# Teaching Method of Japanese Professional Writing Course Based on Big Data Fusion

Authors: Dandan Zhao; Guocai Yin
Journal: Security and Communication Networks
 (2022)

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## Abstract

In recent years, the number of Japanese learners has increased year by year. When Japanese is learned as a language, knowledge of the language itself is indispensable. This article aims to explore the teaching methods of Japanese professional writing courses based on the integration of big data. This article first introduces big data fusion and introduces the definition and model of data fusion. Generally, there are two types of data fusion models: distributed and centralized. Then, I analyzed the artificial neural network algorithm, which is a computational model that imitates the animal brain, and then researched the context-based teaching of Japanese professional courses and analyzed the context-based teaching of Japanese professional writing. The results of the context-based Japanese course teaching research show that the average Japanese writing score of the experimental class is 72.7833, the control class score is 66.3333, and the composition fluency scores are 156.27 and 119.73. It can be concluded that the composition performance and composition fluency of students in the experimental class are higher than those in the control class, so educators should let students strengthen vocabulary spelling and memory skills in situational teaching.


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## Body

## 1. Introduction

The general research focus of Japanese majors in curriculum teaching is mainly on the current research status of curriculum theory and related issues exposed in the course implementation process. For a specific course teaching method, such as contextualized teaching, such research has been carried out relatively little. At the same time, its effect only remained at the level of educators’ reflection and summary, without verifying its effect and feasibility. On the one hand, it is limited by the mobility of students and individual differences, which cannot be satisfied with a set of curriculum teaching methods. On the other hand, there are certain difficulties in the evaluation of specific curriculum teaching methods. The research focus of this article is precisely the research on the implementation effect of the characteristic teaching method in the characteristic Japanese curriculum. This research is dynamic research. In the early stage, many settings are continuously optimized and reflected as the experiment deepens.The current theoretical research on Japanese contextualized teaching has grown rapidly in recent years, but it has not shown changes in educational experiments in the field of educational practice, and there is no statistical analysis of data. The effect of contextualized teaching remains in education. This educational experiment method will use SPSS data analysis software to conduct thorough experimental data analysis to explore the dynamic changes of students’ Japanese ability, to draw conclusions in statistical significance, and to make quantitative analysis and research.The innovations of this article are embodied in the following: First, it introduces big data fusion. Data fusion is generally divided into distributed and centralized models. It analyzes the artificial neural network algorithm; then, it conducts research and analysis on the context-based Japanese professional curriculum teaching and Japanese professional writing.


## 2. Related Work

According to the research progress at home and abroad, different scholars also have a certain degree of cooperation in the field of big data integration. Yang et al. aim to solve three basic problems closely related to the distributed dimensionality reduction of big data, namely, the fusion of big data, dimensionality reduction algorithms, and the construction of distributed computing platforms. A high-order singular-value decomposition algorithm based on Lanczos is proposed to reduce the dimensionality of the unified model. Experimental results show that the proposed overall method is effective for distributed dimensionality reduction of big data [1]. Kang proposed a financial risk assessment model based on big data. This method uses quantitative analysis methods to analyze the explanatory variable model and the controlled variable model of financial risk assessment. The simulation results show that this method can provide high financial risk assessment accuracy; it has a strong adaptive evaluation ability for risk coefficients, and it has good application value in the prevention and control of financial system risk factors [2]. Zhai et al. proposed a multiattention fusion modeling (multi-AFM) to address the problems of low efficiency and heavy workload of university curriculum evaluation methods. Through the gating unit control, the global attention and the local attention are combined to generate a reasonable context representation and achieve an improved classification result. The experimental results show that the multi-AFM model performs better than existing methods in the application of education and other fields [3]. Papalexakis et al. introduced the latest algorithm progress in extending tensor decomposition to today’s big data, outlined the existing system, and summarized the key ideas behind it. Finally, a series of challenges and outstanding issues are summarized, and exciting future research directions are outlined [4]. Shi et al. proposed an integrated data preprocessing framework based on Apache Spark to improve the prediction accuracy of missing data points and the classification accuracy of noisy data and to meet the needs of big data. It mainly combines missing data prediction, data fusion, data cleaning, and classification of fault types [5]. Liu et al. proposed a novel dual-end machine learning model to improve the accuracy and real-time prediction of heterogeneous spectrum states. In addition, a large-spectrum data clustering mechanism is adopted to facilitate data matching and heterogeneous spectrum prediction. Finally, the comprehensive spectrum state is obtained by fusion of heterogeneous spectrum data [6]. Dong et al. proposed a framework for calculating normalized records. It includes a set of normalization methods, ranging from simple methods that use only the records themselves to collect information to complex strategies, globally mining the collection of duplicate records before selecting values for the properties of the normalized records, and extensive empirical research on all the proposed methods [7]. However, these scholars did not analyze and study the teaching methods of Japanese professional writing courses based on the integration of big data, but only unilaterally explored their significance.


