# Design and Exploration of College English Reading, Writing, and Translation Teaching Classroom Based on Machine Learning

Authors: Xushan Ruan
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The aims are to ameliorate the dull classroom atmosphere and unsatisfactory teacher-student interaction and cultivate students’ reading, writing, and translation (R-W-T) English proficiency level (EPL). Specifically, this paper designs an optimized polynomial kernel-based support vector machine (SVM) classification algorithm based on the machine learning (ML) theory. Firstly, this paper expounds on the correlation between ML and teaching classrooms to analyze the optimization direction in the application of ML in classroom teaching. Afterward, SVM and RF algorithms are selected for data normalization optimization, and their optimal hyperparameters are analyzed. Consequently, an experiment is designed to compare the classification accuracy of the algorithms before and after optimization, and the polynomial kernel-based SVM algorithm is proved to present the most remarkable improvement and accuracy after optimization, which is as high as 95.23% or 66.96% improvement. Therefore, the polynomial kernel-based SVM algorithm is chosen for the college English (R-W-T) classroom-oriented human pose recognition (HPR) system. Thus, English teachers can better grasp the students’ psychological state and classroom atmosphere and ameliorate unsatisfactory teacher-student interaction in students’ English (R-W-T). The proposal plays a positive role in cultivating college students’ strong (R-W-T) EPL, which is of great significance in improving the English classroom.


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## Body

## 1. Introduction

At present, English remains the most widely spoken global language. Thus, there is an increasing demand for college students to perfect their reading, writing, and translation (R-W-T) English proficiency level (EPL) through college English (R-W-T) classroom [1]. However, most existing college English (R-W-T) classrooms are characterized by a single teaching mode, a dull classroom atmosphere, and insufficient classroom interaction. Probably, this is due to the traditional one-to-many teaching approach, in which teachers have limited control over individual students’ learning situations [2]. Fortunately, with the development of artificial intelligence (AI) and machine learning (ML), computers are endowed with powerful analytical learning abilities, providing solutions for students’ classroom behavior analysis and classroom teaching optimization.Initially, most researches on classroom behavior analysis collect teacher and student-related data via classroom videos, and then, the teachers conduct follow-up summaries and reflection through manual analysis [3]. With the development of the times, speech recognition (SR) and emotion recognition (ER) are seeing broader applications in teaching scenes. For example, artificial neural networks (ANN) can be used to classify students’ emotions to help teachers devise customized teaching schemes based on students’ emotional feelings. In addition to vision and hearing, the pressure sensor matrix can extract the students’ pose information and analyze the students’ listening state. Moreover, some scholars analyze the students’ in-classroom attention direction by analyzing their head pose features through random forest (RF) and iterative regression algorithm. Consequently, students’ attention information can be obtained [4]. To sum up, the application of existing AI technology in classroom behavior analysis mostly focuses on students’ expression and speech. There is relatively little research on classroom-oriented human pose recognition (HPR). In particular, many intrusive detection devices are prone to external influences, causing much deviation between the collected and real data, so the results lack accuracy [5].Accordingly, this paper attempts to improve the accuracy of students’ HPR by optimizing the polynomial kernel-based support vector machine (SVM) algorithm. Firstly, this paper expounds on the correlation between ML and teaching classrooms to explore the optimization scheme for ML-based classroom teaching. Later, SVM and RF algorithms are selected for data normalization optimization, and the optimal hyperparameters of different algorithms are discussed. Experiments show that the optimized polynomial kernel-based SVM algorithm has the best classification performance and constructs the college English (R-W-T) classroom-oriented HPR system. Lastly, based on the proposed HPR system, teachers can master classroom situations timely, adjust teaching strategies accordingly, ameliorate the college English (R-W-T) classroom environment, and cultivate college students’ (R-W-T) EPL.


## 2. Materials and Methods

### 2.1. Relationship between ML and Teaching Classroom

Generally, ML refers to a computer (or machine)-based simulation of human behavior or logic for learning new things, as well as the approach to empower computers to learn knowledge independently. Meanwhile, ML can self-optimize its recognition ability or learning effect through continuous learning [6]. To that end, there is a need first to build a learning mechanism using the existing knowledge structure, which is then used to classify or predict the unknown information. The classification and prediction results will be optimized so that the learning mechanism can be further developed and strengthened. To date, most ML algorithms are used in either prediction or classification of the currently known information. For example, ML algorithms can first learn (be trained with) the quarterly economic profit (EP) status of an enterprise and then predict the EP of the next quarter, compare the correlation between the quarter and the EP, and further optimize itself [7]. In particular, this paper applies ML to the classification problem; specifically, the students’ most frequent classroom poses are included in the experimental data set. Figure 1 illustrates the common students’ poses in college English (R-W-T) classrooms.Figure 1
Main classroom poses.The common classroom poses are classified in Figure1 as raising hands, chin resting on hands, sleeping, writing, and smartphone (SPHN) playing. Then, the HPR method is used to obtain the key joint coordinates of students. After feature extraction (FE) and normalization, the obtained eigenvalues are defined as classification standards and transmitted to the selected ML algorithm. Then, after continuous learning, the college English (R-W-T) classroom-based HPE-oriented ML model is finally implemented, with which the teacher can master the students’ timely learning situation to create a more interesting atmosphere for English (R-W-T) and enhance the interaction with students.


