# Evaluation System of Foreign Language Teaching Quality Based on Spatiotemporal Feature Fusion

Authors: Yan Hao; Yi Zhu
Journal: Security and Communication Networks
 (2022)

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## Abstract

How to evaluate the teaching quality of foreign language teachers objectively and quantitatively is one of the important directions of teaching evaluation institutions and teaching and research personnel. To solve this problem, this article proposes a foreign language teaching quality evaluation system based on the integration of spatiotemporal features. Given the behavior characteristics of multiperson interaction in class, a framework-based spatiotemporal modeling method is presented in this article. Spatiotemporal modeling features are input into generalized graph convolution for feature learning. The interaction information between skeletons is designed to capture the extra interaction information to increase the accuracy of action recognition. The experimental results show that the proposed method has higher accuracy and can be applied to the evaluation of foreign language teaching quality.


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## Body

## 1. Introduction

The quality of foreign language classroom teaching is mainly determined by two key links, namely, the teacher’s teaching ability and the degree of students’ receiving knowledge in class. The following problems have become the focus of teaching evaluation institutions and teaching and research personnel: how to evaluate the quality of teaching objectively and quantitatively, how to accurately count the students’ understanding and mastery of each knowledge point, and how to intuitively reflect the advantages and disadvantages of different teachers’ teaching effects on the same course [1].In recent years, with the development of artificial intelligence technologies such as deep neural network and ultra-large-scale data feature analysis, the recognition accuracy of computer image recognition, speech recognition, and emotion recognition has been greatly improved. Human-machine language dialogue, AI customer service, AI simultaneous interpretation, and other technologies have been gradually commercialized. In the field of teaching, various virtual experimental teaching and large-scale online classroom supported by AI technology have been applied to practical teaching [2]. These technologies have greatly expanded the way teaching content is presented. At the same time, the application of 3d visual image, virtual real scene, simulation experiment, and other technical means makes the traditional design and experiment process more vivid [3]. These technologies can help schools timely grasp the degree of students’ response and recognition to classroom teaching content. In this context, how to use AI technology to help teachers and teaching departments assess the quality of classroom teaching more accurately, objectively, and efficiently has important research value.Video-based interactive behavior recognition has a high practical value and broad application prospects. The purpose of human motion recognition is to analyze and understand the actions and interactions between people in video. It can be applied in intelligent monitoring, human-computer interaction, video sequence understanding, and medical and health and other fields, playing an increasingly important role in daily life [4].In behavior recognition, the application of human skeleton data has obvious advantages over RGB data and depth data. It can be unaffected by background, lighting, and appearance. In addition, skeleton features compact, strong structure, and rich semantics. It has strong ability to describe human movements and motions, and more and more behavioral recognition studies are carried out based on skeleton. At present, there are three skeleton interactive recognition methods based on deep learning: long- and short-term memory network, convolutional neural network, and graph convolutional network [5]. For the time being, relatively mature studies are focused on the recognition of single skeleton actions, and there is a lack of discussion on interactive actions. However, in daily life, common behaviors are basically some interactive ones, such as shaking hands, hugging, fighting, and so on. Interactive action is more complex than solo action. In the process of completing interactive movements, there are more types of body movements, and the changes between body movements are also more diversified. Therefore, how to effectively extract the characteristics of interactive actions, and conduct modeling and analysis of interactive behavior is a very challenging problem.The previous work was to reorganize the skeleton data into a grid structure, which was implemented by RNN (recurrent neural network) and CNN (convolutional neural networks) [6]. Although they have made great improvements in motion recognition, there are still some problems. Because human skeletons are graphical structures, rather than traditional fixed grids, they do not fully benefit from the superior representation capabilities of deep learning. The human skeleton is a naturally constructed figure in a non-Euclidean space. Although the CNN method has strong feature extraction capability, it requires a convolution kernel of fixed size for ergodic processing. Therefore, it cannot extract key features of graph data effectively, and its computational complexity is large. It cannot meet the accuracy requirement in multitask processing, which makes the traditional convolutional neural network not applicable. Although the traditional RNN can also process the skeleton, the accuracy of skeleton data transformation and recognition is not high. Therefore, in this article, GCN is used to process the transformed skeleton data to capture the motion space features [7]. A variant RNN structure is used to capture the temporal dependence information. GCN can directly model the raw skeleton data, extend the graph neural network to the spatiotemporal graph model, and automatically learn spatial and temporal information from the skeleton. The introduction of GCN into skeleton-based motion recognition has yielded many encouraging results. However, most GCN methods are based on predefined graphs with fixed topological constraints, ignoring the implicit joint associations. Meanwhile, GCN could not capture the time information of the whole action sequence completely and could not obtain the action sequence dependence information [8].To solve this problem, various adaptive connections are designed in this article. It emphasizes the relationship between individuals, interaction objects, and time frames. Meanwhile, feature extraction of time series-dependent information is enhanced. During the recognition of interactive actions, additional information from the interaction itself can be extracted by modeling the interaction relationship between each part of the participant’s body. This information is used in global descriptors to identify human interactions to improve the accuracy of interaction action recognition. This article proposes a framework-based spatiotemporal modeling method, which not only designs the connections of a single object and multiple objects in a single frame, but also combines the different connections of single frame and multiple frames. An effective representation of the interactive skeleton diagram is achieved by connecting the relevant joints in the previous frame and the next frame.The innovations and contributions of this article are listed as follows:(1)
In this article, slice RNN is innovatively applied to the field of video action recognition to enhance the extraction of video sequence-dependent information.(2)
Meanwhile, the spatiotemporal modeling method combined with slice RNN can effectively remedy the disadvantages of slice RNN.(3)
Finally, the algorithm is applied to the foreign language teaching quality assessment system. The experimental results show that the proposed method has higher accuracy and can be applied to the evaluation of foreign language teaching quality.The structure of this article is listed as follows. Related work is described in the next section. The proposed system is expressed in Section 3. Section 4 focuses on the experiment and analysis. Section 5 is the conclusion.


