# Evaluation Algorithm of English Audiovisual Teaching Effect Based on Deep Learning

Authors: Amugu lang
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Traditional English teaching focuses on exam-oriented education. Although many students have passed various English tests and even achieved high scores, they cannot communicate freely in English. In order to change this situation, the state has adjusted the syllabus to enhance students’ audiovisual (AV) and oral ability. With more attention paid to the course of audiovisual speaking, the improvement of teaching quality has become an important part. The rationality of teaching effect evaluation plays a key role. Therefore, it is necessary to establish a scientific and reasonable AV and oral teaching effect evaluation model. Based on this, this paper proposes an evaluation algorithm of English AV and oral teaching effect based on deep learning (DL). Its characteristics are as follows: firstly, a data-based objective evaluation method of teaching effect is constructed, and then the adaptive algorithm is used to optimize the initial weight and threshold of deep neural network. English teaching implementation shows to us that English language characteristic and the matter of actual activities are closely connected to the context. English learning should not only pay attention to the understanding of English vocabulary and sentence and also carry out cultural exchanges with the countries, but English is the common language. The experimental results show that this model improves the prediction accuracy and convergence speed of teaching quality evaluation results, providing a more feasible scheme for the evaluation of English AV teaching effect.


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## Body

## 1. Introduction

AV teaching is a teaching method based on audition, which originated in the 1950s. AV teaching emphasizes that students use the ear, eye, brain, and other sensory organs to learn language perception [1]. AV teaching method is a development of AV teaching method to a certain extent. Some principles of AV teaching method are used in AV teaching [2]. AV teaching is valued by more and more educators for its unique advantages. The importance of English AV and oral teaching mainly includes the following points.Firstly, AV speaking is beneficial to English context teaching [3]. In modern teaching theory, context teaching is the key to English teaching, which is of great help to students’ understanding and application of English. English teaching practice tells us that English language characteristics and the occurrence of actual activities are closely related to the context. There is no doubt that AV teaching cannot be compared with other teaching modes in helping students understand, master, and use English to provide students with powerful learning help. Secondly, AV theory is beneficial to the cultivation of students’ cross-cultural consciousness [4]. In recent years, with the continuous development of educational theories, more and more attention has been paid to the cultivation of cross-cultural consciousness. The lack of cross culture in language teaching is even more serious than the error of tense and word order and even leads to the failure of language communication to a certain extent. Therefore, learning English should not only pay attention to the understanding of English vocabulary and structure, but also carry out cultural interconnect with the countries where English is the common language. Thirdly, the main reason why AV English teaching can effectively cultivate students’ cross-cultural awareness is to use multimedia and network resources to show the culture of English-speaking countries in the form of video [5]. Therefore, AV English teaching shows more cultural background and is more real, which is of great help to the cultivation of students’ cultural awareness. Lastly, AV teaching is beneficial to improve students’ understanding ability [6]. The key to AV English teaching lies in the combination of video and sound, which further combines verbal communication with nonverbal communication and enables students to acquire a wide range of English knowledge. By accepting English information through the parallel teaching method of picture and sound, students will have a feeling of being on the scene and effectively enhance their deep understanding and connotation of English so as to improve their understanding and memory of English knowledge.English teaching quality is an important index to measure the impact of English teaching [7]. At present, the school’s teaching effect and evaluation system need to be improved simultaneously, and a teaching quality evaluation system in line with the actual situation of the curriculum needs to be constructed. The school’s own positioning determines that the evaluation of teachers’ teaching effect cannot be evaluated only through theoretical teaching, but also pay more attention to students’ time and ability of English [8]. Let students not only have a solid theoretical foundation, but also have the time ability to meet the requirements of social development. Therefore, the evaluation of English AV and oral teaching effect is an important content of English teaching management. Therefore, it is necessary to make teaching quality evaluation more scientific and quantitative through intelligent means [9]. At the same time, the established English AV and oral teaching evaluation model has the functions of diagnosis, feedback, and incentive and can find the problems in the teaching process in time and feed back to teachers in time so as to improve and improve the teaching quality. In addition, the scientific evaluation results will also give teachers appropriate pressure and further encourage teachers to actively improve the quality of teaching and talent training [10].Based on the above teaching situation, this paper proposes an evaluation algorithm for English AV teaching effect based on DL. To provide a more feasible scheme for the evaluation of English AV teaching effect with neural network, take total 9 generations; the speed of the first generation as compared to all other eight generations is better and faster. The motive of our research is to evaluate the English AV and oral teaching effect evaluate successfully and make it more better with DL.The novelty of the paper lies in the fact that it introduces a new innovative model named as deep learning model. This model helps to evaluate the English teaching in universities and colleges. It makes the teaching more well organized and structured. In this paper, another model or software by using DL is introduced for undergraduates’ teaching and make it easier and faster.The rest of this paper is organized as follows.Section2 reviews the related work with limitations of previous research. Section 3 introduces the construction process of the whole model. The experimental results are demonstrated in Section 4, while conclusion is presented in Section 5.


