information, the cumulative logistic representation is specified as(18)logitB≤i=αi+β1A1+⋯+βikAk,i=1,⋯,g.We get multiple model equations (g) and one logistic variable (β) for every group/covariate pair.As a result, the generalized cumulative logistic regression method has a lot of variables. In other circumstances, though, a parsimonious method becomes feasible. If the logistic parameters are independent ofi, every covariate has only a single common variable. As a result, the cumulative odds were calculated as follows:
(19)oddsB≤i=expαiexpβ1A1+⋯+βkAk,i=1,⋯,g.Such that, them odds for every cut-off group i vary exclusively in terms of the intercepts, implying that the odds were proportionate.When the ordinal resultB gets associated with an underpinning underlying continuous variable, for instance, whenever B is indeed a categorized continuous variable like age ranges or cash economic classes, the somewhat strict proportional odds hypothesis could be particularly fitting. Ordinal variables that have been evaluated by an investigator, on the other hand, are another essential sort of ordinal variable. Regarding biomedical analysis, these factors are common.


## 3.4.3. ANOVA Test

Analysis of variance (ANOVA) is a statistical technique used to break down aggregate reports of variability into more manageable subsets for use in follow-up analyses. When there are three or more data sets, a one-way ANOVA is performed to determine the correlation between the variables. The traditional analysis of variance (ANOVA)F-statistic is the percentage of the null model with the anthropic principle average sums of squares to the whole model. The parameters are calculated using the least-squares approach, with all variances being identical. This may be expressed as
(20)M=NSbetweenNSerror,where
(21)NSbetween=∑i=1joiB¯i−B¯2j−1m(22)NSerror=∑i=1j∑k=1oiBik−B¯i2h−j.The Welch-test-statistic is defined as(23)X=∑i=1jxiB¯i−B~2/j−11+2j−2/j2−1∑i=1j1−xi/v2/hi−1,where Xj=n/t12, v=∑i=1jXi, and B=1/v̇∑i=1oXiYi is defined as
(24)m=j2−13∑i=1o1−xj/v2/hi−1.The Brown-Forsythe-test-statistic is defined as(25)M∗=∑i=1ohiB¯i−B2∑i=1o1−hi/hSi2,whenLo is factual, the allocation of M∗ is appropriate by a central M distribution with degrees of freedom o−1 and m, where m is defined as
(26)1m=∑i=1odi2hi−1,di=1−hi/hSi2∑i=1o1−hi/hSi2.To calculate the generalizedp value, the generalized p value is now computed as p=1-l, where r is the sample size.
(27)l=HIo−1,h−oh−oo−1t~ch1t12C1C2,⋯,Co−1,h2t22C1C2,⋯,Co−1,h3t321−C2C3,⋯,Co−1,⋯,h1to21−Co−1.The prediction is calculated concerning the separate Beta stochastic process in anF-distribution having Io−1,h−o.
(28)Ck~beta∑i=1khi−12,hk+1−12,k=1,2,⋯,o−1.Thep value is calculated by numerically integrating the anticipated value in the p value formula about the beta random variables.


