# An Evaluation Method of English Teaching Ability Based on Deep Learning

Authors: Chunyan Song
Journal: Security and Communication Networks
 (2022)

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## Abstract

The traditional English teaching ability assessment algorithm has some problems, such as low accuracy and poor effect. Therefore, this paper proposes an English teaching ability assessment algorithm based on deep learning. According to the proposed method, it analyzes the characteristics of the input layer, convolution layer, pooling layer, and fully connected layer in the deep learning technology. And then, it also help to determine the dimensions of the convolution kernel matrix and derive the weight parameters through back propagation. Finally, it introduces the open-minded computing for deep learning assessment of English teaching ability, calculate the best standard data and best vector data ratio, and establish a deep learning model to realize English teaching ability assessment. The experimental results show that this method can improve the accuracy of English teaching ability assessment.


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## Body

## 1. Introduction

The cultivation of students’ deep learning ability is currently a hot spot in basic education research and an important way to cultivate students’ learning ability in the 21st century. According to the related literature, it shows that the information technology discipline lacks scientific and reasonable guidance for the cultivation of students’ deep learning ability, especially in the field of evaluation of the teaching effectiveness [1]. Although there are more and more applications of deep learning technology and related research in the field of classroom teaching analysis, but in fact there are currently few applied researches combined with deep learning technology, and there is a lack of representative solutions, but it does not mean that it is impossible to introduce deep learning technology in the classroom teaching field [2]. In fact, in the information-based teaching environment, the teaching video recording, which is a carrier to record the classroom teaching process, has become a popular research object for classroom teaching research. According to the teaching video recording, it can record real teaching activities comprehensively and completely [3]. Through observation of the classroom teaching process, it is found that the main part of the analysis work is to segment, identify, classify, and code the behavior or information of the teachers and students [4], and the classification and coding are just one of the main application areas of deep learning. Therefore, this research will discuss the optimization and improvement of classification and coding in the analysis of classroom teaching behavior with the help of deep learning technology. The common analysis framework of the teaching behavior analysis is the Flanders interactive analysis system and the interaction based on information technology. According to the analysis system, there are two levels of classification indicators. The first-level indicators are teacher language, student language, and classroom silence. The second-level indicators include further classification of the content of the first-level indicators, such as teaching and questioning in teacher language, praise, and criticism [5]. Based on this, the application process of deep learning technology in the field of teaching behavior analysis can be extracted. Above all, we can know that there are two key steps of the proposed method. First, we should determine a set of class teaching behavior classification and coding standard system, and then use deep learning and neural network-related technologies to carry out the content of classroom teaching behavior. Second, we should do the classified processing, and then realize the automated solution of analysis work.According to the classroom teaching, the traditional classroom teaching evaluation methods always take the way of listening to the lectures and filling out evaluation forms, and it is necessary to summarize multiple lectures to evaluate the teaching effect and quality. This evaluation method has a supervising effect on classroom teaching, but due to the random nature of experts’ listening to the lectures, this will lead to subjective limitations, contingency, and time-consuming and labor-consuming evaluation results inevitably [6]. The using of the artificial intelligence technology in the field of teaching evaluation can analyze and evaluate the learning process and results and provide a scientific basis for teaching decision-making. With the intelligent education that integrates the artificial intelligence and education to carry out student learning behavior analysis, which mainly rely on computer vision, deep learning, and other technologies and algorithms to intelligently identify and count the interactive classroom learning behavior of students in the video, can solve the problem of classroom teaching effect evaluation. It can also effectively promote the development of traditional classroom learning in the direction of informatization and intelligence [7].According to the deep neural networks, which is also called deep learning, it can automatically learn the method of pattern features, and then integrate feature learning into the process of building the model, thereby it can reduce the incompleteness caused by the human design features. For the English teaching ability evaluation, it is important to establish a network that can get the pattern features of English teaching ability. So, we can use deep learning in this paper. At present, some applications with deep learning as the core have achieved recognition or classification performance that surpasses existing algorithms in application scenarios that meet specific conditions [8]. In order to improve the performance of learning evaluation, a method of student behavior identification and teaching effect evaluation based on deep learning is proposed. Through international Chinese interactive classroom teaching, students' learning behavior is analyzed and counted, and a teaching effect evaluation model is established to realize the teaching effect evaluation and ultimately improve the effectiveness of classroom teaching.The contributions of this paper can be concluded as follows:(1)
According to this paper, it deal with the low accuracy and poor effect of the traditional English teaching ability assessment algorithm that can solve practical problems.(2)
This paper use the deep learning in the English teaching ability evaluation that has not been applied before.The structure of this paper can be described as follows: Section1 is the introduction, which can give the background of this paper. Section 2 gives the introduction of deep learning method. Section 3 is English teaching ability assessment algorithm based on deep learning. Section 4 gives the experiment. Conclusion is given in Section 5.


## 2. Deep Learning Technology

Deep learning is a method in the field of machine learning based on the characterization of data. Its core is to use multiple processing layers to abstract massive amounts of data at a high level. The processing layers usually contain complex results or consist of multiple nonlinear transformations. Compared with general machine learning methods, deep learning uses unsupervised or semi-supervised feature learning and efficient hierarchical feature extraction algorithms to replace artificial feature extraction methods, so as to extract deeper feature information by learning. What is more, deep learning can also be applied to training unlabeled data that can extract richer feature information, which has therefore become a major advantage of deep learning [9]. In recent years, with the development of hardware acceleration, the emergence of high-performance graphics processing units (GPU) has greatly accelerated the speed of numerical operations and matrix operations, thereby greatly shortening the training and running time of deep learning algorithms, which makes it costly deep learning has been refocused.Actually, deep learning uses the idea of hierarchical abstraction, and higher level concepts can be obtained through the learning of low-level concepts. Distributed representation, which is the core foundation of deep learning, assumes that the interaction between different factors will produce the final observation value, while deep learning further assumes that the process of this interaction can be subdivided into multiple levels, and each level represents a different level of abstraction of the final observations [10]. Therefore, by adjusting the number of layers of the deep learning and the scale of a single layer, different degrees of abstraction of observations can be designed. Representative models of deep learning are shown in Figure 1.Figure 1
Representative models of deep learning.Due to the development of deep learning technology, it has been widely used or studied in various fields, especially in the fields of computer vision, speech recognition, biological information, and natural language processing. Deep learning has also developed a series of representative models. As shown in Figure1, it includes the perceptron model (PM), the restricted Boltzmann machine (RBM), the deep belief network (DBN), and the convolutional neural Network (DCN), in which convolutional neural networks perform particularly well in the field of computer vision.