## 2. Related Work

### 2.1. Research Status of Teaching Quality

With the rapid development of education informatization, it has been widely used in many aspects of higher education and achieved good results. However, the research and practice of teaching quality evaluation system and evaluation model are relatively lagging behind. As is known to all, teaching quality analysis and evaluation is a very complicated nonlinear process, involving many influential factors and dynamic variables, so the traditional teaching quality model is not fully competent to solve this kind of fuzzy problem [11]. The existing evaluation research mainly focuses on the following three aspects: evaluation subject, evaluation content, and evaluation method.For a long time, the traditional teaching quality evaluation is mostly by the teaching management in colleges and universities’ teaching experts, peer leaders, teachers, and students’ five kinds of evaluation subject to the influence of teachers’ teaching quality indicators do grading work, based on the weighted score the five evaluations, according to the result of the weighted value over a period of time can get the teachers teaching quality and teaching level [12]. Although the traditional model can also evaluate the teaching quality, but is affected by subjectivity, the evaluation results are vague and one-sided, and the results of teaching quality evaluation are easy to be transferred by people’s will [13].The research of teaching quality evaluation content is reflected in the establishment of teaching quality evaluation system, which adopts different teaching quality evaluation items according to evaluation subjects, types of universities, and disciplines [14]. The following illustrates the selection of teaching quality evaluation items from the horizontal and vertical perspectives of the teaching process. From the horizontal angle of teaching process, the teaching quality evaluation system takes teacher quality, teaching attitude, teaching content, teaching method, and teaching effect as the first-level index to set the second-level evaluation index [15]. The quality of teachers focuses on their own professional accomplishment, teaching attitude focuses on teaching preparation and management, and teaching content emphasizes the accuracy, depth, and breadth of the content prepared by teachers. From the vertical angle of the teaching process, the preparation before class, the performance in class, the final examination, and other aspects are taken for the first-level indicators to set the second-level indicators of the teaching quality evaluation system content [16]. According to the situation, the basic process of teaching effect evaluation is shown in Figure 1.Figure 1
Flowchart of teaching evaluation process.


### 2.2. Study on Evaluation Methods of Academic Quality

With the development of computer technology and information technology, many scholars adopt the mathematical model of teaching quality evaluation system directly, including fuzzy comprehensive evaluation method, cluster analysis, analytic hierarchy process, and gray system. Zhou et al. applied a multilevel evaluation model of 2-type fuzzy sets to the evaluation of teaching quality performance [17]. Feng et al. applied analytic hierarchy process and neural network to the teaching quality evaluation model [18]. Zhao applied the TOPSIS method of multiattribute decision making in teaching quality evaluation [19]. As mentioned above, the advantages of the above method are that various evaluation factors are fully considered and expert experience and knowledge are reflected. The deficiency is that there is randomness and subjectivity in the evaluation process so that the nonlinear relationship between the evaluation indicators and the teaching effect is not taken into account; therefore, the evaluation results are very subjective, leading to the failure to truly reflect the teaching quality. Artificial intelligence technology represented by neural network genetic algorithm is a new subject in the field of information science and technology research. It refers to the structure and function of human brain and uses neural network to simulate human thinking so that it has self-learning and nonlinear transformation functions so as to overcome the defects of the above methods.In recent years, many researchers have adopted neural network method in teaching quality evaluation model evaluation. The application of neural network to the field of prediction can be traced back to two scholars Lapeds and Paber; they write programs to simulate neural network to learn data sample knowledge and predict nonlinear data [20]. Bai and Guo applied BP neural network algorithm to teaching quality evaluation [21]. The application of this method in teaching quality evaluation can overcome the subjectivity of expert evaluation, and the results are relatively correct. The applicability of this method is good. Ge et al. applied the improved BP neural network algorithm to the study of classroom teaching quality evaluation model, and the evaluation effect was improved [22]. However, BP neural network adopts gradient descent algorithm, so its learning speed is slow, and it is highly dependent on initial weights and thresholds, so it is easy to fall into local optimal. Therefore, the traditional neural network needs to be improved in algorithm and structure.


### 2.3. Research Status of Neural Network Weight Initialization Methods

The objects of deep convolutional neural network initialization are the convolution kernel weights and convolutional bias parameters in the network model. The purpose of the weight initialization of the convolution kernel is to give the initial values specified for all parameters in the convolution kernel. At present, deep convolutional neural network mainly uses random initialization method and unsupervised pretraining method to initialize weights.Random initialization method is a simple and fast initialization method, so most of the network model weight initialization methods are used. Commonly used random initialization methods mainly include small random number initialization [23], Xavier initialization [24], and MSRA initialization [25]. The small random number initialization method breaks the symmetry of parameters. This method initializes weight parameters into some small random numbers, making the parameters obey the Gaussian distribution with mean value of 0 and standard deviation; however, when the random number is too small, the gradient in the back propagation will be very small, and the problem of gradient dispersion will be caused when the network layer is deepened, and the speed of parameter convergence will also be reduced.At the same time, some scholars used unsupervised pretraining to initialize the weight of the filter and achieved good results [26, 27]. The main idea is to use Sparse Autoencoder (SAE) to initialize the weights of the convolution filters at the first layer in the network model and obtain the filter set that conforms to the statistical characteristics of the data set so as to solve the problem that the networks at the first layer cannot be fully trained. Sparse autoencoder [28] is an unsupervised learning model, whose main idea is to encode and decode data by using hidden layer, so that the output data is roughly equal to the input data. Through this unsupervised pretraining method and supervised training of deep convolutional neural network, the optimal parameters of the network will be obtained at the fastest speed, and its training speed will be improved.