## 3. Teaching of Japanese Professional Writing Course Based on Big Data Fusion

### 3.1. Big Data Fusion

#### 3.1.1. Definition of Data Fusion

Data fusion technology originated in the military industry. In the modern warfare system, only relying on sensors to extract information can no longer meet the combat needs of modern battlefields [8]. To achieve good combat effectiveness, it is necessary to process various sensor information and data including various active and passive detection methods, such as infrared, microwave, laser, submillimeter wave, and electronic intelligence technology. It is precisely with this development trend that big data fusion technology was bred from modern technology and began to develop rapidly [9].Data fusion technology refers to a data processing method based on a special technical problem such as the use of multiple types of sensor systems. Using embedded technology and computer, the multisensor signals obtained according to the time series are intelligently classified, processed, and optimized under specific rules so as to obtain the judgment and prediction tasks we need. The basis of data fusion is various sensors, and the main processing objects of data fusion are various types of perception information. Therefore, the basis of data fusion is collaborative optimization and comprehensive management. However, the research objects of data fusion are broad and varied, and a unified concept is still lacking. Although the introduction of data fusion technology is easy to understand, the actual data fusion technology is very complicated from scheme design to implementation. From the initial modeling process to the actual data fusion, to the transformation through the data processing of various sensors and obtaining the expected results, there will be many difficulties and challenges in the whole process. Due to the continuous advancement of computer, communication technology, and signal processing technology, many problems in big data fusion have begun to be gradually overcome [10]. At present, some real-time fusion systems have been put into real-world applications.


#### 3.1.2. Data Fusion Model

A large number of sensing signals in wireless sensor networks are transmitted from multiple nodes to aggregation nodes. According to the form of signal transmission and the processing level of network nodes, there are usually two types of data fusion modes: distributed and centralized. Figure1 shows the network structure of the wireless sensor network.Figure 1
Network structure of wireless sensor network.The distributed structure is also called the intranetwork data fusion structure, as shown in Figure2 [11]. When the source node forwards data to the intermediate node, the intermediate node will check the content of the data packet and make appropriate data fusion, and then the intermediate node will forward it to the sink node, and then the sink node will complete the data processing. These structures can improve the data acquisition efficiency of the entire network to a certain extent, thereby reducing the amount of data, reducing power consumption, increasing the channel utilization rate, and extending the life of the entire network.Figure 2
Data fusion model.The advantage of the centralized architecture is that the source node can directly transmit relevant data to the aggregation node and then cooperate with the aggregation node to achieve data fusion [12]. Although this architecture can reduce signal loss very well, it is because the nodes of the wireless sensor network are very tightly distributed. Many kinds of source nodes will be generated to characterize the data of the same event so that many redundant signals of similar size are generated, and the transmission of redundant signals also requires a lot of network power resources [13]. Therefore, the centralized architecture is not a good choice for wireless sensor networks with high energy-saving indicators.


### 3.2. Artificial Neural Networks

An artificial neural network, or neural network for short, is a computational model that mimics the animal brain. Compared with the most primitive methods of identifying objects, neural networks have the advantages of autonomous learning, computational parallelism, nonlinear representation of features, and good performance. At present, neural network has a very broad application prospect in image processing, text information processing, speech data processing, and other fields and has become an important processing model in current information data processing.


#### 3.2.1. Single Neuron

Neurons are the basic building blocks of artificial neural networks. The network with only one neuron is the simplest artificial neural network, and its specific structure is shown in Figure3.Figure 3
Single neuron.For a neural network containing only one neuron, it can be expressed as(1)R=g∑n=1kAn∗Tn+cn.Among them,A1,A2,⋯,Ak represent the input of the neuron, T1,T2,⋯,Tk are the weight parameter corresponding to the neuron input, cn is the bias parameter corresponding to the neuron, g· represents an activation function, and R represents the output of the neural network [14].