### 2.1. Deep Convolutional Neural Network

Deep convolutional neural network simplifies the feedforward neural network in the network structure and requires fewer parameters to estimate than other deep learning network structures. A deep convolutional neural network is a feedforward network that contains a network layer composed of multiple two-dimensional planes. Specifically, the network structure can be divided into an input layer, an intermediate layer, and an output layer. The intermediate layer includes a convolutional layer and a pooling layer [11]. Such a network structure determines that the deep convolutional neural network can use the two-dimensional structure of the input data, which also makes it to have more prominent results in the field of computer vision and speech recognition. The following briefly introduces the major components of the network structure.The input layer of the deep convolutional neural network directly receives two-dimensional visual patterns as the input of the network, such as two-dimensional images. The images are not limited to grayscale images. Multichannel images, image sequences, or video sequences can be supported. It is worth noted that the convolutional neural network eliminates the need to artificially propose relevant and qualified features as the input process and can directly extract features from the input data for training, which can not only train a classifier with better classification results but also the process of artificial pretreatment can be largely avoided [12].The convolutional layer belongs to the middle layer of the network and is used for feature extraction. Each convolutional layer in a convolutional neural network contains multiple convolutional neurons, and each convolutional neuron is only connected to the corresponding position of the previous layer of the network, so it is also responsible for this part of the image feature extraction [13]. As for the specific extracted feature performance, it is determined by the connection weight of the convolutional neuron and the corresponding receptive field, and these weights are optimized by the back propagation algorithm. The convolutional neural network limits the weights of the connections between different neurons in the same convolutional layer and their corresponding receptive fields. The weights must be equal, that is, a convolutional layer is only used to extract the same feature at different positions in the previous layer of the network. This restriction is called weight sharing to further reduce network parameters. In addition, through this restriction on weight sharing, the network can be encouraged to learn location-independent robust feature abstraction for classification, that is, no matter where the feature appears, the classifier can always extract it and carry out the correct classification [14].The convolution operation of the convolutional layer is mainly used to extract different features of the input. The convolutional layer with a low network layer can usually only extract some low-level features (such as edges, corners, or lines), but by designing multiple convolutional layers. From these relatively low-level features, richer and more complex features can be extracted for the final classification task.(1)1∗11∗0011∗10∗10101101111⊗1011=2¯11221233.For the forward calculation of the convolutional layer, the input comes from the input layer or the upper-level pooling layer. For example, as shown in formula (1), the input is a 4 × 4 image, after a 2 × 2 convolution kernel, get a 3 × 3 map, namely A4×4⊗B2×2⟶C3×3. Each time the convolution kernel is multiplied by the corresponding element in the map, the convolution kernel will move to the next neuron for calculation [15].The first element 2 inC3×3 shown in formula (1) is the result obtained when the convolution kernel B (2 × 2) is calculated in the upper left corner of A (4 × 4). And there is the following relationship between the dimension of the output map and the input map and the convolution kernel: For the input map of m×m and the convolution kernel of n×n, the dimension of the output map is(2)d×d=m−n+1×m−n+1.For the back propagation process of the convolutional layer, if thea node of the ith layer has an influence on the ß node of the i + 1 layer through the interlayer weight, then in the back propagation process, the gradient passes the corresponding weight from the node propagate back to the node [16]. Specifically for the convolutional layer, it is only necessary to pay attention to which units in the convolutional layer C + 1 are associated with each neuron in the convolutional layer C.Still taking formula (1) as an example, matrix A(1,1) is related to matrix C(1,1) through weights and convolution kernel matrix B(1,1), and A(1,2) is related to matrix C(1,1) through weights and B(1, (2) and C(1, (2) are related, and so on. The specific way to realize this association is as follows: assume that matrix D is a gradient matrix that propagates back to the convolutional layer. Take formula (1) as an example. The dimension of this matrix should be equal to that of matrix C, that is, 3×3. The association is realized as follows: first, the matrix D(3, (3) needs to be expanded to a 5 × 5 matrix, and the expansion formula is (n + 2(k − 1)) × (n + 2(k-1)), where n is the dimension of the matrix to be convolved, k is the dimension of the convolution kernel matrix, that is, (3 + 2(2 − 1)) × (3 + 2(2 − 1)) = 5 × 5. Second, the convolution kernel matrix is rotated by 180° to obtain the back-propagation convolution kernel, and the expanded gradient matrix Dexpand and the rotated convolution kernel Brotation are convolved to obtain the final result. The specific calculation process of the entire back propagation is as follows [17]:(3)D=111111111⟶ExpandDexpand=0000001110011100111000000,B=1011⟶rotationBrotation=1101,Aback−prop=Dexpand⊗Brotation=0000001110011100111000000⊗1101=1110233123311221.Pooling layer: the pooling layer is also called the downsampling layer, which is usually located after the convolutional layer and is the feature mapping layer. Since the output of the convolutional layer is often high-dimensional features, the pooling layer cuts such high-dimensional features into several regions to obtain the maximum or average value of these regions, thereby obtaining new low-dimensional features [18]. Because of this feature, the pooling layer has strong robustness. Even if other values in a certain area change slightly, or the image is slightly shifted, the pooling result of the pooling layer can still remain unchanged, which not only further reduces the number of parameters of the network is improved, and at the same time, the phenomenon of overfitting can be prevented. The pooling layer generally does not have configured parameters. In the back propagation process, the pooling layer only needs to derive the parameters without updating the weights [19]. For the forward calculation of the pooling layer, the pooling layer down-samples each nonoverlapping area of the matrix output by the convolutional layer. There are three sampling methods: the maximum value, the average value, or the minimum value of the selected area. Assuming that the output matrix of the convolutional layer is A(4 × 4), as the input of the pooling layer, the pooling layer samples each nonoverlapping 2 × 2 area, and finally outputs a (4/2) × (4/2) matrix. The specific process of maximum pooling and average pooling of forward calculation is shown below, taking the two cases of maximum pooling and average pooling as examples [20].(4)A=1210233213212010⟶max−pooling3332;A=1210233213212010⟶meam−pooling4332.For the back propagation process of the pooling layer, different sampling methods will also lead to different ways of back calculation. For the backward propagation process of max-pooling, only the position where the maximum value appears in the forward propagation of each region will contribute to the next layer, and the corresponding residual error will be transferred to this position, and other nonmaximum regions will contribute to the next layer. Backward propagation has no effect [21]. For mean-pooling, the difference is that the residual of each area will be divided into n ×n equal parts and evenly transmitted to the corresponding area of the previous layer. The specific process is as shown in formula (4).(5)1234⟶backwardmax−pooling0000012003400000;1234⟶backwardmeam−pooling1414121214141212343411343411.Each node in the fully connected layer structure is connected to all corresponding nodes in the previous layer, and its function is to integrate the previously extracted features. Due to the structural characteristics of the full mapping of the fully connected layer, the amount of parameters that this layer needs to be trained is very large [22].For the forward calculation process of the fully connected layer, it is essentially a purely linear weighted summation process. In the specific calculation of the output of the fully connected layer, each output is essentially the output of each node in the network structure of the previous layer multiplied by a weight coefficient W and a corresponding bias termb is added. Suppose an input has a 50 × 4 × 4 neuron node, and the output has a fully connected layer of 500 nodes, which requires 50 × 4 × 4×500 = 400,000 weight parameters and 500 bias parameters. Taking the simple network in Figure 2 as an example, a simple derivation of the entire weight parameter and bias parameter process is carried out. xi represents the input of the fully connected layer, and ∀ai represents the output of the fully connected layer, where i∈1,2,3, According to the characteristics of the fully connected layer, the following relationships can be obtained:(6)ai=∑j=13Wijxj+bi,i∈1,2,3.Figure 2
Fully connected layer in simple network.Fully connected layer in simple network is shown in Figure2.It can also be expressed intuitively in the form of a matrix, specifically:(7)a1a2a3=W×b=W11W12W13W21W22W23W31W32W33×x1x2x3+b1b2b3,where W is the weight parameter matrix and b is the bias parameter matrix. For the back-propagation process of the fully connected layer, it can be split into three derivation processes to meet the requirements of updating the weight parameter W and the bias term b on the one hand and passing the gradient forward through the network on the other hand, specifically derivation of the input, derivation of the weight parameter, and derivation of the bias term. The following takes a fully connected layer F1 with 50 × 4 × 4 = 800 input nodes and 500 output nodes as an example to describe the three derivation process in detail.Differentiate the input. That is, the output of the previous layer of the network is derived. Assuming that the gradient passed toF1 is ∂loss/∂a, the partial derivative of loss with respect to x can be obtained by the chain derivation rule. The first step is to find the partial derivative of the output ai to the input xi.(8)∂ai∂aj=∑i800wij∂xj=wij.Then use the chain rule to find the partial derivative of loss with respect tox:(9)∂loss∂xk=∑j500∂loss∂aj∂aj∂xk=∑j500∂loss∂aj×ωjk.It can be seen from the above derivation that in the forward calculation process, if the nodeai of the ith layer contributes to the node ai+1 of the i+1 layer through the weight parameter W, then in the back propagation process, the gradient passes the weight. The parameters are propagated from node ai+1 back to node ai.Differentiate the weight parameterW. As mentioned earlier, the formula for the forward calculation process is ai=∑j=1nWijxj+bi, and it can be seen that ∂ai/∂ωij=xj, then:(10)∂loss∂ωkj=∂loss∂ak∂ak∂ωkj=∂loss∂ak×xj.Take the derivative of the bias termb. From the aforementioned forward calculation formula, we know that ∂ai/∂bj=1, which means that the partial derivative of loss to the bias term is equal to the partial derivative of the previous layer’s output.