## 2.1. Research Status of Teaching Quality

With the rapid development of education informatization, it has been widely used in many aspects of higher education and achieved good results. However, the research and practice of teaching quality evaluation system and evaluation model are relatively lagging behind. As is known to all, teaching quality analysis and evaluation is a very complicated nonlinear process, involving many influential factors and dynamic variables, so the traditional teaching quality model is not fully competent to solve this kind of fuzzy problem [11]. The existing evaluation research mainly focuses on the following three aspects: evaluation subject, evaluation content, and evaluation method.For a long time, the traditional teaching quality evaluation is mostly by the teaching management in colleges and universities’ teaching experts, peer leaders, teachers, and students’ five kinds of evaluation subject to the influence of teachers’ teaching quality indicators do grading work, based on the weighted score the five evaluations, according to the result of the weighted value over a period of time can get the teachers teaching quality and teaching level [12]. Although the traditional model can also evaluate the teaching quality, but is affected by subjectivity, the evaluation results are vague and one-sided, and the results of teaching quality evaluation are easy to be transferred by people’s will [13].The research of teaching quality evaluation content is reflected in the establishment of teaching quality evaluation system, which adopts different teaching quality evaluation items according to evaluation subjects, types of universities, and disciplines [14]. The following illustrates the selection of teaching quality evaluation items from the horizontal and vertical perspectives of the teaching process. From the horizontal angle of teaching process, the teaching quality evaluation system takes teacher quality, teaching attitude, teaching content, teaching method, and teaching effect as the first-level index to set the second-level evaluation index [15]. The quality of teachers focuses on their own professional accomplishment, teaching attitude focuses on teaching preparation and management, and teaching content emphasizes the accuracy, depth, and breadth of the content prepared by teachers. From the vertical angle of the teaching process, the preparation before class, the performance in class, the final examination, and other aspects are taken for the first-level indicators to set the second-level indicators of the teaching quality evaluation system content [16]. According to the situation, the basic process of teaching effect evaluation is shown in Figure 1.Figure 1
Flowchart of teaching evaluation process.


## 2.2. Study on Evaluation Methods of Academic Quality

With the development of computer technology and information technology, many scholars adopt the mathematical model of teaching quality evaluation system directly, including fuzzy comprehensive evaluation method, cluster analysis, analytic hierarchy process, and gray system. Zhou et al. applied a multilevel evaluation model of 2-type fuzzy sets to the evaluation of teaching quality performance [17]. Feng et al. applied analytic hierarchy process and neural network to the teaching quality evaluation model [18]. Zhao applied the TOPSIS method of multiattribute decision making in teaching quality evaluation [19]. As mentioned above, the advantages of the above method are that various evaluation factors are fully considered and expert experience and knowledge are reflected. The deficiency is that there is randomness and subjectivity in the evaluation process so that the nonlinear relationship between the evaluation indicators and the teaching effect is not taken into account; therefore, the evaluation results are very subjective, leading to the failure to truly reflect the teaching quality. Artificial intelligence technology represented by neural network genetic algorithm is a new subject in the field of information science and technology research. It refers to the structure and function of human brain and uses neural network to simulate human thinking so that it has self-learning and nonlinear transformation functions so as to overcome the defects of the above methods.In recent years, many researchers have adopted neural network method in teaching quality evaluation model evaluation. The application of neural network to the field of prediction can be traced back to two scholars Lapeds and Paber; they write programs to simulate neural network to learn data sample knowledge and predict nonlinear data [20]. Bai and Guo applied BP neural network algorithm to teaching quality evaluation [21]. The application of this method in teaching quality evaluation can overcome the subjectivity of expert evaluation, and the results are relatively correct. The applicability of this method is good. Ge et al. applied the improved BP neural network algorithm to the study of classroom teaching quality evaluation model, and the evaluation effect was improved [22]. However, BP neural network adopts gradient descent algorithm, so its learning speed is slow, and it is highly dependent on initial weights and thresholds, so it is easy to fall into local optimal. Therefore, the traditional neural network needs to be improved in algorithm and structure.


## 2.3. Research Status of Neural Network Weight Initialization Methods

The objects of deep convolutional neural network initialization are the convolution kernel weights and convolutional bias parameters in the network model. The purpose of the weight initialization of the convolution kernel is to give the initial values specified for all parameters in the convolution kernel. At present, deep convolutional neural network mainly uses random initialization method and unsupervised pretraining method to initialize weights.Random initialization method is a simple and fast initialization method, so most of the network model weight initialization methods are used. Commonly used random initialization methods mainly include small random number initialization [23], Xavier initialization [24], and MSRA initialization [25]. The small random number initialization method breaks the symmetry of parameters. This method initializes weight parameters into some small random numbers, making the parameters obey the Gaussian distribution with mean value of 0 and standard deviation; however, when the random number is too small, the gradient in the back propagation will be very small, and the problem of gradient dispersion will be caused when the network layer is deepened, and the speed of parameter convergence will also be reduced.At the same time, some scholars used unsupervised pretraining to initialize the weight of the filter and achieved good results [26, 27]. The main idea is to use Sparse Autoencoder (SAE) to initialize the weights of the convolution filters at the first layer in the network model and obtain the filter set that conforms to the statistical characteristics of the data set so as to solve the problem that the networks at the first layer cannot be fully trained. Sparse autoencoder [28] is an unsupervised learning model, whose main idea is to encode and decode data by using hidden layer, so that the output data is roughly equal to the input data. Through this unsupervised pretraining method and supervised training of deep convolutional neural network, the optimal parameters of the network will be obtained at the fastest speed, and its training speed will be improved.


## 3. Establishment of Evaluation Model

This section discusses the evaluation algorithm of English AV and oral teaching effect based on DL in detail. Firstly, it introduces an evaluation system of English listening and speaking teaching evaluation, then introduces the basic principle and concept of deep neural network, and finally introduces an improved deep convolution neural network and its weight initialization method proposed in this paper.