## 2. Related Work

### 2.1. Image-Based Interactive Recognition

Much of the early recognition work was based on manually constructed features, for example, using directional gradient histogram and optical flow directional information histogram [9] to extract appearance features of static information, or using optical flow to extract motion features of dynamic information. Newer approaches rely on deep learning. Literature [10] uses the deep learning network for interactive behavior recognition, extracting optical flow characteristic information through CNN and then feeding it into a classifier to realize action recognition.Although the motion recognition method based on RGB video or optical flow has high performance, there are still some problems. For example, it is easily affected by background, illumination, and appearance changes, and it requires high computational cost to extract optical flow information. Some work has been done to extract bone data to avoid learning interaction patterns directly from videos. In some research studies on single-person motion recognition, most scholars use human skeleton for motion recognition. The human skeleton can well represent the movement of the human body, which is helpful to analyze it. On the one hand, skeleton data are inherently robust in background noise, providing abstract and high-level features for human motion. On the other hand, skeleton data are very small compared to RGB data. This allows this article to design a better model. Therefore, this article expands skeleton-based motion recognition from single person to multiple people.


### 2.2. Bone-Based Interactive Recognition

With the development of deep learning, bone-based approaches are emerging. Literature [11] proposes a spatiotemporal LSTM network of node sequences. It extends the learning of LSTM to time domain, and each joint receives information from adjacent joints as well as the previous frame to encode spatiotemporal features. Then, a tree structure is used to represent the adjacent characteristics and motion relations between the nodes. Finally, the results of skeleton data are sent to LSTM network for modeling and identification. Literature [12] divides human skeleton into 5 parts according to the physical structure of a human body and divides them into 5 bidirectional recursive connected subnets, respectively. The researcher proposes an end-to-end spatiotemporal attention model for identifying human actions from skeletal data [13]. Based on LSTM and RNN, a spatial attention module with a joint selection gate is designed. It adaptively allocates different attention to different joints of the input frame within each frame. There are also some methods based on CNN. For example, literature [14] represents bone sequences as a series of enhanced visual and motion color images. The method implicitly describes the spatiotemporal skeletal joints in a compact and unique way. Studies also combine convolutional neural networks with recursive neural networks to perform more complex temporal reasoning for interactions. In view of the good performance of RNN and CNN in skeleton-based action recognition, literature [15] proposed a deep network structure combining CNN classification with RNN. It realizes the attention mechanism of human interaction recognition. The method based on RNN has strong ability of sequence data modeling, and the method based on CNN has good parallelism, and the training process is relatively simple. But neither CNN nor RNN can fully represent the structure of the skeleton. Literature [16] proposed a graph-based regression GCN method for skeleton-based motion recognition to capture spatiotemporal changes in data. However, these methods do not have explicit graph constructs in the actions of identifying interactions. This article further uses the relationship between skeletons to extract interactive features between human bodies. The graph convolution is combined with RNN to better extract the dependency information between nodes and frames.


## 2.1. Image-Based Interactive Recognition

Much of the early recognition work was based on manually constructed features, for example, using directional gradient histogram and optical flow directional information histogram [9] to extract appearance features of static information, or using optical flow to extract motion features of dynamic information. Newer approaches rely on deep learning. Literature [10] uses the deep learning network for interactive behavior recognition, extracting optical flow characteristic information through CNN and then feeding it into a classifier to realize action recognition.Although the motion recognition method based on RGB video or optical flow has high performance, there are still some problems. For example, it is easily affected by background, illumination, and appearance changes, and it requires high computational cost to extract optical flow information. Some work has been done to extract bone data to avoid learning interaction patterns directly from videos. In some research studies on single-person motion recognition, most scholars use human skeleton for motion recognition. The human skeleton can well represent the movement of the human body, which is helpful to analyze it. On the one hand, skeleton data are inherently robust in background noise, providing abstract and high-level features for human motion. On the other hand, skeleton data are very small compared to RGB data. This allows this article to design a better model. Therefore, this article expands skeleton-based motion recognition from single person to multiple people.