## 2.1. Deep Convolutional Neural Network

Deep convolutional neural network simplifies the feedforward neural network in the network structure and requires fewer parameters to estimate than other deep learning network structures. A deep convolutional neural network is a feedforward network that contains a network layer composed of multiple two-dimensional planes. Specifically, the network structure can be divided into an input layer, an intermediate layer, and an output layer. The intermediate layer includes a convolutional layer and a pooling layer [11]. Such a network structure determines that the deep convolutional neural network can use the two-dimensional structure of the input data, which also makes it to have more prominent results in the field of computer vision and speech recognition. The following briefly introduces the major components of the network structure.The input layer of the deep convolutional neural network directly receives two-dimensional visual patterns as the input of the network, such as two-dimensional images. The images are not limited to grayscale images. Multichannel images, image sequences, or video sequences can be supported. It is worth noted that the convolutional neural network eliminates the need to artificially propose relevant and qualified features as the input process and can directly extract features from the input data for training, which can not only train a classifier with better classification results but also the process of artificial pretreatment can be largely avoided [12].The convolutional layer belongs to the middle layer of the network and is used for feature extraction. Each convolutional layer in a convolutional neural network contains multiple convolutional neurons, and each convolutional neuron is only connected to the corresponding position of the previous layer of the network, so it is also responsible for this part of the image feature extraction [13]. As for the specific extracted feature performance, it is determined by the connection weight of the convolutional neuron and the corresponding receptive field, and these weights are optimized by the back propagation algorithm. The convolutional neural network limits the weights of the connections between different neurons in the same convolutional layer and their corresponding receptive fields. The weights must be equal, that is, a convolutional layer is only used to extract the same feature at different positions in the previous layer of the network. This restriction is called weight sharing to further reduce network parameters. In addition, through this restriction on weight sharing, the network can be encouraged to learn location-independent robust feature abstraction for classification, that is, no matter where the feature appears, the classifier can always extract it and carry out the correct classification [14].The convolution operation of the convolutional layer is mainly used to extract different features of the input. The convolutional layer with a low network layer can usually only extract some low-level features (such as edges, corners, or lines), but by designing multiple convolutional layers. From these relatively low-level features, richer and more complex features can be extracted for the final classification task.(1)1∗11∗0011∗10∗10101101111⊗1011=2¯11221233.For the forward calculation of the convolutional layer, the input comes from the input layer or the upper-level pooling layer. For example, as shown in formula (1), the input is a 4 × 4 image, after a 2 × 2 convolution kernel, get a 3 × 3 map, namely A4×4⊗B2×2⟶C3×3. Each time the convolution kernel is multiplied by the corresponding element in the map, the convolution kernel will move to the next neuron for calculation [15].The first element 2 inC3×3 shown in formula (1) is the result obtained when the convolution kernel B (2 × 2) is calculated in the upper left corner of A (4 × 4). And there is the following relationship between the dimension of the output map and the input map and the convolution kernel: For the input map of m×m and the convolution kernel of n×n, the dimension of the output map is(2)d×d=m−n+1×m−n+1.For the back propagation process of the convolutional layer, if thea node of the ith layer has an influence on the ß node of the i + 1 layer through the interlayer weight, then in the back propagation process, the gradient passes the corresponding weight from the node propagate back to the node [16]. Specifically for the convolutional layer, it is only necessary to pay attention to which units in the convolutional layer C + 1 are associated with each neuron in the convolutional layer C.Still taking formula (1) as an example, matrix A(1,1) is related to matrix C(1,1) through weights and convolution kernel matrix B(1,1), and A(1,2) is related to matrix C(1,1) through weights and B(1, (2) and C(1, (2) are related, and so on. The specific way to realize this association is as follows: assume that matrix D is a gradient matrix that propagates back to the convolutional layer. Take formula (1) as an example. The dimension of this matrix should be equal to that of matrix C, that is, 3×3. The association is realized as follows: first, the matrix D(3, (3) needs to be expanded to a 5 × 5 matrix, and the expansion formula is (n + 2(k − 1)) × (n + 2(k-1)), where n is the dimension of the matrix to be convolved, k is the dimension of the convolution kernel matrix, that is, (3 + 2(2 − 1)) × (3 + 2(2 − 1)) = 5 × 5. Second, the convolution kernel matrix is rotated by 180° to obtain the back-propagation convolution kernel, and the expanded gradient matrix Dexpand and the rotated convolution kernel Brotation are convolved to obtain the final result. The specific calculation process of the entire back propagation is as follows [17]:(3)D=111111111⟶ExpandDexpand=0000001110011100111000000,B=1011⟶rotationBrotation=1101,Aback−prop=Dexpand⊗Brotation=0000001110011100111000000⊗1101=1110233123311221.Pooling