### 3.1. Establishment of English Audiovisual and Oral Evaluation System

The establishment of the teaching quality rating system is to make the teaching quality timely and effective evaluation, improve the enthusiasm of teachers, improve the teaching level, and reduce the burden of students. There are still some problems in the process of establishing and using the teaching quality evaluation system, such as the determination of evaluation indicators, the selection of evaluation subjects, the use of evaluation methods, the collection of evaluation data, etc. These problems have a great impact on the scientific and reasonable teaching quality evaluation. The establishment of teaching quality evaluation system should be the teaching mode and method that can promote individual development, respect commonness, pay attention to individuality, and reflect the common law of teaching.On the basis of summarizing the problems existing in the establishment of the current teaching quality evaluation system according to the research results, it is classified according to the unified standards of the school. Different evaluation indexes or different weights are used for evaluation, and artificial intelligence and other objective evaluation methods that can deal with nonlinear problems are used to improve the problems of single evaluation type, unreasonable proportion of evaluation indexes, and strong subjective factors of evaluation methods. The evaluation index system of English AV teaching is shown in Figure2.Figure 2
The evaluation index system of English AV teaching.The establishment of teaching quality evaluation system should be a teaching mode and method that can promote individual development, respect commonness, pay attention to individuality, and reflect the common law of teaching and the characteristics of different types of teaching. At this time, establishing a diversified, reasonable, and characteristic teaching quality evaluation standard system has a key role and significance, which can timely eliminate the idleness and waste of educational resources and realize the effective allocation of resources.


### 3.2. Overview of Convolutional Neural Networks

Artificial Neural Network (ANN) is a mathematical model, which simulates the behavior characteristics of animal neural networks by adjusting the parameter weights of interconnection between neural nodes and then processes data information. Convolutional neural network (CNN) is an efficient neural network that has attracted widespread attention in the field of artificial intelligence in recent years. Different from BP neural network, CNN model is a deep network structure, and its main innovations include local receptive field and weight sharing, aiming to reduce the training parameters of the network. In traditional ANN, full connection is adopted between neuron nodes of two adjacent layers, while in CNN, local connection is adopted between neuron nodes of two adjacent layers; that is, each neuron node is only convolved with part of neuron nodes of the previous layer, as shown in Figure3.Figure 3
Schematic diagram of full connection and local connection.Another innovation of the CNN model is the weight sharing method, which reduces the training parameters of the network to a certain extent. This method is also proposed based on the principle of biological neural network. In the convolution operation, each convolution filter (also known as the convolution kernel) is convolved with the whole receptive field region, where the weight parameters in each convolution filter are the same, so this method is called weight sharing. Each convolution filter is used to extract a feature of the image. The more types of convolution filters are, the more types of image features will be extracted. The weight sharing diagram is shown in Figure4.Figure 4
Schematic diagram of neural network weight sharing.In the traditional CNN structure, the first layer is the input layer, the middle layer is repeatedly composed of several “convolutional + pooling” structures, and the last layer is the output layer composed of the full connection layer and classifier. There are two operations of forward propagation: convolution and pooling. The convolution layer is a feature extraction layer, which is mainly used to extract features. Its main working principle is to convolute a convolution filter with each local receptive field of the previous feature and then activate the calculation result with the activation function to obtain a feature corresponding to the convolution filter, as shown in Figure5.Figure 5
Schematic diagram of convolution operation on features.On the left is 3 × 3 size convolution filter, the middle figure is 5 × 5 size of the original features, and the right figure is 3 × 3. For the characteristic image obtained after convolution-by-convolution filter, the convolution step size is set to 1. The convolution calculation formula is as follows:(1)zjl=∑i∈Rjxil−1∗Wijl+bjl,ajl=fzjl,where l is the number of layers, zjl represents the jth feature graph at l layer, Rj represents the set of input feature graph, ∗ represents 2D convolution operation, Wijl represents the convolution filter at l layer, and bjl is bias, which is usually set as a smaller parameter. ajl represents the characteristic graph after acting on the activation function, f. is the activation function, and ReLU (Rectified Linear Unit) activation function is generally adopted. The ReLU activation function is defined as follows:(2)fx=x,x≥0,0,x<0.Whenx≥0, the function takes the value of x and the output value remains unchanged from the input value. When x<0, the function value is 0, indicating that the input data is ignored. The pooling layer is the feature mapping layer, which can also be called the downsampling layer. The main function of this layer is to sample the features obtained from the previous layer (convolution layer). The specific operation of pooling is shown in Figure 6.Figure 6
Schematic diagram of pool operation.The size of pooling area is 2 × 2. Feature mapping is performed on the 2 × 2 area and a mapping value is calculated. Pooling operation can not only reduce the computational burden and the connection parameters between convolution layers, but also suppress the overfitting phenomenon and ultimately enhance the performance and robustness of the algorithm. The pooling calculation formula is as follows:(3)zjl=downpoolingzil−1,where “downpooling” is the pooling function, l is the number of layers, and zil−1 is the jth feature map in l layer. The most common pooling methods are maximum pooling and mean pooling. As the name implies, the maximum pooling method is to calculate the maximum element of each pixel in the pooling region, which ensures the translation invariance of extracted features. The mean pooling method is to calculate the mean value of pixels in each pooled region to make the features to be extracted more robust to small deformation. It can be convolved and pooled until the previous layer of the fully connected layer uses spatial pyramid pooling.At present, backpropagation is the most important and effective method used in the training of neural networks. Its essence is to use the chain derivative rule in calculus to correct the deviation of network parameters. The main idea is as follows: because there is an error between the output result of network and the actual result, the error is propagated back from the output layer to the hidden layer until the input layer. Meanwhile, network parameters are updated and adjusted according to the error. The training stops when the error is less than a certain value or the number of iterations reaches a preset value through the continuous alternation of forward- and backpropagation.