### 2.2. Classification of ML Algorithms

#### 2.2.1. Decision Tree (DT) Algorithm

It is an inductive classification algorithm based on instance categories, which can build a DT model from relevant data sets and summarize simple and clear classification methods. Meanwhile, DT adopts the high-to-low recursive classification principle. Specifically, the attribute classification measurement is used to find the root node; then, it branches down from the root, and the same principle is used to classify the sub-data set, thereby building the terminal leaf node. Each leaf node is recorded as a category. The relevant path from the root node to the path DT and, finally, to the leaf node is the classification corresponding data or classification rules [8]. Here, according to the different characteristics of male and female students, such as voice and hairstyles, the DT discriminates the gender of the respondents and divides them into groups of boys and girls. The specific process principle is shown in Figure 2.Figure 2
Principle of DT.


#### 2.2.2. ANN-Based Classification Algorithm

ANN system is based on neurons, as depicted in Figure3.Figure 3
Neuronal structure.The neuron is called perceptron, and the algorithm idea of ANN is inspired by biology, with hardly any human involvement [9].


#### 2.2.3. Bayesian-Based Classification Algorithm

Mainly, it is the Bayesian theorem-based posterior possibility deduction according to the object’s prior possibility, and the object is classified into the class with the greatest posterior possibility. Then, following the comparison of naive Bayes (NB) classification, DT algorithm, and NN algorithm, it is found that the NB algorithm performs similarly with related algorithms and is even more advantageous than other algorithms in special fields. The Bayesian-based method possesses some special characteristics: firstly, when the assumption and the relevant training samples are out of sync, the Bayesian-based algorithm can incrementally increase and reduce the estimation probability of relevant assumptions, whereas other similar algorithms will completely delete the assumptions. Secondly, the Bayesian-based algorithm employs a priori knowledge, possibility, and input data collaboratively to deduce the final probability. Thirdly, the Bayesian-based model can be used for uncertainty prediction; for example, the Bayesian-based model predicts that a student has an 88% chance of passing an exam, and it has a 77% chance of rain tomorrow. Yet, the Bayesian-based algorithm also has some shortcomings. For instance, it is imperative for the Bayesian-based algorithm to provide relevant knowledge of possibility distribution. To that end, relevant background information is often chosen, together with the probability of previously known category distribution and statistical, empirical data assumptions, to analyze the relevant possibility. Additionally, there is a high cost for determining the optimal Bayesian assumption in ordinary applications, while the cost can become very low [10]. If the correlation probability is recorded as M and H, the principle of the Bayesian-based algorithm can be shown in Figure 4.Figure 4
Bayesian theory diagram.


#### 2.2.4. SVM Is a Widely Used Binary Classification (BC) Model

Essentially, the linear classifier maximizes the interval of data in the feature space. It can also use the kernel function (KF) to map the input to the high-dimensional space for nonlinear classification. When the information is linearly separable, data setD is recorded as follows:
(1)D=X1,y1,X2,y2,⋯,XD,yD,where Xi and Yi represent the category labels, and the relevant linearly separable data sets are presented in Figure 5.Figure 5
Linearly separable data set.Figure5 implies that many lines and hyperplanes can distinguish the two data categories; noticeably, SVM strives to find the best line and hyperplane to minimize the classification error, in which the interval maximization method is most commonly used. A linearly separable hyperplane can be expressed as follows:
(2)ω⋅X+b=0,where ω and b are the weight vector and the offset, respectively. The boundary H1·H2 of the interval defined by this hyperplane reads as follows:
(3)H1:ω0+ω1x1+ω2x2≥1,yi=+1,(4)H2:ω0+ω1x1+ω2x2≤−1,yi=−1.The training tuples that fall on hyperplanesH1 and H2 are called support vectors, which is essentially a convex quadratic optimization problem, and its objective function (OF) can be expressed as follows:
(5)f=min12ω2,yiωTxi+b,i=1,⋯,n.