layer: the pooling layer is also called the downsampling layer, which is usually located after the convolutional layer and is the feature mapping layer. Since the output of the convolutional layer is often high-dimensional features, the pooling layer cuts such high-dimensional features into several regions to obtain the maximum or average value of these regions, thereby obtaining new low-dimensional features [18]. Because of this feature, the pooling layer has strong robustness. Even if other values in a certain area change slightly, or the image is slightly shifted, the pooling result of the pooling layer can still remain unchanged, which not only further reduces the number of parameters of the network is improved, and at the same time, the phenomenon of overfitting can be prevented. The pooling layer generally does not have configured parameters. In the back propagation process, the pooling layer only needs to derive the parameters without updating the weights [19]. For the forward calculation of the pooling layer, the pooling layer down-samples each nonoverlapping area of the matrix output by the convolutional layer. There are three sampling methods: the maximum value, the average value, or the minimum value of the selected area. Assuming that the output matrix of the convolutional layer is A(4 × 4), as the input of the pooling layer, the pooling layer samples each nonoverlapping 2 × 2 area, and finally outputs a (4/2) × (4/2) matrix. The specific process of maximum pooling and average pooling of forward calculation is shown below, taking the two cases of maximum pooling and average pooling as examples [20].(4)A=1210233213212010⟶max−pooling3332;A=1210233213212010⟶meam−pooling4332.For the back propagation process of the pooling layer, different sampling methods will also lead to different ways of back calculation. For the backward propagation process of max-pooling, only the position where the maximum value appears in the forward propagation of each region will contribute to the next layer, and the corresponding residual error will be transferred to this position, and other nonmaximum regions will contribute to the next layer. Backward propagation has no effect [21]. For mean-pooling, the difference is that the residual of each area will be divided into n ×n equal parts and evenly transmitted to the corresponding area of the previous layer. The specific process is as shown in formula (4).(5)1234⟶backwardmax−pooling0000012003400000;1234⟶backwardmeam−pooling1414121214141212343411343411.Each node in the fully connected layer structure is connected to all corresponding nodes in the previous layer, and its function is to integrate the previously extracted features. Due to the structural characteristics of the full mapping of the fully connected layer, the amount of parameters that this layer needs to be trained is very large [22].For the forward calculation process of the fully connected layer, it is essentially a purely linear weighted summation process. In the specific calculation of the output of the fully connected layer, each output is essentially the output of each node in the network structure of the previous layer multiplied by a weight coefficient W and a corresponding bias termb is added. Suppose an input has a 50 × 4 × 4 neuron node, and the output has a fully connected layer of 500 nodes, which requires 50 × 4 × 4×500 = 400,000 weight parameters and 500 bias parameters. Taking the simple network in Figure 2 as an example, a simple derivation of the entire weight parameter and bias parameter process is carried out. xi represents the input of the fully connected layer, and ∀ai represents the output of the fully connected layer, where i∈1,2,3, According to the characteristics of the fully connected layer, the following relationships can be obtained:(6)ai=∑j=13Wijxj+bi,i∈1,2,3.Figure 2
Fully connected layer in simple network.Fully connected layer in simple network is shown in Figure2.It can also be expressed intuitively in the form of a matrix, specifically:(7)a1a2a3=W×b=W11W12W13W21W22W23W31W32W33×x1x2x3+b1b2b3,where W is the weight parameter matrix and b is the bias parameter matrix. For the back-propagation process of the fully connected layer, it can be split into three derivation processes to meet the requirements of updating the weight parameter W and the bias term b on the one hand and passing the gradient forward through the network on the other hand, specifically derivation of the input, derivation of the weight parameter, and derivation of the bias term. The following takes a fully connected layer F1 with 50 × 4 × 4 = 800 input nodes and 500 output nodes as an example to describe the three derivation process in detail.Differentiate the input. That is, the output of the previous layer of the network is derived. Assuming that the gradient passed toF1 is ∂loss/∂a, the partial derivative of loss with respect to x can be obtained by the chain derivation rule. The first step is to find the partial derivative of the output ai to the input xi.(8)∂ai∂aj=∑i800wij∂xj=wij.Then use the chain rule to find the partial derivative of loss with respect tox:(9)∂loss∂xk=∑j500∂loss∂aj∂aj∂xk=∑j500∂loss∂aj×ωjk.It can be seen from the above derivation that in the forward calculation process, if the nodeai of the ith layer contributes to the node ai+1 of the i+1 layer through the weight parameter W, then in the back propagation process, the gradient passes the weight. The parameters are propagated from node ai+1 back to node ai.Differentiate the weight parameterW. As mentioned earlier, the formula for the forward calculation process is ai=∑j=1nWijxj+bi, and it can be seen that ∂ai/∂ωij=xj, then:(10)∂loss∂ωkj=∂loss∂ak∂ak∂ωkj=∂loss∂ak×xj.Take the derivative of the bias termb. From the aforementioned forward calculation formula, we know that ∂ai/∂bj=1, which means that the partial derivative of loss to the bias term is equal to the partial derivative of the previous layer’s output.