## 3.1. Establishment of English Audiovisual and Oral Evaluation System

The establishment of the teaching quality rating system is to make the teaching quality timely and effective evaluation, improve the enthusiasm of teachers, improve the teaching level, and reduce the burden of students. There are still some problems in the process of establishing and using the teaching quality evaluation system, such as the determination of evaluation indicators, the selection of evaluation subjects, the use of evaluation methods, the collection of evaluation data, etc. These problems have a great impact on the scientific and reasonable teaching quality evaluation. The establishment of teaching quality evaluation system should be the teaching mode and method that can promote individual development, respect commonness, pay attention to individuality, and reflect the common law of teaching.On the basis of summarizing the problems existing in the establishment of the current teaching quality evaluation system according to the research results, it is classified according to the unified standards of the school. Different evaluation indexes or different weights are used for evaluation, and artificial intelligence and other objective evaluation methods that can deal with nonlinear problems are used to improve the problems of single evaluation type, unreasonable proportion of evaluation indexes, and strong subjective factors of evaluation methods. The evaluation index system of English AV teaching is shown in Figure2.Figure 2
The evaluation index system of English AV teaching.The establishment of teaching quality evaluation system should be a teaching mode and method that can promote individual development, respect commonness, pay attention to individuality, and reflect the common law of teaching and the characteristics of different types of teaching. At this time, establishing a diversified, reasonable, and characteristic teaching quality evaluation standard system has a key role and significance, which can timely eliminate the idleness and waste of educational resources and realize the effective allocation of resources.


## 3.2. Overview of Convolutional Neural Networks

Artificial Neural Network (ANN) is a mathematical model, which simulates the behavior characteristics of animal neural networks by adjusting the parameter weights of interconnection between neural nodes and then processes data information. Convolutional neural network (CNN) is an efficient neural network that has attracted widespread attention in the field of artificial intelligence in recent years. Different from BP neural network, CNN model is a deep network structure, and its main innovations include local receptive field and weight sharing, aiming to reduce the training parameters of the network. In traditional ANN, full connection is adopted between neuron nodes of two adjacent layers, while in CNN, local connection is adopted between neuron nodes of two adjacent layers; that is, each neuron node is only convolved with part of neuron nodes of the previous layer, as shown in Figure3.Figure 3
Schematic diagram of full connection and local connection.Another innovation of the CNN model is the weight sharing method, which reduces the training parameters of the network to a certain extent. This method is also proposed based on the principle of biological neural network. In the convolution operation, each convolution filter (also known as the convolution kernel) is convolved with the whole receptive field region, where the weight parameters in each convolution filter are the same, so this method is called weight sharing. Each convolution filter is used to extract a feature of the image. The more types of convolution filters are, the more types of image features will be extracted. The weight sharing diagram is shown in Figure4.Figure 4
Schematic diagram of neural network weight sharing.In the traditional CNN structure, the first layer is the input layer, the middle layer is repeatedly composed of several “convolutional + pooling” structures, and the last layer is the output layer composed of the full connection layer and classifier. There are two operations of forward propagation: convolution and pooling. The convolution layer is a feature extraction layer, which is mainly used to extract features. Its main working principle is to convolute a convolution filter with each local receptive field of the previous feature and then activate the calculation result with the activation function to obtain a feature corresponding to the convolution filter, as shown in Figure5.Figure 5
Schematic diagram of convolution operation on features.On the left is 3 × 3 size convolution filter, the middle figure is 5 × 5 size of the original features, and the right figure is 3 × 3. For the characteristic image obtained after convolution-by-convolution filter, the convolution step size is set to 1. The convolution calculation formula is as follows:(1)zjl=∑i∈Rjxil−1∗Wijl+bjl,ajl=fzjl,where l is the number of layers, zjl represents the jth feature graph at l layer, Rj represents the set of input feature graph, ∗ represents 2D convolution operation, Wijl represents the convolution filter at l layer, and bjl is bias, which is usually set as a smaller parameter. ajl represents the characteristic graph after acting on the activation function, f. is the activation function, and ReLU (Rectified Linear Unit) activation function is generally adopted. The ReLU activation function is defined as follows:(2)fx=x,x≥0,0,x<0.Whenx≥0, the function takes the value of x and the output value remains unchanged from the input value. When x<0, the function value is 0, indicating that the input data is ignored. The pooling layer is the feature mapping layer, which can also be called the downsampling layer. The main function of this layer is to sample the features obtained from the previous layer (convolution layer). The specific operation of pooling is shown in Figure 6.Figure 6
Schematic diagram of pool operation.The size of pooling area is 2 × 2. Feature mapping is performed on the 2 × 2 area and a mapping value is calculated. Pooling operation can not only reduce the computational burden and the connection parameters between convolution layers, but also suppress the overfitting phenomenon and ultimately enhance the performance and robustness of the algorithm. The pooling calculation formula is as follows:(3)zjl=downpoolingzil−1,where “downpooling” is the pooling function, l is the number of layers, and zil−1 is the jth feature map in l layer. The most common pooling methods are maximum pooling and mean pooling. As the name implies, the maximum pooling method is to calculate the maximum element of each pixel in the pooling region, which ensures the translation invariance of extracted features. The mean pooling method is to calculate the mean value of pixels in each pooled region to make the features to be extracted more robust to small deformation. It can be convolved and pooled until the previous layer of the fully connected layer uses spatial pyramid pooling.At present, backpropagation is the most important and effective method used in the training of neural networks. Its essence is to use the chain derivative rule in calculus to correct the deviation of network parameters. The main idea is as follows: because there is an error between the output result of network and the actual result, the error is propagated back from the output layer to the hidden layer until the input layer. Meanwhile, network parameters are updated and adjusted according to the error. The training stops when the error is less than a certain value or the number of iterations reaches a preset value through the continuous alternation of forward- and backpropagation.