#### 2.2.5. RF Algorithm

In practice, the bagging strategy has given birth to RF. The RF model often contains multiple DT, and the prediction results are obtained by the related DT voting mechanism. Its algorithm process is outlined in Figure6.Figure 6
Algorithm flow of RF.Then, the completely trained RF model uses the relative majority voting system to classify the samples and output the results. That is, the item marked with the highest vote is ultimately output. Particularly, only one random item will be output from parallel items (with the same highest votes) [11].Meanwhile, the classification results of the RF algorithm model are closely related to the performance of related DT. In other words, given a DT with excellent classification performance, the comprehensive classification performance of RF is even better; on the other hand, the performance of RF is related to the correlation of two random DT in the model: the higher the correlation degree is, the greater the error possibility is [12]. The correlation of DT is related to the number of feature selection m: a large m indicates a stronger correlation. Moreover, the bootstrap method is often used in RF, and the observations that are not sampled for DT construction are denoted by out-of-bag (OOB) samples, based on which the OOB error rate (ER) is calculated [13]. Further, the OOB ER calculation is divided into three steps: firstly, the classification results of the RF algorithm are deduced on each OOB sample; then, the relevant items with the highest DT voting in the classification results are obtained; lastly, the final sample classification is determined; in other words, the out of OOB ER refers to the ratio of the number of misclassified samples to the total samples; the OOB ER is an unbiased estimation of RF generalization error [14].Based on the above classification methods, this paper selects two ML algorithms: RF and SVM to learn and train the key features of students’ joints, compare and test the final results, and determine the classification algorithm with the best effect as the final system algorithm [15].


### 2.3. Motion FE and Normalization Based on Joint Key Points

At present, there is no standard public data set for classroom pose recognition. Therefore, the basis of the subsequent research is the production of classroom students’ learning pose data. Because this paper studies the college English (R-W-T) classroom, it is more appropriate to collect data from the college classroom. The collected videos are saved by manual screening and video software. Finally, the personal data sets of the five poses mentioned above, including raising hands, chin resting on hand, sleeping, writing, and SPHN playing, can be obtained, with a total of 100 segments. The video time is from 1 second to 3 seconds. Data normalization and FE are needed for image data. Additionally, for the maximum likelihood algorithm of FE, reducing the scale of data input can reduce the data classification complexity of the algorithm.In the ML algorithm, the input eigenvalues determined by different classifiers are deemed highly representative of such kind of data. Accordingly, a classification model with better classification accuracy and performance can be obtained after continuous training and learning [16]. In particular, in the data set of hand raising, chin resting on hand, sleeping, writing, and SPHN playing, different poses present distinct joint key points. Here, the OpenPose model can be used to classify and recognize the relationship features of positional information for human joint key points. Figure 7 manifests a shot of collecting classroom information.Figure 7
Classroom monitoring equipment.Student pose collection involves multiple factors, such as individual size and distance between students and the lens. Hence, if only the captured key position information is input into the classifier as the eigenvalue of different poses, the final classification results might be greatly deviated [17]. Afterward, this section performs data normalization on the extracted position information of key joint points of different poses to even the individual differences and acquisition distances, thereby improving the generalization ability of the classification model. Usually, students’ upper-part bodies are enough to reflect common classroom poses, such as head, neck, shoulder, elbow, and wrist. Therefore, the classification training is primarily focused on the key points of their upper-body joints. Meanwhile, it is believed that too much attention on the overall poses might negatively impact the classification effect; this is because the classroom pose is special; in the classroom environment, students’ lower-part bodies are mostly shielded, and it generally does not strongly correlate with other poses [18]. Finally, Figure 8 reveals the proposed 18-joint point-based manikin.Figure 8
18-joint point-based manikin.In the 18-joint point-based manikin, 0 to 17 correspond to the person’s pen tip, neck, right shoulder, right wrist, left shoulder, left elbow, left wrist, right hip, right knee, left ankle, left hip, left knee, right ankle, eye, left eye, right ear, and left ear, respectively. According to the previous classroom-oriented HPR, it is necessary to exclude the joint points of the lower-part body (no. 8 to no. 13) and retain the joint points of the upper-part body (no. 0 to no. 7) in the 18-joint point manikin [19]. Meanwhile, the 18-joint point manikin can be used to extract the five pose models in the college English (R-W-T) classroom. The key points of the five poses will differ greatly. In particular, the key points of SPHN playing and writing might be very close due to their similar joint movement: the distances of two wrists while playing mobile phone are a fixed distance, whereas that of writing is different. Therefore, the 18-joint point manikin can still be used to classify the key joint points of the pose through the coordinate method. OpenPose is used to extract the coordinates of each pose key point in the data set, and after format conversion, it lays the foundation for later data processing.Due to individual differences, the same joint points might present distinct coordinates according to the distance between the human body and video equipment. This will eventually lead to erroneous classification results. Accordingly, the dimensionless normalization method is employed to optimize the classification effect [20].The so-called dimensionless is to normalize the data. Because indexes are not comparable under dimensional differences, it is necessary to normalize them to avoid the negative impact of dimensionality. Specifically, this section chooses the min–max normalization method, as calculated by Equations (6) and (7):
(6)x=320∗x−xminxmax−xmin,(7)y=240∗y−yminymax−ymin,where xmaxymax represents the maximum xy value extracted from all joint coordinates of each individual, and xminymin is the minimum xy value. Sometimes, the coordinate of the key joint points cannot be extracted when local key points are unidentifiable because of specific data, and the results are output as 0; such data need to be deleted from the extraction process since when the coordinates of this key point are unidentifiable, the minimum of the normalization equation gets 0, and the data normalization will be deviated, which will have a great negative impact on subsequent training and learning [21]. In the follow-up, the normalized data will be labeled and stored in the corresponding files to serve as the data training set for the ML algorithm. The order of labels is drawn in Figure 9.Figure 9
Corresponding label of pose data.