#### 3.2.2. Neural Network Model

A simple neural network structure is shown in Figure4. Using kh to represent the number of network layers, then kh=3, and Rh to represent the number of nodes in the hth layer, where layer H1 represents the input layer, layer H2 represents the hidden layer, and layer H3 represents the output layer [15]. Tnmh represents the connection weight parameter between the mth node of the hth layer and the mth node of the h + 1th layer, and cnh is the bias term of the nth node of the h + 1th layer. dnh is the activation value (output value) of the nth node of the hth layer, and rt,ca represents the output of the neural network.Figure 4
Simple neural network.The calculation process of a simple neural network is as follows:(2)d12=gT111a1+T121a2+T131a3+c11,d22=gT211a1+T221a2+T231a3+c21,d32=gT311a1+T321a2+T331a3+c31,rt,ca=d13=gT112a12+T122a22+T132a22+c12.In order to facilitate the calculation of the calculation formula,vnh is used to represent the weighted sum of the inputs of each node of the h − 1 layer to the nth node of the h layer, when h = 2.(3)vn2=∑m=1kTnm1am+cn1,dnh=gvnh.Then, the equation can be expressed as follows:(4)v2=t1a+c1,d2=gv2,v3=t2d2+c2,rt,ca=d2=gv3.When the activation valuedh of the hth layer is given, the activation value dh+1 of the h + 1th layer can be calculated according to the following formula:(5)vh+1=thdh+ch,dh+1=gvh+1.Assuming that the weight parametert and the bias parameter c are known, the activation value dnh of each node in the neural network can be obtained through the continuous iterative calculation of the above formula. The forward propagation process of the neural network is the calculation process of the above neural network [16].


#### 3.2.3. Back Propagation Algorithm

The training process of artificial neural network is a complex autonomous learning process [17]. The forward propagation process and the back propagation process constitute the training process of the artificial neural network [18]. The forward propagation process is to calculate the input image data, and the back propagation process mainly compares the output sample value calculated in the forward transfer process with the actual value of the sample. Then, the residual error is obtained by statistics and then propagated forward from the network, and the residual error of each node in the hidden layer of the neural network is counted. Then, the weight parameter t and bias parameter c of the neural network are continuously adjusted until the error value between the output predicted sample value of the model and the actual value is the smallest.Given a sample set (a, b), a total of i groups, namely, a1,b1,a2,b2,⋯,ai,bi , for a single sample ai,bi , the cost function is defined as(6)y=t,c;a,b=12rt,cai−bi2.The formula is a variance cost function. The overall cost function calculation expression is defined as(7)yt,c=1s∑n=1syt,c,ai,bi+φ2∑h=1kh−1∑n=1Rh∑m=1Rh+1Tnmh2.In the above formula,s represents the number of samples in the training sample set; the first term represents the mean square error term of the cost function t,c , and the second term represents a weight attenuation term of the cost function. The function of the weight attenuation parameter is to adjust the ratio of the two items before and after in the formula in the cost function so as to control the importance of each item in the cost function and prevent the network model from overfitting [19].The main goal of the backpropagation process is to minimize the value of the cost function of the calculated network model, which can be achieved by finding the optimal weightt and bias c in the process of continuously updating the network weight parameters. In order to realize the calculation of the neural network, the ownership value parameters can be initialized to random values close to 0 at first, but not all the ownership value parameters are set to 0. When the network parameters are initialized, if each weight parameter is set to the same value, all parameters and output values in the same layer in the network will be the same so that the model cannot achieve convergence through training. After initializing the parameters in the network model, optimization methods such as gradient descent are usually used to minimize the cost function of the network model [20].In each iteration process of network model training, the weight parametert and bias parameter c are fine-tuned using gradient descent method to obtain the network model with the minimum cost function. The process calculation formula is as follows:(8)Tnmh:=Tnmh−δ∂yt,c∂Tnmh,cnh:=cnh−δ∂yt,c∂cnh,whereδ represents the learning rate.The calculation formula of the partial derivative of the total cost functionyt,c of the entire sample data set is(9)∂∂Tnmhyt,c=1s∑n=1s∂∂Tnmhyt,c,ai,bi+φTnmh,∂∂cnhyt,c=1s∑n=1s∂∂cnhyt,c,ai,bi.The calculation of the above formula needs to first calculate the partial derivatives∂/∂Tnmhyt,c,a,b and ∂/∂cnhyt,c,a,b of the cost function y(t, c, a, b) corresponding to a single training sample (a, b), and the BP algorithm is very effective for calculating the partial derivative of the cost function [2]. The entire process of the back propagation algorithm is as follows:(1)
A sample set (a, b), a total of i groups, namely, a1,b1,a2,b2,⋯,ai,bi is given. Use the formula of the forward propagation process to calculate the activation value dh of each layer of h1,h2,⋯,hk , including the value of the output rt,ca of the neural network.(2)
For each output unitn of the last output layer of the neural network, the residual expression is(10)βnkh=∂∂vnkh12b−rt,ca2=−bn−dnkh∗gvnkh.(3)
Calculating the residuals of each layerhkh−1,hkh−2,⋯,h2 of the hidden layer in the neural network, the calculation formula is as follows:(11)βnh=∑y=1Sh+1Tynhβnh+1gvnh.(4)
The partial derivative numerical expression for calculating the cost function of a single sample is as follows:(12)∂∂Tnmhyt,c,a,b=dyhβnh+1,∂∂cnhyt,c,a,b=βnh+1.The partial derivative value of the cost function of a single sample is substituted into the partial derivative calculation formula of the overall sample cost function so that the weight parametert and the bias parameter c can be updated [22]. Figure 5 shows the training process of the neural network.Figure 5
Neural network training process.