## 2.2. Bone-Based Interactive Recognition

With the development of deep learning, bone-based approaches are emerging. Literature [11] proposes a spatiotemporal LSTM network of node sequences. It extends the learning of LSTM to time domain, and each joint receives information from adjacent joints as well as the previous frame to encode spatiotemporal features. Then, a tree structure is used to represent the adjacent characteristics and motion relations between the nodes. Finally, the results of skeleton data are sent to LSTM network for modeling and identification. Literature [12] divides human skeleton into 5 parts according to the physical structure of a human body and divides them into 5 bidirectional recursive connected subnets, respectively. The researcher proposes an end-to-end spatiotemporal attention model for identifying human actions from skeletal data [13]. Based on LSTM and RNN, a spatial attention module with a joint selection gate is designed. It adaptively allocates different attention to different joints of the input frame within each frame. There are also some methods based on CNN. For example, literature [14] represents bone sequences as a series of enhanced visual and motion color images. The method implicitly describes the spatiotemporal skeletal joints in a compact and unique way. Studies also combine convolutional neural networks with recursive neural networks to perform more complex temporal reasoning for interactions. In view of the good performance of RNN and CNN in skeleton-based action recognition, literature [15] proposed a deep network structure combining CNN classification with RNN. It realizes the attention mechanism of human interaction recognition. The method based on RNN has strong ability of sequence data modeling, and the method based on CNN has good parallelism, and the training process is relatively simple. But neither CNN nor RNN can fully represent the structure of the skeleton. Literature [16] proposed a graph-based regression GCN method for skeleton-based motion recognition to capture spatiotemporal changes in data. However, these methods do not have explicit graph constructs in the actions of identifying interactions. This article further uses the relationship between skeletons to extract interactive features between human bodies. The graph convolution is combined with RNN to better extract the dependency information between nodes and frames.


## 3. The Proposed Evaluation System of Foreign Language Teaching Quality

### 3.1. Intraframe Interaction Modeling

The connection of key points is divided into single-person connection within the frame, interactive connection, and interframe connection. These connections are designed by different methods, and then, the spectral convolution is used to obtain the variation characteristics. Then, sequence-dependent information is acquired by combining with slice RNN for action recognition.In-frame design is divided into single-person design and interactive design. For a single person in each frame, the human body is modeled by a connected graph. The human body connectedness graph is only represented by the natural connection of individuals, so the global information of the human body cannot be well extracted. Therefore, it can be divided into internal connection and external connection through different correlations between nodes. Internal connections include physical connections between joints, and external connections represent potential connections between joints that are not physically connected. There is no skeletal connection between the human hand and head during communication. But because people generally place their hands in front of their bodies, there is an underlying relationship between the hands and the head. Establish external connections between them. Different parameters are set in the weighted adjacency matrix to distinguish the two relations. As shown in Figure1, the internally connected edge and externally connected edge are given different weights, and the weight of the intraframe edge is set as(1)mx,y=α,x,y∈ε1,β,x,y∈ε2,0,x=y,where mx,y indicates that the joints are not connected. α and β indicate the parameters set for internal and external connection, respectively. In addition, the connection between the joints was represented by ε1 and ε2, respectively. ε1 represents the internal connection between the joints, as shown by the solid black line in Figure 1. As an important property, the distance between the connecting nodes remains constant during motion. ε2 represents the external connection between the joints, as shown by the dotted line in Figure 1. External dependence refers to the disconnection between two joints, which is also an important factor in the process of motion.Figure 1
Interframe connectivity diagram.Unlike previous work, in bone interaction recognition, the joints between two people are disconnected. Learning how to describe how each object relates to each other is necessary to merge two people and their interaction information. By analyzing the structure of the bones between two people, information about their interactions can be extracted. Interaction design was carried out between participants of the action, and two independent skeleton diagrams were connected through the joint. Then, they are integrated into an action skeleton diagram with interactive information, and the interactive information of actions can be extracted through the graph convolutional network.Interaction design consists of two parts, and the joining of points prone to joint-like changes is called correspondence joining. Usingε3 to represent corresponding associations, such as hugging, the two participants performed roughly the same. Establish connections between the corresponding gateways, as shown in the dotted line in Figure 2. These correspondences play an important role when the participants’ actions are generally consistent. In addition, connections between other nodes are called potential connections. The potential connections are indicated by ε4 as represented by the dotted line in Figure 2. Assign θ to the weight of the edge in ε3 and δ to the weight of the edge in ε4, that is,(2)mx,y=θ,x,y∈ε3,δ,x,y∈ε4,0,other,where x and y represent the key points of different people. The adjacency matrix within a single frame is expressed as(3)G∗=U1W1W2U2,where U1 and U2 describe the one-person connection. W1 and W2 describe the interconnections.Figure 2
Interframe connectivity of multiple people.In order to determine which nodes are connected for the interaction modeling within the above frame, the correlation between interaction nodes is measured by Euclidean distance. Calculate the value between all points:(4)dix,iy=ix−iy2,where ix and iy are the feature representations of key points x and y, respectively. Compute only the Euclidean distance of the externally connected and potentially connected edges. Then, the resulting distance is normalized. The results are mapped to between [0, 1], and the normalization method of maximum and minimum values is adopted, that is,(5)t=dx−dmindmax−dmin,where dmax represents the maximum joint distance and dmin represents the minimum joint distance.In this article, a new edge connection is generated whent < 0.3 is set experimentally. This not only adds some new necessary interconnections, but also gives the underlying graph some sparsity.