### 2.4. Case Analysis

The experimental link uses classroom monitoring equipment to collect students’ data, uses ANN and SVM model to process and analyze students’ expression images, then uses RF model to learn and train the constructed data set, and compares the image data results for different models. Then, to verify the effectiveness of normalization of joint key points, this section collects five conventional pose videos (with 1 to 10 seconds during) in the classroom in addition to the constructed data set. Then, five new videos are obtained after zooming in and out operations for the normalization test set of abnormal human joint data due to individual differences and collection distance. The specific training environment of the model includes Python 3.7; the Sklearn module is used to train and optimize the encapsulated SVM model and RF model parameters. Of these, the key hyperparameter in SVM is the penalty factor: the larger the penalty factor is, the smaller the model tolerance to the classification error will be, and the model is more prone to overfitting. By contrast, the smaller the penalty factor is, the less likely the model overfits. On the other hand, the key hyperparameters in RF are the number of DT and the maximum depth of DT. Overall, SVM and RF contain three hyperparameters to be optimized. Finally, the penalty factor is determined as 0.001 for optimizing polynomial kernel-based SVM and Sigmoid kernel function-based SVM by network search, the maximum depth as 20 for RF model, and the number of DT as 100.


## 2.1. Relationship between ML and Teaching Classroom

Generally, ML refers to a computer (or machine)-based simulation of human behavior or logic for learning new things, as well as the approach to empower computers to learn knowledge independently. Meanwhile, ML can self-optimize its recognition ability or learning effect through continuous learning [6]. To that end, there is a need first to build a learning mechanism using the existing knowledge structure, which is then used to classify or predict the unknown information. The classification and prediction results will be optimized so that the learning mechanism can be further developed and strengthened. To date, most ML algorithms are used in either prediction or classification of the currently known information. For example, ML algorithms can first learn (be trained with) the quarterly economic profit (EP) status of an enterprise and then predict the EP of the next quarter, compare the correlation between the quarter and the EP, and further optimize itself [7]. In particular, this paper applies ML to the classification problem; specifically, the students’ most frequent classroom poses are included in the experimental data set. Figure 1 illustrates the common students’ poses in college English (R-W-T) classrooms.Figure 1
Main classroom poses.The common classroom poses are classified in Figure1 as raising hands, chin resting on hands, sleeping, writing, and smartphone (SPHN) playing. Then, the HPR method is used to obtain the key joint coordinates of students. After feature extraction (FE) and normalization, the obtained eigenvalues are defined as classification standards and transmitted to the selected ML algorithm. Then, after continuous learning, the college English (R-W-T) classroom-based HPE-oriented ML model is finally implemented, with which the teacher can master the students’ timely learning situation to create a more interesting atmosphere for English (R-W-T) and enhance the interaction with students.


## 2.2. Classification of ML Algorithms

### 2.2.1. Decision Tree (DT) Algorithm

It is an inductive classification algorithm based on instance categories, which can build a DT model from relevant data sets and summarize simple and clear classification methods. Meanwhile, DT adopts the high-to-low recursive classification principle. Specifically, the attribute classification measurement is used to find the root node; then, it branches down from the root, and the same principle is used to classify the sub-data set, thereby building the terminal leaf node. Each leaf node is recorded as a category. The relevant path from the root node to the path DT and, finally, to the leaf node is the classification corresponding data or classification rules [8]. Here, according to the different characteristics of male and female students, such as voice and hairstyles, the DT discriminates the gender of the respondents and divides them into groups of boys and girls. The specific process principle is shown in Figure 2.Figure 2
Principle of DT.


### 2.2.2. ANN-Based Classification Algorithm

ANN system is based on neurons, as depicted in Figure3.Figure 3
Neuronal structure.The neuron is called perceptron, and the algorithm idea of ANN is inspired by biology, with hardly any human involvement [9].


### 2.2.3. Bayesian-Based Classification Algorithm

Mainly, it is the Bayesian theorem-based posterior possibility deduction according to the object’s prior possibility, and the object is classified into the class with the greatest posterior possibility. Then, following the comparison of naive Bayes (NB) classification, DT algorithm, and NN algorithm, it is found that the NB algorithm performs similarly with related algorithms and is even more advantageous than other algorithms in special fields. The Bayesian-based method possesses some special characteristics: firstly, when the assumption and the relevant training samples are out of sync, the Bayesian-based algorithm can incrementally increase and reduce the estimation probability of relevant assumptions, whereas other similar algorithms will completely delete the assumptions. Secondly, the Bayesian-based algorithm employs a priori knowledge, possibility, and input data collaboratively to deduce the final probability. Thirdly, the Bayesian-based model can be used for uncertainty prediction; for example, the Bayesian-based model predicts that a student has an 88% chance of passing an exam, and it has a 77% chance of rain tomorrow. Yet, the Bayesian-based algorithm also has some shortcomings. For instance, it is imperative for the Bayesian-based algorithm to provide relevant knowledge of possibility distribution. To that end, relevant background information is often chosen, together with the probability of previously known category distribution and statistical, empirical data assumptions, to analyze the relevant possibility. Additionally, there is a high cost for determining the optimal Bayesian assumption in ordinary applications, while the cost can become very low [10]. If the correlation probability is recorded as M and H, the principle of the Bayesian-based algorithm can be shown in Figure 4.Figure 4
Bayesian theory diagram.