## 4. Improved Depth Neural Network

Autoencoder is a common algorithm in DL. It is a neural network used to reproduce input signals as much as possible. The main idea is that the hidden layer in the network is regarded as an encoder and decoder. The input data is encoded and decoded by the hidden layer to make the data of the output layer equal to that of the input layer as much as possible, and at the same time, the nodes of the hidden layer are forced to learn the compressed representation of the input data. After training the autoencoder, 50 neuron nodes were selected in the input layer and output layer of the autoencoder network model, respectively, and 20 neuron nodes were selected in the hidden layer for experiments. The 20 neurons in the hidden layer can read the important features of the 50 neurons nodes data in the input layer, while ensuring that the input data is as equal as possible to the output data. The automatic encoder loss function is defined as follows:(4)JW,b=1m∑i=1mJW,b;xi,yi+λ2∑l=1nt−1∑i=1sl∑j=1sl+1Wjil2=1m∑i=1m12hW,bxi−yi2+λ2∑l=1nl−1∑i=1sl∑j=1si+1Wjil2.The first term is the mean square deviation term. The second term is the weight attenuation term, which is used to reduce the weight to prevent overfitting. Sparse autoencoder is a special kind of autoencoder, which mainly adds sparse restriction on the basis of autoencoder to constrain the hidden layer, making most neuron nodes in suppressed state and a few neuron nodes in activated state, so as to achieve the purpose of sparse autoencoder.(5)JsparseW,b=JW,b+β∑j=1s2KLρρ^j.KL (Kullback–Leibler) distance is expressed as follows:(6)KLρρ^j=ρlogρ|ρ^j+1−ρlog1−ρ|1−ρ^j,where ρ is the sparsity parameter and usually takes a smaller number close to 0. We take the multiple nodes in the compute layer. ρ^j is the average output of hidden layer nodes. The expression is(7)ρ^j=1m∑i=1maj2X,where ajX is the activation value of input vector X to hidden layer unit jth. Salience refers to the use of computer algorithms to simulate the human visual system in order to obtain the salience of an image, the area of human interest. The brain quickly finds areas of interest to itself, ignoring other areas, and those areas that the brain quickly locates are salient areas. In this paper, the spectral residual algorithm is used to calculate the significant part of the feature. From the viewpoint of information theory, the information in features can be divided into two parts:(8)HImage=HInnovation+HPrior Knowledge,where HInnovation represents the prominent part in the feature and HPrior Knowledge represents the redundant information of the feature. By removing the redundant information from the original feature, the prominent part of the feature, that is, the salient region, can be obtained. SR is used to process the features to be detected, and the specific formula is as follows:(9)Af=RFIx,Pf=JFIx,Lf=logAf,where FIx represents the two-dimensional discrete Fourier transform, Af is the amplitude spectrum, Pf is the phase spectrum, and Lf is the log amplitude spectrum. Spectral residual is the difference between the log spectrum and its mean filtering; namely,(10)Rf=Lf−hnf∗Lf,where hnf is a convolution filter with n×n mean filter. Take the inverse two-dimensional discrete Fourier transform of spectrum residual and phase Pf; namely,(11)Sx=gx∗F−1expRf+Pf2.Thus, a new feature can be obtained to show whether each pixel in the original feature is significant, that is, the salient feature. The sparse automatic encoder randomly cuts small blocks from the original features as the input of the network model and obtains the weights through training, which is a sparse expression of the randomly cut small blocks. But at the same time, the selection of small blocks is also very important. If the recognition target accounts for a small proportion in the original features, the randomly cut small blocks are more likely to get the background. The convolution filter obtained by unsupervised pretraining of these small blocks makes little contribution to the convergence of the network.To solve the above problems, this chapter proposes to initialize the weights of the first layer convolution filter of convolution neural network by using saliency and unsupervised pretraining. The salient part is extracted from the original features by using the saliency model, and the salient part is intercepted and input into the sparse automatic encoder for unsupervised pretraining so as to obtain a convolution filter set more in line with the features. Through the weight initialization method based on the visual saliency model and unsupervised pretraining, a set of convolution filters with better initial weight and more consistent with the statistical characteristics of the image data set can be obtained. The specific pretraining framework is shown in Figure7.Figure 7
Pretraining framework based on spectral residual model and sparse autoencoder.


## 5. Experiments and Results

In this section, elaborate all the methods that are used to collect data for evaluating results and design the questionnaire. Then, apply the experimental methods to the data to verify the results.