### 3.2. Interframe Modeling

Each joint is disconnected in the time domain, allowing each joint in framein to be connected to its corresponding neighborhood in previous frame in−1 and later frame in+1, as shown in Figure 2.Extending the receptive field by using more adjacent joints can help the model learn information about the changes in the time domain. These adjacent joints include two types: joints within the same video frame (intraframe joints) and joints between two video frames (interframe joints). The corresponding joint is represented asε5. The connected connection between each joint and the neighborhood of the corresponding joint in the adjacent frame is expressed as ε6. The weights of these two kinds of edges are expressed as(6)mx,y=γ,x,y∈ε5,λ,x,y∈ε6,0,other,where c and y represent the nodes between different frames. The finally constructed multiframe adjacency matrix is expressed as(7)Gtotal=G∗n−1Gn−1,n0Gn,n−1G∗nGn,n+10Gn+1,nG∗n+1,where G∗n represents the adjacency matrix of the in-frame modeling graph of frame x. Gx,y represents the adjacency matrix between frame x and frame y. Zero is the zero matrix. The calculated graph Laplace is thus L=D−Gtotal.


### 3.3. Spectral Convolution Algorithm Based on Connected Graph

The skeleton diagram is constructed by taking joints as nodes and the connections between nodes as edges. In a frame, joints are connected internally and externally to act as spatial edges. Interframe connections act as time edges, and the property of each node is the coordinate vector of the joint. The spectral convolution operation is applied to the spatiotemporal skeleton graph to obtain an advanced feature graph.Consider an undirected graphA = {Q, E, G} consisting of vertex set Q and edge set E connecting vertices and weighted adjacency matrix G. G is a real symmetric matrix, and g(xy) is the weight assigned to the edges (x, y) connecting vertices x and y. Assume that the weight is non-negative. Laplacian matrices defined by adjacency matrices can be used to reveal many useful properties of graphs. In different variations of the Laplace matrix, the combinatorial graph used is defined by Laplace:(8)L=D−G,where the Laplace definition of symmetry normalization is L˜=D−1/2LD−1/2. D is the degree matrix of dxx=∑y=1tgx,y.The basis of skeleton-based motion recognition is to capture the changes of joints and learn motion features for classification. Use Laplace to simulate the changes in bone. Laplace matrixL is essentially a high-pass operator that can capture the changes of underlying signals. In order to adapt the input sequence length to the input requirements of slice RNN, a full connection layer is used to adjust the data dimension. Finally, the output classification is generated by softmax activation function.


### 3.4. Timing Sequence Modeling Based on Slice RNN

Interframe modeling has been carried out above to expand the receptive field and learn time-domain change information. However, this kind of interframe modeling cannot capture the time information of the whole action sequence completely and cannot obtain the action sequence dependence information. Therefore, RNN is used in time series processing to solve the dependency problem of action sequence data. However, the current node information of the traditional RNN network is only related to the previous node, so it can only model short-term dynamic information and cannot store long-term sequence. Meanwhile, the standard RNN network structure cannot realize parallel computation like the CNN network model, so the slicing RNN network model is adopted to solve the above problems.The input sequence is divided into multiple sequence segments, and an independent RNN network is used to calculate each segment. In this article, the RNN hiding unit adopts the gated cyclic unit (GRU), which not only realizes the “parallelism” of computation, but also performs RNN feature extraction on each relatively short sequence fragment. The transfer of information between layers allows for a greater degree of retention of information about long-term dependencies.H represents the hidden layer state of the network, and Y represents the top-level output. The input data itself can compensate for the loss of long-term dependence at the slice point through interframe modeling.At level 0, the recursive unit acts on each of the smallest sequences by joining structures. Then, the last hidden state of each smallest sequence at level 0 is obtained and used as input to the parent sequence at level 1. The last hidden state of each subsequence atu − 1 layer is used as the input of its parent sequence at u layer. The last hidden state of the subsequence on the u layer is calculated:(9)bn1=GRU0mssn−l0+1∼n0,(10)bnu+1=GRUubn−luu∼bnu,where l0 represents the smallest sequence length at layer 0. lu represents the minimum sequence length of u layer. bnu represents the hidden layer representation of the n subsequence of u layer. mss0 represents the smallest sequence at layer 0. mssn−l0+1∼n0 is the calculation of hidden state in subsequence at layer 0. Different GRUs can be used for different layers. Equation (10) indicates that after the hidden state is calculated at layer 0, the next hidden state is calculated again with the calculation result, and the calculation is repeated. This operation is repeated between each subsequence on each layer until the final hidden state of the top layer (z-th layer) is obtained:(11)F=GRUzbn−lzz∼bnz.Similar to standard RNN, the softmax layer is added after the final hidden stateF to classify video actions, that is,(12)u=softmaxMFF+hF.


### 3.5. Design of Evaluation System

This system is capable of analyzing continuous images and identifying human behavior characteristics from images, which is shown in Figure3. The system realizes the functions of image interpretation and transcoding, image preprocessing, and face moving optical flow tracking by OpenCV. The algorithm in this article realizes the recognition of human morphological features. Finally, Python and deep learning architecture library are used to realize the recognition of facial features, including the performance of students’ specific behaviors such as nodding, bowing, and sleeping.Figure 3
Software system block diagram of the proposed evaluation system.Supported by the above technologies, this article analyzes the classroom video collected by the camera in real time. At present, it can recognize and output information mainly including the following three points:(1)
Students’ attendance. The number of students in a class can be calculated through the recognition of human morphological features. Combined with the information of courses and classes provided by the school educational administration system, the present course attendance rate and absence rate are calculated.(2)
Students’ attendance. By analyzing facial morphological features in successive images, the number of students facing the blackboard, lowering their heads, and lying prone at their desks for long periods of time were identified. Then, the current class attendance rate, head down (looking at mobile phones) rate, and sleep rate were calculated.(3)
Other teaching information. Through the action recognition of continuous images, the characteristics of students “rushing to” the classroom door are judged, and then, the class time of the current course is obtained. Due to the diversity and complexity of students’ movement behavior in the after-class, the judgment algorithm is not perfect, and the statistics is only an experimental function. Some statistics, such as absenteeism, tardiness, and mobile phone use, were part of the subsequent experiment.The data of students’ attendance rate, bowing rate, and abnormal attendance rate of each course in each classroom are counted and then sent to the special server of teaching evaluation system for further processing.