### 2.2.4. SVM Is a Widely Used Binary Classification (BC) Model

Essentially, the linear classifier maximizes the interval of data in the feature space. It can also use the kernel function (KF) to map the input to the high-dimensional space for nonlinear classification. When the information is linearly separable, data setD is recorded as follows:
(1)D=X1,y1,X2,y2,⋯,XD,yD,where Xi and Yi represent the category labels, and the relevant linearly separable data sets are presented in Figure 5.Figure 5
Linearly separable data set.Figure5 implies that many lines and hyperplanes can distinguish the two data categories; noticeably, SVM strives to find the best line and hyperplane to minimize the classification error, in which the interval maximization method is most commonly used. A linearly separable hyperplane can be expressed as follows:
(2)ω⋅X+b=0,where ω and b are the weight vector and the offset, respectively. The boundary H1·H2 of the interval defined by this hyperplane reads as follows:
(3)H1:ω0+ω1x1+ω2x2≥1,yi=+1,(4)H2:ω0+ω1x1+ω2x2≤−1,yi=−1.The training tuples that fall on hyperplanesH1 and H2 are called support vectors, which is essentially a convex quadratic optimization problem, and its objective function (OF) can be expressed as follows:
(5)f=min12ω2,yiωTxi+b,i=1,⋯,n.


### 2.2.5. RF Algorithm

In practice, the bagging strategy has given birth to RF. The RF model often contains multiple DT, and the prediction results are obtained by the related DT voting mechanism. Its algorithm process is outlined in Figure6.Figure 6
Algorithm flow of RF.Then, the completely trained RF model uses the relative majority voting system to classify the samples and output the results. That is, the item marked with the highest vote is ultimately output. Particularly, only one random item will be output from parallel items (with the same highest votes) [11].Meanwhile, the classification results of the RF algorithm model are closely related to the performance of related DT. In other words, given a DT with excellent classification performance, the comprehensive classification performance of RF is even better; on the other hand, the performance of RF is related to the correlation of two random DT in the model: the higher the correlation degree is, the greater the error possibility is [12]. The correlation of DT is related to the number of feature selection m: a large m indicates a stronger correlation. Moreover, the bootstrap method is often used in RF, and the observations that are not sampled for DT construction are denoted by out-of-bag (OOB) samples, based on which the OOB error rate (ER) is calculated [13]. Further, the OOB ER calculation is divided into three steps: firstly, the classification results of the RF algorithm are deduced on each OOB sample; then, the relevant items with the highest DT voting in the classification results are obtained; lastly, the final sample classification is determined; in other words, the out of OOB ER refers to the ratio of the number of misclassified samples to the total samples; the OOB ER is an unbiased estimation of RF generalization error [14].Based on the above classification methods, this paper selects two ML algorithms: RF and SVM to learn and train the key features of students’ joints, compare and test the final results, and determine the classification algorithm with the best effect as the final system algorithm [15].


## 2.2.1. Decision Tree (DT) Algorithm

It is an inductive classification algorithm based on instance categories, which can build a DT model from relevant data sets and summarize simple and clear classification methods. Meanwhile, DT adopts the high-to-low recursive classification principle. Specifically, the attribute classification measurement is used to find the root node; then, it branches down from the root, and the same principle is used to classify the sub-data set, thereby building the terminal leaf node. Each leaf node is recorded as a category. The relevant path from the root node to the path DT and, finally, to the leaf node is the classification corresponding data or classification rules [8]. Here, according to the different characteristics of male and female students, such as voice and hairstyles, the DT discriminates the gender of the respondents and divides them into groups of boys and girls. The specific process principle is shown in Figure 2.Figure 2
Principle of DT.


## 2.2.2. ANN-Based Classification Algorithm

ANN system is based on neurons, as depicted in Figure3.Figure 3
Neuronal structure.The neuron is called perceptron, and the algorithm idea of ANN is inspired by biology, with hardly any human involvement [9].