## 3. English Teaching Ability Assessment Algorithm Based on Deep Learning

The use of information processing technology and big data analysis technology for teaching evaluation and resource information scheduling has a positive and important significance in improving the quantitative management and planning capabilities of the teaching process. In this regard, this article studies the evaluation of English teaching ability based on deep learning. As the evaluation of English teaching ability is subject to many constraints, it is necessary to carry out quantitative testing and analysis of the level of English teaching, construct a parameter model and big data analysis model that constrain the level of English teaching, adopt big data information fusion and clustering processing methods for English teaching ability assessment, construct the objective function and statistical analysis model of teaching ability assessment, and improve the quantitative prediction ability of English teaching ability assessment.


### 3.1. Introducing Open-Minded Computing for Deep Learning Evaluation

The English teaching ability assessment method based on deep learning proposed in this paper is to complete the effective assessment of English teaching ability by establishing a deep learning assessment model. In order to ensure that the evaluation of the deep learning model can be efficiently and accurately evaluated, the open-minded calculation of deep learning is introduced. The open-minded calculation of deep learning evaluation can limit the evaluation criteria to a certain extent, unify different variables, ensure the evaluation criteria, and at the same time, there is only one variable in the evaluation process. Open-minded calculations using deep learning evaluation first need to establish a limiting matrix, so that multiple elements are replaced to ensure variable balance. The establishment of the limiting matrix is(11)A′=FPEFPEA11′A12′A13′0A22′A23′0A32′A33′.In the formula,A is the expression of the established limit matrix; F,P,E are three basic parameters, respectively; and A11′∼A33′ represent built-in variables. After the deep learning data are selected, the open-minded calculation conditions are limited. A large number of limiting conditions are used in the open-minded calculation process. For this reason, the expression conditions are used to connect to avoid the limitations of the calculation. The conditional expression is(12)PN=PN+1+Δ∘F1n=P0+Δ∘∑i=1nF1i.Open-minded calculation can be carried out after the applicable conditions are selected, the formula is as follows:(13)xio′=∑r=1suryri′−∑i=1mvixij′≤0,j=1,2,…,n,where xio′ is an open-minded variable level parameter; vi is the ability to express the highlight data in English teaching courses; yri′ is the measured elevation diffusion enthalpy; ur is the peak value of the highlighted data; and m,r,n are the maximum values of the limited dependent variables, respectively, minimum value and peak extreme value.After open-minded processing, the data must be calculated inverse distance weight [4], so that it can be identified by the design evaluation model, the process is as follows:(14)S′=maxmax∑j=1naij,s′,max∑j=1naij,s′.In the formula,S′ is the best open-minded data weighting ratio; max∑j=1naij,s′ is the best dependent variable extreme value change parameter; and aij,s′ is the standard amount of English teaching expression ability evaluation value. Through the above process, the characteristics and various expressions in the English teaching process can be comprehensively presented.


### 3.2. Deep Learning Evaluation Model Construction

The introduced deep learning can comprehensively express the characteristics of the data in the English teaching process and only need to enter the deep learning model to complete the comprehensive evaluation. Establishing a deep learning model first requires matching calculation, the process is as follows:(15)Pn′Pn″=Ps−∑i=0n−1F2′=Ps−f2′f2″1−f2=Ps−11−f2+f2′f2″1−f2,where Pn′ is the docking index of the ratio energy level; Ps is the minimum value of the corresponding recreation value during the weight calculation process; F2′ is the best ratio data; 1−f2′/1−f2 is the deep learning data model Should be connected; and f2′ is the minimum range ratio data. After the pairing, the model has a certain acceptance ability and cannot comprehensively analyze the data. Only after the parameter design calculation can the data be processed effectively. The process is as follows:(16)P′=Pn′,Bn′=Δf1n′,Bn=Δf1n′∑i=1n−n′f2n′,n>n′.Substitute in:(17)Bn+1Bn=∑i=1n−n′+1f2i∑i=1n−n′f2i=f2n−n′+1−1f2n−n′−1,where Bn+1 is the expression value of the elevation data; Bn is the average value of the comprehensive data; f2ii is the parameter variable used; and f2n−n′+1,f2n−n′ are continuous variable high-quality data. After participating in the establishment, the evaluation criteria can be set, and the process is as follows:(18)P2=PA1+fAP1−P0KA+PA1+fBP1−P0KBKA+KB=P1+fA′P1−P0=P1+f1′∘Δf1=P0+Δf1+Δ∘f1.In the formula:PA1 is the standard average; fA and fB are the best standard data and the best vector data, respectively; andΔf1 is the variable standard. The process of using the deep learning evaluation model designed in this article to evaluate English teaching ability is as follows:(19)Da0=Pa0−P0K0,Db0=Pb0−P0Kb,Da0+Db0.In the formula:Da0 is the estimated effective value of the independent variable data; Db0 is the estimated effective value of the dependent variable; and K0,Kb are the variable and nonvariable weighting coefficients, respectively. After the evaluation, NUYYT certification is required. After the certification is passed, the evaluation data are regarded as a valid value. The process is as follows:(20)J=KAfA+KBfBKA+KB,fB≤f1≤fA.In the formula:J is the effective value of NUYYT. After calculation, it shows that the evaluation data are not valid if it is equal to or greater than 0.7. KA,KB are high-level parameters and low-level parameters, respectively.