## 3.1. Intraframe Interaction Modeling

The connection of key points is divided into single-person connection within the frame, interactive connection, and interframe connection. These connections are designed by different methods, and then, the spectral convolution is used to obtain the variation characteristics. Then, sequence-dependent information is acquired by combining with slice RNN for action recognition.In-frame design is divided into single-person design and interactive design. For a single person in each frame, the human body is modeled by a connected graph. The human body connectedness graph is only represented by the natural connection of individuals, so the global information of the human body cannot be well extracted. Therefore, it can be divided into internal connection and external connection through different correlations between nodes. Internal connections include physical connections between joints, and external connections represent potential connections between joints that are not physically connected. There is no skeletal connection between the human hand and head during communication. But because people generally place their hands in front of their bodies, there is an underlying relationship between the hands and the head. Establish external connections between them. Different parameters are set in the weighted adjacency matrix to distinguish the two relations. As shown in Figure1, the internally connected edge and externally connected edge are given different weights, and the weight of the intraframe edge is set as(1)mx,y=α,x,y∈ε1,β,x,y∈ε2,0,x=y,where mx,y indicates that the joints are not connected. α and β indicate the parameters set for internal and external connection, respectively. In addition, the connection between the joints was represented by ε1 and ε2, respectively. ε1 represents the internal connection between the joints, as shown by the solid black line in Figure 1. As an important property, the distance between the connecting nodes remains constant during motion. ε2 represents the external connection between the joints, as shown by the dotted line in Figure 1. External dependence refers to the disconnection between two joints, which is also an important factor in the process of motion.Figure 1
Interframe connectivity diagram.Unlike previous work, in bone interaction recognition, the joints between two people are disconnected. Learning how to describe how each object relates to each other is necessary to merge two people and their interaction information. By analyzing the structure of the bones between two people, information about their interactions can be extracted. Interaction design was carried out between participants of the action, and two independent skeleton diagrams were connected through the joint. Then, they are integrated into an action skeleton diagram with interactive information, and the interactive information of actions can be extracted through the graph convolutional network.Interaction design consists of two parts, and the joining of points prone to joint-like changes is called correspondence joining. Usingε3 to represent corresponding associations, such as hugging, the two participants performed roughly the same. Establish connections between the corresponding gateways, as shown in the dotted line in Figure 2. These correspondences play an important role when the participants’ actions are generally consistent. In addition, connections between other nodes are called potential connections. The potential connections are indicated by ε4 as represented by the dotted line in Figure 2. Assign θ to the weight of the edge in ε3 and δ to the weight of the edge in ε4, that is,(2)mx,y=θ,x,y∈ε3,δ,x,y∈ε4,0,other,where x and y represent the key points of different people. The adjacency matrix within a single frame is expressed as(3)G∗=U1W1W2U2,where U1 and U2 describe the one-person connection. W1 and W2 describe the interconnections.Figure 2
Interframe connectivity of multiple people.In order to determine which nodes are connected for the interaction modeling within the above frame, the correlation between interaction nodes is measured by Euclidean distance. Calculate the value between all points:(4)dix,iy=ix−iy2,where ix and iy are the feature representations of key points x and y, respectively. Compute only the Euclidean distance of the externally connected and potentially connected edges. Then, the resulting distance is normalized. The results are mapped to between [0, 1], and the normalization method of maximum and minimum values is adopted, that is,(5)t=dx−dmindmax−dmin,where dmax represents the maximum joint distance and dmin represents the minimum joint distance.In this article, a new edge connection is generated whent < 0.3 is set experimentally. This not only adds some new necessary interconnections, but also gives the underlying graph some sparsity.


## 3.2. Interframe Modeling

Each joint is disconnected in the time domain, allowing each joint in framein to be connected to its corresponding neighborhood in previous frame in−1 and later frame in+1, as shown in Figure 2.Extending the receptive field by using more adjacent joints can help the model learn information about the changes in the time domain. These adjacent joints include two types: joints within the same video frame (intraframe joints) and joints between two video frames (interframe joints). The corresponding joint is represented asε5. The connected connection between each joint and the neighborhood of the corresponding joint in the adjacent frame is expressed as ε6. The weights of these two kinds of edges are expressed as(6)mx,y=γ,x,y∈ε5,λ,x,y∈ε6,0,other,where c and y represent the nodes between different frames. The finally constructed multiframe adjacency matrix is expressed as(7)Gtotal=G∗n−1Gn−1,n0Gn,n−1G∗nGn,n+10Gn+1,nG∗n+1,where G∗n represents the adjacency matrix of the in-frame modeling graph of frame x. Gx,y represents the adjacency matrix between frame x and frame y. Zero is the zero matrix. The calculated graph Laplace is thus L=D−Gtotal.