## 2.2.3. Bayesian-Based Classification Algorithm

Mainly, it is the Bayesian theorem-based posterior possibility deduction according to the object’s prior possibility, and the object is classified into the class with the greatest posterior possibility. Then, following the comparison of naive Bayes (NB) classification, DT algorithm, and NN algorithm, it is found that the NB algorithm performs similarly with related algorithms and is even more advantageous than other algorithms in special fields. The Bayesian-based method possesses some special characteristics: firstly, when the assumption and the relevant training samples are out of sync, the Bayesian-based algorithm can incrementally increase and reduce the estimation probability of relevant assumptions, whereas other similar algorithms will completely delete the assumptions. Secondly, the Bayesian-based algorithm employs a priori knowledge, possibility, and input data collaboratively to deduce the final probability. Thirdly, the Bayesian-based model can be used for uncertainty prediction; for example, the Bayesian-based model predicts that a student has an 88% chance of passing an exam, and it has a 77% chance of rain tomorrow. Yet, the Bayesian-based algorithm also has some shortcomings. For instance, it is imperative for the Bayesian-based algorithm to provide relevant knowledge of possibility distribution. To that end, relevant background information is often chosen, together with the probability of previously known category distribution and statistical, empirical data assumptions, to analyze the relevant possibility. Additionally, there is a high cost for determining the optimal Bayesian assumption in ordinary applications, while the cost can become very low [10]. If the correlation probability is recorded as M and H, the principle of the Bayesian-based algorithm can be shown in Figure 4.Figure 4
Bayesian theory diagram.


## 2.2.4. SVM Is a Widely Used Binary Classification (BC) Model

Essentially, the linear classifier maximizes the interval of data in the feature space. It can also use the kernel function (KF) to map the input to the high-dimensional space for nonlinear classification. When the information is linearly separable, data setD is recorded as follows:
(1)D=X1,y1,X2,y2,⋯,XD,yD,where Xi and Yi represent the category labels, and the relevant linearly separable data sets are presented in Figure 5.Figure 5
Linearly separable data set.Figure5 implies that many lines and hyperplanes can distinguish the two data categories; noticeably, SVM strives to find the best line and hyperplane to minimize the classification error, in which the interval maximization method is most commonly used. A linearly separable hyperplane can be expressed as follows:
(2)ω⋅X+b=0,where ω and b are the weight vector and the offset, respectively. The boundary H1·H2 of the interval defined by this hyperplane reads as follows:
(3)H1:ω0+ω1x1+ω2x2≥1,yi=+1,(4)H2:ω0+ω1x1+ω2x2≤−1,yi=−1.The training tuples that fall on hyperplanesH1 and H2 are called support vectors, which is essentially a convex quadratic optimization problem, and its objective function (OF) can be expressed as follows:
(5)f=min12ω2,yiωTxi+b,i=1,⋯,n.


## 2.2.5. RF Algorithm

In practice, the bagging strategy has given birth to RF. The RF model often contains multiple DT, and the prediction results are obtained by the related DT voting mechanism. Its algorithm process is outlined in Figure6.Figure 6
Algorithm flow of RF.Then, the completely trained RF model uses the relative majority voting system to classify the samples and output the results. That is, the item marked with the highest vote is ultimately output. Particularly, only one random item will be output from parallel items (with the same highest votes) [11].Meanwhile, the classification results of the RF algorithm model are closely related to the performance of related DT. In other words, given a DT with excellent classification performance, the comprehensive classification performance of RF is even better; on the other hand, the performance of RF is related to the correlation of two random DT in the model: the higher the correlation degree is, the greater the error possibility is [12]. The correlation of DT is related to the number of feature selection m: a large m indicates a stronger correlation. Moreover, the bootstrap method is often used in RF, and the observations that are not sampled for DT construction are denoted by out-of-bag (OOB) samples, based on which the OOB error rate (ER) is calculated [13]. Further, the OOB ER calculation is divided into three steps: firstly, the classification results of the RF algorithm are deduced on each OOB sample; then, the relevant items with the highest DT voting in the classification results are obtained; lastly, the final sample classification is determined; in other words, the out of OOB ER refers to the ratio of the number of misclassified samples to the total samples; the OOB ER is an unbiased estimation of RF generalization error [14].Based on the above classification methods, this paper selects two ML algorithms: RF and SVM to learn and train the key features of students’ joints, compare and test the final results, and determine the classification algorithm with the best effect as the final system algorithm [15].