## 3.1. Introducing Open-Minded Computing for Deep Learning Evaluation

The English teaching ability assessment method based on deep learning proposed in this paper is to complete the effective assessment of English teaching ability by establishing a deep learning assessment model. In order to ensure that the evaluation of the deep learning model can be efficiently and accurately evaluated, the open-minded calculation of deep learning is introduced. The open-minded calculation of deep learning evaluation can limit the evaluation criteria to a certain extent, unify different variables, ensure the evaluation criteria, and at the same time, there is only one variable in the evaluation process. Open-minded calculations using deep learning evaluation first need to establish a limiting matrix, so that multiple elements are replaced to ensure variable balance. The establishment of the limiting matrix is(11)A′=FPEFPEA11′A12′A13′0A22′A23′0A32′A33′.In the formula,A is the expression of the established limit matrix; F,P,E are three basic parameters, respectively; and A11′∼A33′ represent built-in variables. After the deep learning data are selected, the open-minded calculation conditions are limited. A large number of limiting conditions are used in the open-minded calculation process. For this reason, the expression conditions are used to connect to avoid the limitations of the calculation. The conditional expression is(12)PN=PN+1+Δ∘F1n=P0+Δ∘∑i=1nF1i.Open-minded calculation can be carried out after the applicable conditions are selected, the formula is as follows:(13)xio′=∑r=1suryri′−∑i=1mvixij′≤0,j=1,2,…,n,where xio′ is an open-minded variable level parameter; vi is the ability to express the highlight data in English teaching courses; yri′ is the measured elevation diffusion enthalpy; ur is the peak value of the highlighted data; and m,r,n are the maximum values of the limited dependent variables, respectively, minimum value and peak extreme value.After open-minded processing, the data must be calculated inverse distance weight [4], so that it can be identified by the design evaluation model, the process is as follows:(14)S′=maxmax∑j=1naij,s′,max∑j=1naij,s′.In the formula,S′ is the best open-minded data weighting ratio; max∑j=1naij,s′ is the best dependent variable extreme value change parameter; and aij,s′ is the standard amount of English teaching expression ability evaluation value. Through the above process, the characteristics and various expressions in the English teaching process can be comprehensively presented.


## 3.2. Deep Learning Evaluation Model Construction

The introduced deep learning can comprehensively express the characteristics of the data in the English teaching process and only need to enter the deep learning model to complete the comprehensive evaluation. Establishing a deep learning model first requires matching calculation, the process is as follows:(15)Pn′Pn″=Ps−∑i=0n−1F2′=Ps−f2′f2″1−f2=Ps−11−f2+f2′f2″1−f2,where Pn′ is the docking index of the ratio energy level; Ps is the minimum value of the corresponding recreation value during the weight calculation process; F2′ is the best ratio data; 1−f2′/1−f2 is the deep learning data model Should be connected; and f2′ is the minimum range ratio data. After the pairing, the model has a certain acceptance ability and cannot comprehensively analyze the data. Only after the parameter design calculation can the data be processed effectively. The process is as follows:(16)P′=Pn′,Bn′=Δf1n′,Bn=Δf1n′∑i=1n−n′f2n′,n>n′.Substitute in:(17)Bn+1Bn=∑i=1n−n′+1f2i∑i=1n−n′f2i=f2n−n′+1−1f2n−n′−1,where Bn+1 is the expression value of the elevation data; Bn is the average value of the comprehensive data; f2ii is the parameter variable used; and f2n−n′+1,f2n−n′ are continuous variable high-quality data. After participating in the establishment, the evaluation criteria can be set, and the process is as follows:(18)P2=PA1+fAP1−P0KA+PA1+fBP1−P0KBKA+KB=P1+fA′P1−P0=P1+f1′∘Δf1=P0+Δf1+Δ∘f1.In the formula:PA1 is the standard average; fA and fB are the best standard data and the best vector data, respectively; andΔf1 is the variable standard. The process of using the deep learning evaluation model designed in this article to evaluate English teaching ability is as follows:(19)Da0=Pa0−P0K0,Db0=Pb0−P0Kb,Da0+Db0.In the formula:Da0 is the estimated effective value of the independent variable data; Db0 is the estimated effective value of the dependent variable; and K0,Kb are the variable and nonvariable weighting coefficients, respectively. After the evaluation, NUYYT certification is required. After the certification is passed, the evaluation data are regarded as a valid value. The process is as follows:(20)J=KAfA+KBfBKA+KB,fB≤f1≤fA.In the formula:J is the effective value of NUYYT. After calculation, it shows that the evaluation data are not valid if it is equal to or greater than 0.7. KA,KB are high-level parameters and low-level parameters, respectively.


## 4. Experiment

In order to ensure the effectiveness of the English teaching ability evaluation algorithm based on deep learning proposed in this paper, a comparative simulation experiment is designed. Different English teaching classrooms are selected for simulation experiments, and the English teaching ability assessment algorithm based on deep learning proposed in this paper is used for experimental verification. In order to ensure the validity of the experiment proposed in this article, traditional English teaching ability assessment methods are also used for evaluation.


### 4.1. Parameter Setting

In order to ensure the effectiveness of the evaluation method proposed in this paper, we set the network feedback to use the reference dataYX outside the value range [1000, 1250] and set the Dp,DD,Sj,Fi to 40 × 104, 12.5, 900, 1280, respectively. The design of the experiment in this paper needs to set up the experimental data to ensure that the experiment is compared under one variable. The set data are shown in Table 1.Table 1
Setting test data.
Test countEnglish teaching time/minEnglish teaching content115English grammar230English vocabulary explanation335Sentence pattern analysis440Oral practice545Writing practice745English conversationPerform experimental verification based on the above data.