## 3.3. Spectral Convolution Algorithm Based on Connected Graph

The skeleton diagram is constructed by taking joints as nodes and the connections between nodes as edges. In a frame, joints are connected internally and externally to act as spatial edges. Interframe connections act as time edges, and the property of each node is the coordinate vector of the joint. The spectral convolution operation is applied to the spatiotemporal skeleton graph to obtain an advanced feature graph.Consider an undirected graphA = {Q, E, G} consisting of vertex set Q and edge set E connecting vertices and weighted adjacency matrix G. G is a real symmetric matrix, and g(xy) is the weight assigned to the edges (x, y) connecting vertices x and y. Assume that the weight is non-negative. Laplacian matrices defined by adjacency matrices can be used to reveal many useful properties of graphs. In different variations of the Laplace matrix, the combinatorial graph used is defined by Laplace:(8)L=D−G,where the Laplace definition of symmetry normalization is L˜=D−1/2LD−1/2. D is the degree matrix of dxx=∑y=1tgx,y.The basis of skeleton-based motion recognition is to capture the changes of joints and learn motion features for classification. Use Laplace to simulate the changes in bone. Laplace matrixL is essentially a high-pass operator that can capture the changes of underlying signals. In order to adapt the input sequence length to the input requirements of slice RNN, a full connection layer is used to adjust the data dimension. Finally, the output classification is generated by softmax activation function.


## 3.4. Timing Sequence Modeling Based on Slice RNN

Interframe modeling has been carried out above to expand the receptive field and learn time-domain change information. However, this kind of interframe modeling cannot capture the time information of the whole action sequence completely and cannot obtain the action sequence dependence information. Therefore, RNN is used in time series processing to solve the dependency problem of action sequence data. However, the current node information of the traditional RNN network is only related to the previous node, so it can only model short-term dynamic information and cannot store long-term sequence. Meanwhile, the standard RNN network structure cannot realize parallel computation like the CNN network model, so the slicing RNN network model is adopted to solve the above problems.The input sequence is divided into multiple sequence segments, and an independent RNN network is used to calculate each segment. In this article, the RNN hiding unit adopts the gated cyclic unit (GRU), which not only realizes the “parallelism” of computation, but also performs RNN feature extraction on each relatively short sequence fragment. The transfer of information between layers allows for a greater degree of retention of information about long-term dependencies.H represents the hidden layer state of the network, and Y represents the top-level output. The input data itself can compensate for the loss of long-term dependence at the slice point through interframe modeling.At level 0, the recursive unit acts on each of the smallest sequences by joining structures. Then, the last hidden state of each smallest sequence at level 0 is obtained and used as input to the parent sequence at level 1. The last hidden state of each subsequence atu − 1 layer is used as the input of its parent sequence at u layer. The last hidden state of the subsequence on the u layer is calculated:(9)bn1=GRU0mssn−l0+1∼n0,(10)bnu+1=GRUubn−luu∼bnu,where l0 represents the smallest sequence length at layer 0. lu represents the minimum sequence length of u layer. bnu represents the hidden layer representation of the n subsequence of u layer. mss0 represents the smallest sequence at layer 0. mssn−l0+1∼n0 is the calculation of hidden state in subsequence at layer 0. Different GRUs can be used for different layers. Equation (10) indicates that after the hidden state is calculated at layer 0, the next hidden state is calculated again with the calculation result, and the calculation is repeated. This operation is repeated between each subsequence on each layer until the final hidden state of the top layer (z-th layer) is obtained:(11)F=GRUzbn−lzz∼bnz.Similar to standard RNN, the softmax layer is added after the final hidden stateF to classify video actions, that is,(12)u=softmaxMFF+hF.


## 3.5. Design of Evaluation System

This system is capable of analyzing continuous images and identifying human behavior characteristics from images, which is shown in Figure3. The system realizes the functions of image interpretation and transcoding, image preprocessing, and face moving optical flow tracking by OpenCV. The algorithm in this article realizes the recognition of human morphological features. Finally, Python and deep learning architecture library are used to realize the recognition of facial features, including the performance of students’ specific behaviors such as nodding, bowing, and sleeping.Figure 3
Software system block diagram of the proposed evaluation system.Supported by the above technologies, this article analyzes the classroom video collected by the camera in real time. At present, it can recognize and output information mainly including the following three points:(1)
Students’ attendance. The number of students in a class can be calculated through the recognition of human morphological features. Combined with the information of courses and classes provided by the school educational administration system, the present course attendance rate and absence rate are calculated.(2)
Students’ attendance. By analyzing facial morphological features in successive images, the number of students facing the blackboard, lowering their heads, and lying prone at their desks for long periods of time were identified. Then, the current class attendance rate, head down (looking at mobile phones) rate, and sleep rate were calculated.(3)
Other teaching information. Through the action recognition of continuous images, the characteristics of students “rushing to” the classroom door are judged, and then, the class time of the current course is obtained. Due to the diversity and complexity of students’ movement behavior in the after-class, the judgment algorithm is not perfect, and the statistics is only an experimental function. Some statistics, such as absenteeism, tardiness, and mobile phone use, were part of the subsequent experiment.The data of students’ attendance rate, bowing rate, and abnormal attendance rate of each course in each classroom are counted and then sent to the special server of teaching evaluation system for further processing.