## 2.3. Motion FE and Normalization Based on Joint Key Points

At present, there is no standard public data set for classroom pose recognition. Therefore, the basis of the subsequent research is the production of classroom students’ learning pose data. Because this paper studies the college English (R-W-T) classroom, it is more appropriate to collect data from the college classroom. The collected videos are saved by manual screening and video software. Finally, the personal data sets of the five poses mentioned above, including raising hands, chin resting on hand, sleeping, writing, and SPHN playing, can be obtained, with a total of 100 segments. The video time is from 1 second to 3 seconds. Data normalization and FE are needed for image data. Additionally, for the maximum likelihood algorithm of FE, reducing the scale of data input can reduce the data classification complexity of the algorithm.In the ML algorithm, the input eigenvalues determined by different classifiers are deemed highly representative of such kind of data. Accordingly, a classification model with better classification accuracy and performance can be obtained after continuous training and learning [16]. In particular, in the data set of hand raising, chin resting on hand, sleeping, writing, and SPHN playing, different poses present distinct joint key points. Here, the OpenPose model can be used to classify and recognize the relationship features of positional information for human joint key points. Figure 7 manifests a shot of collecting classroom information.Figure 7
Classroom monitoring equipment.Student pose collection involves multiple factors, such as individual size and distance between students and the lens. Hence, if only the captured key position information is input into the classifier as the eigenvalue of different poses, the final classification results might be greatly deviated [17]. Afterward, this section performs data normalization on the extracted position information of key joint points of different poses to even the individual differences and acquisition distances, thereby improving the generalization ability of the classification model. Usually, students’ upper-part bodies are enough to reflect common classroom poses, such as head, neck, shoulder, elbow, and wrist. Therefore, the classification training is primarily focused on the key points of their upper-body joints. Meanwhile, it is believed that too much attention on the overall poses might negatively impact the classification effect; this is because the classroom pose is special; in the classroom environment, students’ lower-part bodies are mostly shielded, and it generally does not strongly correlate with other poses [18]. Finally, Figure 8 reveals the proposed 18-joint point-based manikin.Figure 8
18-joint point-based manikin.In the 18-joint point-based manikin, 0 to 17 correspond to the person’s pen tip, neck, right shoulder, right wrist, left shoulder, left elbow, left wrist, right hip, right knee, left ankle, left hip, left knee, right ankle, eye, left eye, right ear, and left ear, respectively. According to the previous classroom-oriented HPR, it is necessary to exclude the joint points of the lower-part body (no. 8 to no. 13) and retain the joint points of the upper-part body (no. 0 to no. 7) in the 18-joint point manikin [19]. Meanwhile, the 18-joint point manikin can be used to extract the five pose models in the college English (R-W-T) classroom. The key points of the five poses will differ greatly. In particular, the key points of SPHN playing and writing might be very close due to their similar joint movement: the distances of two wrists while playing mobile phone are a fixed distance, whereas that of writing is different. Therefore, the 18-joint point manikin can still be used to classify the key joint points of the pose through the coordinate method. OpenPose is used to extract the coordinates of each pose key point in the data set, and after format conversion, it lays the foundation for later data processing.Due to individual differences, the same joint points might present distinct coordinates according to the distance between the human body and video equipment. This will eventually lead to erroneous classification results. Accordingly, the dimensionless normalization method is employed to optimize the classification effect [20].The so-called dimensionless is to normalize the data. Because indexes are not comparable under dimensional differences, it is necessary to normalize them to avoid the negative impact of dimensionality. Specifically, this section chooses the min–max normalization method, as calculated by Equations (6) and (7):
(6)x=320∗x−xminxmax−xmin,(7)y=240∗y−yminymax−ymin,where xmaxymax represents the maximum xy value extracted from all joint coordinates of each individual, and xminymin is the minimum xy value. Sometimes, the coordinate of the key joint points cannot be extracted when local key points are unidentifiable because of specific data, and the results are output as 0; such data need to be deleted from the extraction process since when the coordinates of this key point are unidentifiable, the minimum of the normalization equation gets 0, and the data normalization will be deviated, which will have a great negative impact on subsequent training and learning [21]. In the follow-up, the normalized data will be labeled and stored in the corresponding files to serve as the data training set for the ML algorithm. The order of labels is drawn in Figure 9.Figure 9
Corresponding label of pose data.


## 2.4. Case Analysis

The experimental link uses classroom monitoring equipment to collect students’ data, uses ANN and SVM model to process and analyze students’ expression images, then uses RF model to learn and train the constructed data set, and compares the image data results for different models. Then, to verify the effectiveness of normalization of joint key points, this section collects five conventional pose videos (with 1 to 10 seconds during) in the classroom in addition to the constructed data set. Then, five new videos are obtained after zooming in and out operations for the normalization test set of abnormal human joint data due to individual differences and collection distance. The specific training environment of the model includes Python 3.7; the Sklearn module is used to train and optimize the encapsulated SVM model and RF model parameters. Of these, the key hyperparameter in SVM is the penalty factor: the larger the penalty factor is, the smaller the model tolerance to the classification error will be, and the model is more prone to overfitting. By contrast, the smaller the penalty factor is, the less likely the model overfits. On the other hand, the key hyperparameters in RF are the number of DT and the maximum depth of DT. Overall, SVM and RF contain three hyperparameters to be optimized. Finally, the penalty factor is determined as 0.001 for optimizing polynomial kernel-based SVM and Sigmoid kernel function-based SVM by network search, the maximum depth as 20 for RF model, and the number of DT as 100.


## 3. Results

### 3.1. Accuracy Test of Different Algorithms before and after Normalization

Table1 exhibits the accuracy test of different algorithms in the college English (R-W-T) classroom-oriented HPR test set.Table 1
Classification accuracy of different methods before and after optimization.
MethodAccuracy before optimization (%)Optimized accuracy (%)Polynomial SVM28.2795.23Sigmoid SVM19.7519.76Decision tree26.4434.25Random forests33.5243.69Table1 suggests that the performance of the optimized classification methods has been improved. Concretely, the accuracy of polynomial kernel-based SVM has reached 95.23%, whereas that of other algorithms is less than 50%. Hence, the polynomial kernel-based SVM outmatches other comparative algorithms and can be recommended as the proposed classification algorithm.