### 4.2. Experimental Results

#### 4.2.1. Assessment Accuracy and Utilization Analysis

Table2 shows the test results of the evaluation accuracy and other indicators. The analysis shows that the method adopted in this paper has higher accuracy in teaching ability evaluation and better utilization of teaching resources.Table 2
Performance test comparison.
Evaluation periodMethod of this articleMultiparameter similarity measurement methodDiversified learning evaluation methodsAssessment accuracy (%)Utilization rate (%)Assessment accuracy (%)Utilization rate (%)Assessment accuracy (%)Utilization rate (%)198.2198.0287.4389.1283.2386.33297.0997.6786.5587.3482.1287.30396.3399.0388.7689.3186.0979.31498.5496.3489.4387.6788.2378.92Table2 shows that when the evaluation period is 1, the evaluation accuracy rate of this method is 98.21% and the utilization rate is 98.02%. The evaluation accuracy rate of the multiparameter similarity measure method is 87.43% and the utilization rate is 89.12%. The evaluation accuracy rate of the diversified learning evaluation method is 83.23% and the utilization rate is 86.33%. When the evaluation period is 3, the evaluation accuracy rate of this method is 96.33% and the utilization rate is 98.02%. The evaluation accuracy rate of the multiparameter similarity measure method is 99.03% and the utilization rate is 89.31%. The evaluation accuracy rate of the multiparameter learning evaluation method is 86.09% and the utilization rate is 79.31%. The accuracy and utilization of this method are much higher than the other two methods, and the performance of this method is better.


#### 4.2.2. English Teaching Detection Accuracy

CelebA data set was relabelled in this paper to generate gfP-DCN data set used in this paper. The data set still contains 202,599 images, of which 190,000 images were randomly selected for training the six GFP-DCN models, 150,000 images for the training process, and 40,000 test sets for the training process. The remaining 12,599 images in the data set were used for the test of the model after training in this part. Before analyzing the results, determine the following abbreviations: the hair-eye classifier is abbreviated as (He-DCN), “Hair-nose” (HN-DCN), “hair-mouth” (Hm-DCN), “eye-nose” (EN-DCN), “eye-mouth” (EM-DCN), and “nose-mouth” (nm-DCN). During the testing process, the gfP-DCN network was first constructed using the trained model, and then the GFP-DCN data set was taken as the input, which was preprocessed and input into the network, and the corresponding classification results and probability vectors were output after network processing. The whole process was carried out in GPU mode. Classification results of sample test images on deep learning model are shown in Figure3.Figure 3
Classification results of sample test images on deep learning model.Taking He-DCN as an example, the test results are shown in Figure4. It can be seen that the most likely classification of this image in He-DCN is the fourth category, namely “H1-E4”, which is consistent with the actual label. The analysis of test results for other classifiers was similar.Figure 4
Detection accuracy under different number of test samples.When testing on the test set, this paper adopts the method of gradually increasing the test amount and taking the average value of multiple tests. The specific test method is as follows: the test sample size is gradually increased, 50 samples are taken as an interval, and the test is carried out under the sample size of 1, 50, 100, 150, 200, respectively. During the test of each sample size, samples of this number are randomly selected from the test set for testing, and the average value of the detection rate is calculated by randomly selecting several times. In order to better count the test results of classifier under different sample sizes. The specific test results are shown in Figure4.As can be seen from this figure, when the sample size is about 1200, the detection rate of each classifier tends to be stable. According to the statistical results, when the sample size is 1200, the detection rates of each combined local feature classifier are as follows: HE-DCN (96.78%), HN-DCN (89.91%), HM-DCN (90.02%), EN-DCN (95.20%), EM-DCN (94.35%), and NM-DCN (93.46%).


## 4.1. Parameter Setting

In order to ensure the effectiveness of the evaluation method proposed in this paper, we set the network feedback to use the reference dataYX outside the value range [1000, 1250] and set the Dp,DD,Sj,Fi to 40 × 104, 12.5, 900, 1280, respectively. The design of the experiment in this paper needs to set up the experimental data to ensure that the experiment is compared under one variable. The set data are shown in Table 1.Table 1
Setting test data.
Test countEnglish teaching time/minEnglish teaching content115English grammar230English vocabulary explanation335Sentence pattern analysis440Oral practice545Writing practice745English conversationPerform experimental verification based on the above data.


## 4.2. Experimental Results

### 4.2.1. Assessment Accuracy and Utilization Analysis

Table2 shows the test results of the evaluation accuracy and other indicators. The analysis shows that the method adopted in this paper has higher accuracy in teaching ability evaluation and better utilization of teaching resources.Table 2
Performance test comparison.
Evaluation periodMethod of this articleMultiparameter similarity measurement methodDiversified learning evaluation methodsAssessment accuracy (%)Utilization rate (%)Assessment accuracy (%)Utilization rate (%)Assessment accuracy (%)Utilization rate (%)198.2198.0287.4389.1283.2386.33297.0997.6786.5587.3482.1287.30396.3399.0388.7689.3186.0979.31498.5496.3489.4387.6788.2378.92Table2 shows that when the evaluation period is 1, the evaluation accuracy rate of this method is 98.21% and the utilization rate is 98.02%. The evaluation accuracy rate of the multiparameter similarity measure method is 87.43% and the utilization rate is 89.12%. The evaluation accuracy rate of the diversified learning evaluation method is 83.23% and the utilization rate is 86.33%. When the evaluation period is 3, the evaluation accuracy rate of this method is 96.33% and the utilization rate is 98.02%. The evaluation accuracy rate of the multiparameter similarity measure method is 99.03% and the utilization rate is 89.31%. The evaluation accuracy rate of the multiparameter learning evaluation method is 86.09% and the utilization rate is 79.31%. The accuracy and utilization of this method are much higher than the other two methods, and the performance of this method is better.