## 4. Result and Discussion

Experiments are done to further confirm its effectiveness in real implementation. For experiment’s validity, use the attribute information about the virtual reality classroom account gathered from the network. In this study, the parameters are used accuracy, learning efficiency, achievement improvement rate, physical quality of the students, affection and attitude indicators, VR technology cognition, and application. The findings were compared to those obtained using existing approaches. The existing methods like “Recursive Neural Network (RNN), Graph Neural Network (GNN), Back propagation-Neural Network (BP-NN), and Multilayer Neural network (ML-NN)” are compared with the proposed method to attain the greatest performance in this work.The term “learning efficiency” refers to the correlation between a learner’s PME and an outcome measure of performance, such as a test score or the time it takes to complete a task properly. Teaching and learning strategies that encourage students to take an active role in their education and growth are what we mean when we talk about “effective learning.” Consider it a step beyond rote memorization and imitation of classroom practices to educate students well on how to learn independently. The quantitative judgment and statistical analysis of a teacher’s performance in an interactive classroom are carried out via the lens of a teaching benefit and a novel assessment method. Figure3 depicts the comparison of learning efficiency for existing and proposed methodologies.Figure 3
Comparison of learning efficiency for existing and proposed methodologies.When compared to the existing method, the proposed method has greater learning efficiency. RNN has a 75%, GNN has a 65%, BP-NN has an 84%, ML-NN has a 58%, and the proposed DPH-CNN has 96% learning efficiency.In the analysis stage, accuracy is the percentage of times a classifier correctly predicted the actual value of a label. It may also be stated as a ratio of the number of correct evaluations to the overall number of tests. The accuracy is calculated using the equation:(29)Accuracy=A+BA+B+C+D,where A is the true negative, B is the true positive, C is the false positive, and D is the false negative.Figure4 shows the comparison of accuracy for existing and proposed methodologies. When compared to the existing method, the proposed method has greater accuracy. RNN has a 55%, GNN has an 88%, BP-NN has a 66%, ML-NN has a 70%, and the proposed DPH-CNN has 98% accuracy.Figure 4
Comparison of accuracy for existing and proposed methodologies.A design approach is used as a pedagogical platform to investigate the viability of a deep learning-based, augmented reality-based, interactive classroom. After using this strategy, the virtual reality interactive classroom is much more stable. The primary reason is that a deep learning algorithm is utilized to examine the VR-interactive classroom throughout the creation phase of this methodology.Figure5 shows the comparison of achievement improvement rates for existing and proposed methodologies. When compared to the existing method, the proposed method has a greater achievement improvement rate. RNN has an 85%, GNN has a 66%, BP-NN has a 75%, ML-NN has a 55%, and the proposed DPH-CNN has a 94% achievement improvement rate.Figure 5
Comparison of achievement improvement rate for existing and proposed methodologies.The students in the experimental group and the control group are compared based on their physical appearance and general health in this research. Figure6 displays the outcomes of the tests. Therefore, this article indicates that both conventional instruction and instruction aided by virtual reality technology both contribute to the general enhancement of students’ physical well-being. Students may get a psychological high from using VR technology to study before or after class, but this has little impact on their motivation. It does not promise that pupils will be able to push over their laziness and keep training. The author hypothesizes that the experimental group’s slightly better test value of basic physical fitness compared to the control group is due to the increased excitement of the students to study martial arts made possible by the use of virtual reality technology.Figure 6
Comparison of basic physical quality of the students in the experimental group and the control group.Interaction with the material and a desire to learn are examples of students’ learning interests and sentimental openness to instruction, only by storing up knowledge and abilities unless they are sufficiently motivated to do so. Self-confidence refers to a person’s belief in his or her abilities to accomplish a task while learning motivation describes the inner drive to encourage, inspire, and direct kids toward academic success. Figure7 shows the results of a preliminary statistical analysis of emotional disposition test markers of the groups of students during learning.Figure 7
Comparative analysis of two groups of students’ affection and attitude indicators after the experiment and before the experiment.Students in the experimental group demonstrated more enthusiasm, desire, and confidence in their ability to study martial arts than those in the control group did. Since students in the experimental group wore VR headsets before class, they benefited from a new immersive learning experience, and the physical motions of martial arts were more clearly conveyed in front of their eyes thanks to the situational substitution teaching approach. The system may track the evolution of martial arts moves from a variety of perspectives.As seen in Figure8, there are five possible degrees of agreement (strongly agree, agree, slightly agree, unsure, and disagree) represented by the letters A, B, C, D, and E, respectively. According to the poll, using VR to teach martial arts has been well received by pupils, and many report a renewed interest in the subject as a result. The primary reason is that the stereoscopic rotation of the gadget allows for seeing the movie from any angle and in any direction. In addition to providing students with a more engaging learning environment, VR may improve their capacity to watch and analyze movements and aid them in more thoroughly correcting incorrect movements based on accurate comprehension and recognition. It raises the bar for what is considered “normal” in martial arts. On the other hand, it is a useful supplementary resource for students’ education. It transforms kids’ learning strategies while fostering more independence and curiosity in the classroom. However, not all pupils are comfortable with this methodology.Figure 8
VR technology cognition and application.