### 3.2. Performance Comparison of Different Algorithms before and after Optimization

Figure10 summarizes the accuracy of different algorithms to more intuitively compare their performance before and after optimization.Figure 10
Accuracy variation range.Figure10 corroborates that the polynomial kernel-based SVM has a lower classification accuracy than RF before optimization. After optimization, the classification accuracy of polynomial kernel-based SVM, RF, DT, and Sigmoid kernel-based SVM is improved by 66.96%, 43.69%, 34.25%, and 19.76%, respectively; meanwhile, the classification accuracy of SVM is higher than that of RF after normalization optimization. This is because the RF is composed of N DTs; during learning and training, all the data features will be randomly selected, in which the optimal features will be taken as the splitting features; in particular, in the classroom students’ joint key point model, each joint key point is crucial feature information; the kernel function-based SVM maps each feature to high-dimensional space for the overall classification and is suitable for college English (R-W-T) classroom-oriented HPR, whereas RF is not suitable for the constructed classroom-oriented HPR data set. According to the experimental results, the normalized polynomial kernel-based SVM shows the best performance in college English (R-W-T) classroom-oriented HPR. Finally, with the help of the proposed HPR system, English teachers can better grasp students’ psychological state and classroom atmosphere in college English (R-W-T) classrooms to ameliorate unsatisfactory interaction and cooperation between teachers and students.


## 3.1. Accuracy Test of Different Algorithms before and after Normalization

Table1 exhibits the accuracy test of different algorithms in the college English (R-W-T) classroom-oriented HPR test set.Table 1
Classification accuracy of different methods before and after optimization.
MethodAccuracy before optimization (%)Optimized accuracy (%)Polynomial SVM28.2795.23Sigmoid SVM19.7519.76Decision tree26.4434.25Random forests33.5243.69Table1 suggests that the performance of the optimized classification methods has been improved. Concretely, the accuracy of polynomial kernel-based SVM has reached 95.23%, whereas that of other algorithms is less than 50%. Hence, the polynomial kernel-based SVM outmatches other comparative algorithms and can be recommended as the proposed classification algorithm.


## 3.2. Performance Comparison of Different Algorithms before and after Optimization

Figure10 summarizes the accuracy of different algorithms to more intuitively compare their performance before and after optimization.Figure 10
Accuracy variation range.Figure10 corroborates that the polynomial kernel-based SVM has a lower classification accuracy than RF before optimization. After optimization, the classification accuracy of polynomial kernel-based SVM, RF, DT, and Sigmoid kernel-based SVM is improved by 66.96%, 43.69%, 34.25%, and 19.76%, respectively; meanwhile, the classification accuracy of SVM is higher than that of RF after normalization optimization. This is because the RF is composed of N DTs; during learning and training, all the data features will be randomly selected, in which the optimal features will be taken as the splitting features; in particular, in the classroom students’ joint key point model, each joint key point is crucial feature information; the kernel function-based SVM maps each feature to high-dimensional space for the overall classification and is suitable for college English (R-W-T) classroom-oriented HPR, whereas RF is not suitable for the constructed classroom-oriented HPR data set. According to the experimental results, the normalized polynomial kernel-based SVM shows the best performance in college English (R-W-T) classroom-oriented HPR. Finally, with the help of the proposed HPR system, English teachers can better grasp students’ psychological state and classroom atmosphere in college English (R-W-T) classrooms to ameliorate unsatisfactory interaction and cooperation between teachers and students.


## 4. Conclusion

With the ever-dominating role of English as the international language, teaching English as a Second Language (ESL) in college has put forward higher requirements for (R-W-T) EPL. However, the traditional college English (R-W-T) classroom is characterized by single teaching modes, a dull classroom atmosphere, and insufficient teacher-student interaction. To this end, this paper designs an optimized polynomial kernel-based SVM classification algorithm through the ML classification method. Firstly, this paper expounds on the correlation between ML and teaching classrooms to analyze the optimization direction for the application of ML in classroom teaching. Later, SVM and RF algorithms are chosen explicitly for data normalization optimization, and the optimal hyperparameters of different algorithms are discussed. Afterward, an experiment is designed to compare the classification accuracy of the algorithms before and after optimization; as a result, the polynomial kernel-based SVM algorithm with the largest change and the highest accuracy after optimization is selected to build an HPR system. With the help of the proposed college English (R-W-T) classroom-oriented HPR system, English teachers can better grasp the students’ mental state and classroom atmosphere to ameliorate unsatisfactory teacher-student interaction in English (R-W-T). The proposed College CSL classroom-oriented HPR system plays a positive role in cultivating college students with strong (R-W-T) EPL and is of great significance to improve the form of the English classroom. However, some shortcomings of the research have not been avoided mainly because the HPR model has a certain complexity and needs a lot of data training. In future research, the algorithm model needs to be simplified to filter the algorithm’s input data to achieve a better algorithm training effect.



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*Source: 4506025-2022-03-10.xml*