### 4.2.2. English Teaching Detection Accuracy

CelebA data set was relabelled in this paper to generate gfP-DCN data set used in this paper. The data set still contains 202,599 images, of which 190,000 images were randomly selected for training the six GFP-DCN models, 150,000 images for the training process, and 40,000 test sets for the training process. The remaining 12,599 images in the data set were used for the test of the model after training in this part. Before analyzing the results, determine the following abbreviations: the hair-eye classifier is abbreviated as (He-DCN), “Hair-nose” (HN-DCN), “hair-mouth” (Hm-DCN), “eye-nose” (EN-DCN), “eye-mouth” (EM-DCN), and “nose-mouth” (nm-DCN). During the testing process, the gfP-DCN network was first constructed using the trained model, and then the GFP-DCN data set was taken as the input, which was preprocessed and input into the network, and the corresponding classification results and probability vectors were output after network processing. The whole process was carried out in GPU mode. Classification results of sample test images on deep learning model are shown in Figure3.Figure 3
Classification results of sample test images on deep learning model.Taking He-DCN as an example, the test results are shown in Figure4. It can be seen that the most likely classification of this image in He-DCN is the fourth category, namely “H1-E4”, which is consistent with the actual label. The analysis of test results for other classifiers was similar.Figure 4
Detection accuracy under different number of test samples.When testing on the test set, this paper adopts the method of gradually increasing the test amount and taking the average value of multiple tests. The specific test method is as follows: the test sample size is gradually increased, 50 samples are taken as an interval, and the test is carried out under the sample size of 1, 50, 100, 150, 200, respectively. During the test of each sample size, samples of this number are randomly selected from the test set for testing, and the average value of the detection rate is calculated by randomly selecting several times. In order to better count the test results of classifier under different sample sizes. The specific test results are shown in Figure4.As can be seen from this figure, when the sample size is about 1200, the detection rate of each classifier tends to be stable. According to the statistical results, when the sample size is 1200, the detection rates of each combined local feature classifier are as follows: HE-DCN (96.78%), HN-DCN (89.91%), HM-DCN (90.02%), EN-DCN (95.20%), EM-DCN (94.35%), and NM-DCN (93.46%).


## 4.2.1. Assessment Accuracy and Utilization Analysis

Table2 shows the test results of the evaluation accuracy and other indicators. The analysis shows that the method adopted in this paper has higher accuracy in teaching ability evaluation and better utilization of teaching resources.Table 2
Performance test comparison.
Evaluation periodMethod of this articleMultiparameter similarity measurement methodDiversified learning evaluation methodsAssessment accuracy (%)Utilization rate (%)Assessment accuracy (%)Utilization rate (%)Assessment accuracy (%)Utilization rate (%)198.2198.0287.4389.1283.2386.33297.0997.6786.5587.3482.1287.30396.3399.0388.7689.3186.0979.31498.5496.3489.4387.6788.2378.92Table2 shows that when the evaluation period is 1, the evaluation accuracy rate of this method is 98.21% and the utilization rate is 98.02%. The evaluation accuracy rate of the multiparameter similarity measure method is 87.43% and the utilization rate is 89.12%. The evaluation accuracy rate of the diversified learning evaluation method is 83.23% and the utilization rate is 86.33%. When the evaluation period is 3, the evaluation accuracy rate of this method is 96.33% and the utilization rate is 98.02%. The evaluation accuracy rate of the multiparameter similarity measure method is 99.03% and the utilization rate is 89.31%. The evaluation accuracy rate of the multiparameter learning evaluation method is 86.09% and the utilization rate is 79.31%. The accuracy and utilization of this method are much higher than the other two methods, and the performance of this method is better.


## 4.2.2. English Teaching Detection Accuracy

CelebA data set was relabelled in this paper to generate gfP-DCN data set used in this paper. The data set still contains 202,599 images, of which 190,000 images were randomly selected for training the six GFP-DCN models, 150,000 images for the training process, and 40,000 test sets for the training process. The remaining 12,599 images in the data set were used for the test of the model after training in this part. Before analyzing the results, determine the following abbreviations: the hair-eye classifier is abbreviated as (He-DCN), “Hair-nose” (HN-DCN), “hair-mouth” (Hm-DCN), “eye-nose” (EN-DCN), “eye-mouth” (EM-DCN), and “nose-mouth” (nm-DCN). During the testing process, the gfP-DCN network was first constructed using the trained model, and then the GFP-DCN data set was taken as the input, which was preprocessed and input into the network, and the corresponding classification results and probability vectors were output after network processing. The whole process was carried out in GPU mode. Classification results of sample test images on deep learning model are shown in Figure3.Figure 3
Classification results of sample test images on deep learning model.Taking He-DCN as an example, the test results are shown in Figure4. It can be seen that the most likely classification of this image in He-DCN is the fourth category, namely “H1-E4”, which is consistent with the actual label. The analysis of test results for other classifiers was similar.Figure 4
Detection accuracy under different number of test samples.When testing on the test set, this paper adopts the method of gradually increasing the test amount and taking the average value of multiple tests. The specific test method is as follows: the test sample size is gradually increased, 50 samples are taken as an interval, and the test is carried out under the sample size of 1, 50, 100, 150, 200, respectively. During the test of each sample size, samples of this number are randomly selected from the test set for testing, and the average value of the detection rate is calculated by randomly selecting several times. In order to better count the test results of classifier under different sample sizes. The specific test results are shown in Figure4.As can be seen from this figure, when the sample size is about 1200, the detection rate of each classifier tends to be stable. According to the statistical results, when the sample size is 1200, the detection rates of each combined local feature classifier are as follows: HE-DCN (96.78%), HN-DCN (89.91%), HM-DCN (90.02%), EN-DCN (95.20%), EM-DCN (94.35%), and NM-DCN (93.46%).


## 5. Conclusion

In order to improve the effectiveness of English teaching ability assessment, this paper proposes an English teaching ability assessment algorithm based on deep learning. According to this method, the characteristics of deep learning technology are analyzed, the dimension of convolution kernel matrix is determined, the deep learning evaluation of English teaching ability is carried out by introducing open-minded calculation, and the deep learning model is established to realize the evaluation of English teaching ability. The following results were obtained through the experiment:(1)
When the evaluation period is 3, the accuracy rate of this method is 96.33% and the utilization rate is 98.02%, which means that the evaluation performance of this method is good.(2)
When the sample size is about 1200, the detection rate of each classifier tends to be stable. The detection rates of local feature classifiers were he-DCN (96.78%), HN-DCN (89.91%), HM-DCN (90.02%), EN-DCN (95.20%), EM-DCN (94.35%), and Nm-DCN (93.46%), respectively. This indicates that the proposed method can obtain good detection results under various classifiers and can effectively ensure the evaluation effect of the proposed method.In summary, the proposed English teaching ability evaluation algorithm can improve the accuracy of English teaching ability assessment. However, because the deep learning algorithm requires very high hardware capability and large time complexity, it is necessary to study low overhead algorithms in the future.



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*Source: 8339137-2022-05-07.xml*