### 5.1. Data Collection

Questionnaire survey is a common way to obtain information. In combination with the relevant theories and practices of teacher teaching quality evaluation, this study lists the influencing factors of the evaluation results, formulates the index system, designs the questionnaire, and issues the recycling questionnaire. The questionnaire designed in this paper adopts comprehensive forms, including selective questions and open questions. The first part is the choice, according to the index system of teaching quality evaluation in five primary index and 18 secondary indexes, design choice, from each indicator multiple rating select the most appropriate one, because the questionnaire is quantitative questionnaire, also need to choose the appropriate option, in the range of options for the corresponding score. From the perspective of data analysis, the content of this part is not included in the total score of the questionnaire. The subject object of the questionnaire survey is a college undergraduate, taking the class as a unit, using the break time, entrusted the monitor to issue. 100 questionnaires were distributed, 1050 were recovered, and 1000 valid questionnaires were screened. The division criteria of reliability coefficient are shown in Table1 below.Table 1
Reliability coefficient.
Reliability coefficientRepresentative meaning0.60–0.65Bad data0.65–0.70Minimum acceptable value0.70–0.80Pretty good0.80–0.90Very goodIt can be concluded from the above table that for a scale or questionnaire with reliability, its reliability coefficient should be better than 0.80, greater than 0.70 and less than 0.80, indicating that it is quite good and acceptable. If the reliability coefficient of the total quantity table is below 0.80, the questionnaire should be designed or questions should be added or deleted to improve the reliability of the questionnaire. The overall reliability of this questionnaire is carried out by using the “Cronbach” coefficient method. The results obtained after analysis by SPSS software are shown in the table below.According to the reliability coefficient standard in Table2, the overall reliability of this questionnaire is good, and actual investigation can be carried out to collect data. Validity test refers to the test of the validity of the whole questionnaire. The degree to which the final measurement results are consistent with the objective to be investigated is the level of questionnaire validity. The higher the consistency, the better the questionnaire effect. Content validity, criterion validity, and structure validity are three types of validity. Structural validity, also known as constructional validity, refers to whether the experimental structure conforms to the theory and the degree of consistency between the structure and the theory. The meanings of these different KMO coefficients are shown in Table 3.Table 2
“Cronbach” coefficient.
Primary indexCronbach alphaStandardized alphaBasic knowledge and skills0.8620.867Learning interest0.8420.846Innovative consciousness0.9030.899Problem-solving ability0.8760.871Amount of classroom information0.8890.882Table 3
KMO coefficient value.
KMO coefficient valueDegree of suitability for factor analysis0.90–1.00Very suitable0.80–0.89Suitable0.70–0.79Fair0.60–0.69Reluctantly0.50–0.59Not suitableLess than 0.50Completely unsuitableAccording to the theoretical knowledge, taking the questionnaire of teaching quality as an example, KMO and Bartlett spherical test were conducted on the data of teacher quality by SPSS software. It can be seen from the test results that the KMO coefficient is 0.896 between 0.8 and 0.89. The above results show that the questionnaire is effective.


### 5.2. Model Training and Testing

Through MATLAB 2013b, the simulation experiment of teaching quality evaluation optimized by deep neural network algorithm is realized, and the teaching quality evaluation results of group 901–1000th are predicted, and Figure8 is obtained. The comparison of the mean square error curve shows that the mean square error of the neural network model optimized by genetic algorithm decreases rapidly in the first 30 iterations, but slowly in the 31st to the 80th iterations. After 87 iterations, the mean square error converges at 8.6066E − 08.Figure 8
The mean square error of depth neural network is proposed in this paper.Comparing the mean square error curve, it is found that the iterative speed of the first generation of the depth neural network model with weight adaptive is faster, and the speed of the second to eighth generations is slower. The convergence occurs in the ninth iteration, which improves the convergence speed and convergence accuracy of the neural network to a certain extent. The convergence speed is 79.31% higher than that of the traditional depth neural network model, and the convergence accuracy is nearly doubled. It shows that the weight adaptive optimization can not only accelerate the convergence speed of the network, but also improve the prediction accuracy of the model.


### 5.3. Comparative Experiment

The traditional depth neural network model and the improved model are, respectively, applied to predict the evaluation results in the last 100 groups of test sample data. The prediction results are shown in Figure9. It can be seen that the prediction error of the traditional depth neural network model is too large, and the prediction results of the improved model are basically consistent with the actual results. It can be seen from the figure that the evaluation accuracy of the improved model is significantly higher than that of BP neural network model and traditional deep neural network model. Therefore, the optimization of deep neural network evaluation model based on weight optimization algorithm has better application value, and the model can make timely, effective, and scientific evaluation of teaching quality.Figure 9
Neural network model prediction results.The comparison results of average evaluation accuracy are shown in Table4. The average evaluation accuracy of BP neural network model for 100 groups of data is 84.26%, the average evaluation accuracy of traditional depth neural network model is 91.83%, while the average evaluation accuracy of depth neural network based on adaptive weight optimization is 98.35%, which is 13.99% and 6.42% higher than the two, respectively. It can be seen that the evaluation result of the depth neural network method based on adaptive weight optimization is better.Table 4
Comparison of model performance.
ModelAverage accuracy of evaluation (%)BP neural network84.26Traditional deep network91.83Our model98.35At the end, the summary of this section is firstly collecting the data and analyze it, verify the results by applying experiments. Results show that average accuracy of our model is better than both the BP neural network and traditional deep network. Our model is more efficient, effective and easily examine the ANN.