## 4. Experiment and Analysis

### 4.1. Validation of the Proposed Algorithm

In order to verify the effectiveness of the proposed algorithm, action recognition experiments are carried out on two large action recognition datasets, NTU 60 and NTU 120 [17]. NTU 60 and its extended version NTU 120 are currently the largest motion recognition dataset based on 3d human skeleton sequences. Each sample was an action sequence obtained by a Microsoft Kinect V2 camera in a restricted indoor environment. Each moment contains the 3-dimensional coordinates of 25 major human joints in the camera coordinate system. The NTU 60 dataset contains 56,880 samples from 60 action categories performed by 40 participants. The NTU 120 dataset extends the original sample by adding 57,600 samples. It expands the action categories to 120 and the number of participants to 106. Cross-participant recognition and cross-perspective recognition experiments were performed on both datasets. In the cross-participant recognition experiment, 50% participant samples were used as the training set, and the remaining 50% participant samples were used as the test set. In the cross-view recognition experiment, two of the samples were used as training sets, and the other sample was used as test sets. NTU 120 introduces more factors that affect perspective, including the height distance between the camera and the action participant, and extends cross-view recognition to cross-environment recognition (cross-setup). The two experiments investigated the learning ability of the algorithm model from different perspectives.The validity of each part of the proposed algorithm was evaluated by testing on NTU 60 dataset and NTU 120 dataset. The performance of the proposed algorithm is compared. The confusion matrix of this algorithm is shown in Figures4 and 5. It can be found by observation that the algorithm in this article is diagonally dominant on each class of NTU 60 and NTU 120 datasets. This shows that the algorithm in this article has achieved a good classification effect on these two datasets. But there are still behaviors that can be mislabeled because they are inherently so similar that even human perception can be hard to tell apart. This algorithm can extract the relation between objects well and reduce the error.Figure 4
The confusion matrix of the NTU 60 dataset.Figure 5
The confusion matrix of the NTU 120 dataset.In order to compare the recognition accuracy of the proposed algorithm with that of other algorithms, further tests were carried out on the NTU 60 dataset and the NTU 120 dataset. Literature [6], Literature [7], and Literature [18] are selected as the comparison algorithms. The comparison results are shown in Tables 1 and 2. The best experimental data are indicated in bold. It can be seen that the method in this article achieves optimal results on both datasets. This further verifies the advantages of the algorithm in this article.Table 1
Recognition accuracy of different methods on the NTU 60 dataset.
MethodsCross-participant identificationCross-view recognitionLiterature [6]82.288.5Literature [7]82.689.4Literature [18]81.791.6Proposed84.592.4Table 2
Recognition accuracy of different methods on the NTU 120 data set.
MethodsCross-participant identificationCross-view recognitionLiterature [6]63.362.9Literature [7]72.875.4Literature [18]73.380.1Proposed78.480.2In order to compare the convergence performance of the algorithm, the convergence curve can be obtained by counting the loss function of the training process, as shown in Figure6. It can be seen that Literature [6] is difficult to get convergence in some cases. The reason why the network is difficult to converge may be that its features are mainly calculated based on the low-order differential of the curve. It loses some sample information while maintaining invariance, so the distinction between samples becomes weak. However, this defect can be effectively compensated by combining invariant features with joint coordinates through channel enhancement. The algorithm in this article has faster convergence speed and more stable performance due to the fusion of spatiotemporal information.Figure 6
Convergence performance comparison.


### 4.2. Intelligent Evaluation of Foreign Language Classroom Teaching Quality

The special server of the foreign language course teaching evaluation system collects the data information sent by all cameras in the classroom. More intuitive statistics are processed by the back-end business logic module and stored in the database. The server system uses Windows Server + Tomcat + MySQL + Java software environment. The information stored in the database (data table) mainly includes the basic information of courses, classrooms, teachers, students, and colleges from the educational administration management system. At the same time, it also includes classroom situation, including course number, teacher, classroom number, and class time, which should be to the number of students, the number of students nod, the number of times of looking up, and the number of long time bow. In addition, there are separate data tables for high-frequency and specialized words told by teachers. The table also records the classroom, time, course name, and the list of high-frequency and professional words.Educational administrators can call and view the classroom situation of each classroom in real time. In the video screen, the system marks the current student’s head and difficult-to-identify areas with different color boxes. In addition, the server is responsible for summarizing the data information and generating various statistical reports for different users. The statistical reports include the evaluation report of teaching quality for teaching administrators, the evaluation report of curriculum teaching, the evaluation report of class style of study, and the evaluation report of teaching quality for teachers.Through these reports, teachers’ teaching and students’ performance in class can be objectively reflected. It can help teachers understand the teaching situation after class and improve teaching methods. At the same time, it can also provide a fair and quantitative evaluation index for the teaching management and assessment of colleges and schools.