### 4.1. Discussion

The usage of RNNs in disciplines including pattern recognition, image processing, intelligent control, signal processing optimization calculations, and others have made them large-scale nonlinear dynamic systems with feedback loops. The issue of vanishing gradients, which affects RNNs [27], makes it difficult for these networks to learn from extended data sequences. Gradients are a vital part of the RNN variable update, and as they decrease in magnitude, the learning gains from these updates diminish to zero. Most fundamental GNN restrictions [6] are works of GNN confined to a set number of points. Time and space complexity are greater. We can reduce the time spent on edge processing graphs by classifying them into fewer categories. Based on big data technologies and GNN, it creates an interactive teaching system for Tai Chi that is ideological and political. Each expert is only used for a small number of input instances in BP-NN [28]. As new situations emerge, the Mixture of Experts is unable to swiftly adjust its parsing. Existing Mixtures of Experts cannot be modified to include additional types of expertise if such types are suddenly needed. The BP-NN adjusts to the speed of learning. The learning step length selection, difficulty defining the size and direction of the weights, and difficulty controlling the learning rate are all improved in the method for the conventional BP neural network. Most notably, ML-NN [29] has the drawbacks of losing neighborhood information, having additional parameters to optimize, and not being translation invariant. This implies that neural networks may typically be tried against a problem with an unknown form even if other kinds of machine learning algorithms have already failed; ML-NN flexibility is used in solving issues with nonlinear geometries. So in this work, we propose an architecture of virtual reality based on the deep learning algorithm of the Deep Binary Hashed Convolutional Neural Network (DBH-CNN).


## 4.1. Discussion

The usage of RNNs in disciplines including pattern recognition, image processing, intelligent control, signal processing optimization calculations, and others have made them large-scale nonlinear dynamic systems with feedback loops. The issue of vanishing gradients, which affects RNNs [27], makes it difficult for these networks to learn from extended data sequences. Gradients are a vital part of the RNN variable update, and as they decrease in magnitude, the learning gains from these updates diminish to zero. Most fundamental GNN restrictions [6] are works of GNN confined to a set number of points. Time and space complexity are greater. We can reduce the time spent on edge processing graphs by classifying them into fewer categories. Based on big data technologies and GNN, it creates an interactive teaching system for Tai Chi that is ideological and political. Each expert is only used for a small number of input instances in BP-NN [28]. As new situations emerge, the Mixture of Experts is unable to swiftly adjust its parsing. Existing Mixtures of Experts cannot be modified to include additional types of expertise if such types are suddenly needed. The BP-NN adjusts to the speed of learning. The learning step length selection, difficulty defining the size and direction of the weights, and difficulty controlling the learning rate are all improved in the method for the conventional BP neural network. Most notably, ML-NN [29] has the drawbacks of losing neighborhood information, having additional parameters to optimize, and not being translation invariant. This implies that neural networks may typically be tried against a problem with an unknown form even if other kinds of machine learning algorithms have already failed; ML-NN flexibility is used in solving issues with nonlinear geometries. So in this work, we propose an architecture of virtual reality based on the deep learning algorithm of the Deep Binary Hashed Convolutional Neural Network (DBH-CNN).


## 5. Conclusion

It is clear that there are problems with the way we have always done things in the classroom, but it does not mean conventional techniques are without value. Advances in science and technology have a subtle but discernible impact on educational reform and pedagogical experimentation. The key to advancing the reform of pedagogical practices is figuring out how to more effectively use cutting-edge scientific and technological tools in the classroom. In the video presentation, the activity is shown in a more three-dimensional, intuitive, and multiangle way. The film is enhanced with action explanations and music, and students utilize virtual reality (VR) technology to simulate realistic training scenarios, all to better their ability to grasp and recall martial arts techniques. It performs multidirectional observation and gains an immersed learning opportunity, which in turn increases the quality of students’ martial arts movements by their attention and delight, and gradually improves the quality of motions over time. When compared to other existing approaches like RNN, GNN, BP-NN, and ML-NN, the DBH-CNN has 98% accuracy. However, research in the field of martial arts is still at the theoretical stage and lacks practical application countermeasures and plans, demanding more inquiry and study. Virtual reality technology has developed as a new trend. This suggests that there is merit and potential in using VR technology to train martial artists. There are, of course, issues with using VR technology to teach martial arts, and we should do everything we can to address them. To gain the respect and appreciation of students, it must also do more research on the benefits of using virtual reality technology in its professional context. It seems clear that the use of VR technology to aid martial arts is practical. There is still little research on the use of VR and its real effects on instruction and student learning. Future research will involve detailed quantitative validation of the results, including comparisons of their efficacy to conventional methods like GIS.



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*Source: 3921842-2022-10-06.xml*