## 5.1. Data Collection

Questionnaire survey is a common way to obtain information. In combination with the relevant theories and practices of teacher teaching quality evaluation, this study lists the influencing factors of the evaluation results, formulates the index system, designs the questionnaire, and issues the recycling questionnaire. The questionnaire designed in this paper adopts comprehensive forms, including selective questions and open questions. The first part is the choice, according to the index system of teaching quality evaluation in five primary index and 18 secondary indexes, design choice, from each indicator multiple rating select the most appropriate one, because the questionnaire is quantitative questionnaire, also need to choose the appropriate option, in the range of options for the corresponding score. From the perspective of data analysis, the content of this part is not included in the total score of the questionnaire. The subject object of the questionnaire survey is a college undergraduate, taking the class as a unit, using the break time, entrusted the monitor to issue. 100 questionnaires were distributed, 1050 were recovered, and 1000 valid questionnaires were screened. The division criteria of reliability coefficient are shown in Table1 below.Table 1
Reliability coefficient.
Reliability coefficientRepresentative meaning0.60–0.65Bad data0.65–0.70Minimum acceptable value0.70–0.80Pretty good0.80–0.90Very goodIt can be concluded from the above table that for a scale or questionnaire with reliability, its reliability coefficient should be better than 0.80, greater than 0.70 and less than 0.80, indicating that it is quite good and acceptable. If the reliability coefficient of the total quantity table is below 0.80, the questionnaire should be designed or questions should be added or deleted to improve the reliability of the questionnaire. The overall reliability of this questionnaire is carried out by using the “Cronbach” coefficient method. The results obtained after analysis by SPSS software are shown in the table below.According to the reliability coefficient standard in Table2, the overall reliability of this questionnaire is good, and actual investigation can be carried out to collect data. Validity test refers to the test of the validity of the whole questionnaire. The degree to which the final measurement results are consistent with the objective to be investigated is the level of questionnaire validity. The higher the consistency, the better the questionnaire effect. Content validity, criterion validity, and structure validity are three types of validity. Structural validity, also known as constructional validity, refers to whether the experimental structure conforms to the theory and the degree of consistency between the structure and the theory. The meanings of these different KMO coefficients are shown in Table 3.Table 2
“Cronbach” coefficient.
Primary indexCronbach alphaStandardized alphaBasic knowledge and skills0.8620.867Learning interest0.8420.846Innovative consciousness0.9030.899Problem-solving ability0.8760.871Amount of classroom information0.8890.882Table 3
KMO coefficient value.
KMO coefficient valueDegree of suitability for factor analysis0.90–1.00Very suitable0.80–0.89Suitable0.70–0.79Fair0.60–0.69Reluctantly0.50–0.59Not suitableLess than 0.50Completely unsuitableAccording to the theoretical knowledge, taking the questionnaire of teaching quality as an example, KMO and Bartlett spherical test were conducted on the data of teacher quality by SPSS software. It can be seen from the test results that the KMO coefficient is 0.896 between 0.8 and 0.89. The above results show that the questionnaire is effective.


## 5.2. Model Training and Testing

Through MATLAB 2013b, the simulation experiment of teaching quality evaluation optimized by deep neural network algorithm is realized, and the teaching quality evaluation results of group 901–1000th are predicted, and Figure8 is obtained. The comparison of the mean square error curve shows that the mean square error of the neural network model optimized by genetic algorithm decreases rapidly in the first 30 iterations, but slowly in the 31st to the 80th iterations. After 87 iterations, the mean square error converges at 8.6066E − 08.Figure 8
The mean square error of depth neural network is proposed in this paper.Comparing the mean square error curve, it is found that the iterative speed of the first generation of the depth neural network model with weight adaptive is faster, and the speed of the second to eighth generations is slower. The convergence occurs in the ninth iteration, which improves the convergence speed and convergence accuracy of the neural network to a certain extent. The convergence speed is 79.31% higher than that of the traditional depth neural network model, and the convergence accuracy is nearly doubled. It shows that the weight adaptive optimization can not only accelerate the convergence speed of the network, but also improve the prediction accuracy of the model.


## 5.3. Comparative Experiment

The traditional depth neural network model and the improved model are, respectively, applied to predict the evaluation results in the last 100 groups of test sample data. The prediction results are shown in Figure9. It can be seen that the prediction error of the traditional depth neural network model is too large, and the prediction results of the improved model are basically consistent with the actual results. It can be seen from the figure that the evaluation accuracy of the improved model is significantly higher than that of BP neural network model and traditional deep neural network model. Therefore, the optimization of deep neural network evaluation model based on weight optimization algorithm has better application value, and the model can make timely, effective, and scientific evaluation of teaching quality.Figure 9
Neural network model prediction results.The comparison results of average evaluation accuracy are shown in Table4. The average evaluation accuracy of BP neural network model for 100 groups of data is 84.26%, the average evaluation accuracy of traditional depth neural network model is 91.83%, while the average evaluation accuracy of depth neural network based on adaptive weight optimization is 98.35%, which is 13.99% and 6.42% higher than the two, respectively. It can be seen that the evaluation result of the depth neural network method based on adaptive weight optimization is better.Table 4
Comparison of model performance.
ModelAverage accuracy of evaluation (%)BP neural network84.26Traditional deep network91.83Our model98.35At the end, the summary of this section is firstly collecting the data and analyze it, verify the results by applying experiments. Results show that average accuracy of our model is better than both the BP neural network and traditional deep network. Our model is more efficient, effective and easily examine the ANN.


## 6. Conclusion

English AV teaching quality evaluation is a very complex and fuzzy nonlinear process. In this process, many factors and variables are involved. Therefore, the establishment of mathematical model is more complex, and the traditional teaching quality evaluation methods cannot be fully competent. Only by applying the correct intelligent optimization algorithm to a reasonable teaching quality evaluation system can we get a scientific and reliable teaching quality evaluation model. Compared with other algorithms, deep neural network is suitable for solving problems with complex internal mechanism, and it has self-learning ability and generalization ability. It can express more complex nonlinear mapping, but it is easy to fall into local optimization and slow convergence speed. To solve the above problems, this paper proposes an improved deep neural network to optimize the connection weight of neural network and establish the evaluation model of teaching quality in colleges and universities. The experimental results show that this method can provide a more feasible scheme for the evaluation of teaching quality. Although this paper analyzes the problems of the existing English AV and oral teaching quality evaluation system, makes improvements, establishes a deep neural network teaching quality evaluation model based on adaptive weight, and obtains some results, there are still some problems and in future; it can be further studied. Based on the teaching quality evaluation model in this paper, an evaluation system or software based on this model can be developed for the real-time evaluation of undergraduate teachers’ teaching quality, which is faster and more reliable.



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