## 4.1. Validation of the Proposed Algorithm

In order to verify the effectiveness of the proposed algorithm, action recognition experiments are carried out on two large action recognition datasets, NTU 60 and NTU 120 [17]. NTU 60 and its extended version NTU 120 are currently the largest motion recognition dataset based on 3d human skeleton sequences. Each sample was an action sequence obtained by a Microsoft Kinect V2 camera in a restricted indoor environment. Each moment contains the 3-dimensional coordinates of 25 major human joints in the camera coordinate system. The NTU 60 dataset contains 56,880 samples from 60 action categories performed by 40 participants. The NTU 120 dataset extends the original sample by adding 57,600 samples. It expands the action categories to 120 and the number of participants to 106. Cross-participant recognition and cross-perspective recognition experiments were performed on both datasets. In the cross-participant recognition experiment, 50% participant samples were used as the training set, and the remaining 50% participant samples were used as the test set. In the cross-view recognition experiment, two of the samples were used as training sets, and the other sample was used as test sets. NTU 120 introduces more factors that affect perspective, including the height distance between the camera and the action participant, and extends cross-view recognition to cross-environment recognition (cross-setup). The two experiments investigated the learning ability of the algorithm model from different perspectives.The validity of each part of the proposed algorithm was evaluated by testing on NTU 60 dataset and NTU 120 dataset. The performance of the proposed algorithm is compared. The confusion matrix of this algorithm is shown in Figures4 and 5. It can be found by observation that the algorithm in this article is diagonally dominant on each class of NTU 60 and NTU 120 datasets. This shows that the algorithm in this article has achieved a good classification effect on these two datasets. But there are still behaviors that can be mislabeled because they are inherently so similar that even human perception can be hard to tell apart. This algorithm can extract the relation between objects well and reduce the error.Figure 4
The confusion matrix of the NTU 60 dataset.Figure 5
The confusion matrix of the NTU 120 dataset.In order to compare the recognition accuracy of the proposed algorithm with that of other algorithms, further tests were carried out on the NTU 60 dataset and the NTU 120 dataset. Literature [6], Literature [7], and Literature [18] are selected as the comparison algorithms. The comparison results are shown in Tables 1 and 2. The best experimental data are indicated in bold. It can be seen that the method in this article achieves optimal results on both datasets. This further verifies the advantages of the algorithm in this article.Table 1
Recognition accuracy of different methods on the NTU 60 dataset.
MethodsCross-participant identificationCross-view recognitionLiterature [6]82.288.5Literature [7]82.689.4Literature [18]81.791.6Proposed84.592.4Table 2
Recognition accuracy of different methods on the NTU 120 data set.
MethodsCross-participant identificationCross-view recognitionLiterature [6]63.362.9Literature [7]72.875.4Literature [18]73.380.1Proposed78.480.2In order to compare the convergence performance of the algorithm, the convergence curve can be obtained by counting the loss function of the training process, as shown in Figure6. It can be seen that Literature [6] is difficult to get convergence in some cases. The reason why the network is difficult to converge may be that its features are mainly calculated based on the low-order differential of the curve. It loses some sample information while maintaining invariance, so the distinction between samples becomes weak. However, this defect can be effectively compensated by combining invariant features with joint coordinates through channel enhancement. The algorithm in this article has faster convergence speed and more stable performance due to the fusion of spatiotemporal information.Figure 6
Convergence performance comparison.


## 4.2. Intelligent Evaluation of Foreign Language Classroom Teaching Quality

The special server of the foreign language course teaching evaluation system collects the data information sent by all cameras in the classroom. More intuitive statistics are processed by the back-end business logic module and stored in the database. The server system uses Windows Server + Tomcat + MySQL + Java software environment. The information stored in the database (data table) mainly includes the basic information of courses, classrooms, teachers, students, and colleges from the educational administration management system. At the same time, it also includes classroom situation, including course number, teacher, classroom number, and class time, which should be to the number of students, the number of students nod, the number of times of looking up, and the number of long time bow. In addition, there are separate data tables for high-frequency and specialized words told by teachers. The table also records the classroom, time, course name, and the list of high-frequency and professional words.Educational administrators can call and view the classroom situation of each classroom in real time. In the video screen, the system marks the current student’s head and difficult-to-identify areas with different color boxes. In addition, the server is responsible for summarizing the data information and generating various statistical reports for different users. The statistical reports include the evaluation report of teaching quality for teaching administrators, the evaluation report of curriculum teaching, the evaluation report of class style of study, and the evaluation report of teaching quality for teachers.Through these reports, teachers’ teaching and students’ performance in class can be objectively reflected. It can help teachers understand the teaching situation after class and improve teaching methods. At the same time, it can also provide a fair and quantitative evaluation index for the teaching management and assessment of colleges and schools.


## 5. Conclusion

Foreign language courses play an important role in basic education. To analyze and judge students’ behavior in a foreign language classroom, this article proposes a foreign language teaching quality evaluation system based on the fusion of spatiotemporal features. In order to describe interaction information effectively, a spatiotemporal modeling method was proposed, which combined intraframe interaction modeling design with interframe modeling. The potential relationship between joints is used for better identification to take full advantage of the spatial and temporal dependence of joints in the human body. The interactive skeleton graph is effectively represented, and then, the spectral convolution is used to extract spatial features. The algorithm in this article improves the accuracy of interactive action recognition, and experiments show the superiority of this method. The evaluation of classroom quality includes not only students’ behavior in class, but also students’ behavior outside class. Therefore, more factors will be considered in the evaluation system in the future to make the system more perfect.



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*Source: 9390358-2022-04-23.xml*