## 3.1. Big Data Fusion

### 3.1.1. Definition of Data Fusion

Data fusion technology originated in the military industry. In the modern warfare system, only relying on sensors to extract information can no longer meet the combat needs of modern battlefields [8]. To achieve good combat effectiveness, it is necessary to process various sensor information and data including various active and passive detection methods, such as infrared, microwave, laser, submillimeter wave, and electronic intelligence technology. It is precisely with this development trend that big data fusion technology was bred from modern technology and began to develop rapidly [9].Data fusion technology refers to a data processing method based on a special technical problem such as the use of multiple types of sensor systems. Using embedded technology and computer, the multisensor signals obtained according to the time series are intelligently classified, processed, and optimized under specific rules so as to obtain the judgment and prediction tasks we need. The basis of data fusion is various sensors, and the main processing objects of data fusion are various types of perception information. Therefore, the basis of data fusion is collaborative optimization and comprehensive management. However, the research objects of data fusion are broad and varied, and a unified concept is still lacking. Although the introduction of data fusion technology is easy to understand, the actual data fusion technology is very complicated from scheme design to implementation. From the initial modeling process to the actual data fusion, to the transformation through the data processing of various sensors and obtaining the expected results, there will be many difficulties and challenges in the whole process. Due to the continuous advancement of computer, communication technology, and signal processing technology, many problems in big data fusion have begun to be gradually overcome [10]. At present, some real-time fusion systems have been put into real-world applications.


### 3.1.2. Data Fusion Model

A large number of sensing signals in wireless sensor networks are transmitted from multiple nodes to aggregation nodes. According to the form of signal transmission and the processing level of network nodes, there are usually two types of data fusion modes: distributed and centralized. Figure1 shows the network structure of the wireless sensor network.Figure 1
Network structure of wireless sensor network.The distributed structure is also called the intranetwork data fusion structure, as shown in Figure2 [11]. When the source node forwards data to the intermediate node, the intermediate node will check the content of the data packet and make appropriate data fusion, and then the intermediate node will forward it to the sink node, and then the sink node will complete the data processing. These structures can improve the data acquisition efficiency of the entire network to a certain extent, thereby reducing the amount of data, reducing power consumption, increasing the channel utilization rate, and extending the life of the entire network.Figure 2
Data fusion model.The advantage of the centralized architecture is that the source node can directly transmit relevant data to the aggregation node and then cooperate with the aggregation node to achieve data fusion [12]. Although this architecture can reduce signal loss very well, it is because the nodes of the wireless sensor network are very tightly distributed. Many kinds of source nodes will be generated to characterize the data of the same event so that many redundant signals of similar size are generated, and the transmission of redundant signals also requires a lot of network power resources [13]. Therefore, the centralized architecture is not a good choice for wireless sensor networks with high energy-saving indicators.


## 3.1.1. Definition of Data Fusion

Data fusion technology originated in the military industry. In the modern warfare system, only relying on sensors to extract information can no longer meet the combat needs of modern battlefields [8]. To achieve good combat effectiveness, it is necessary to process various sensor information and data including various active and passive detection methods, such as infrared, microwave, laser, submillimeter wave, and electronic intelligence technology. It is precisely with this development trend that big data fusion technology was bred from modern technology and began to develop rapidly [9].Data fusion technology refers to a data processing method based on a special technical problem such as the use of multiple types of sensor systems. Using embedded technology and computer, the multisensor signals obtained according to the time series are intelligently classified, processed, and optimized under specific rules so as to obtain the judgment and prediction tasks we need. The basis of data fusion is various sensors, and the main processing objects of data fusion are various types of perception information. Therefore, the basis of data fusion is collaborative optimization and comprehensive management. However, the research objects of data fusion are broad and varied, and a unified concept is still lacking. Although the introduction of data fusion technology is easy to understand, the actual data fusion technology is very complicated from scheme design to implementation. From the initial modeling process to the actual data fusion, to the transformation through the data processing of various sensors and obtaining the expected results, there will be many difficulties and challenges in the whole process. Due to the continuous advancement of computer, communication technology, and signal processing technology, many problems in big data fusion have begun to be gradually overcome [10]. At present, some real-time fusion systems have been put into real-world applications.


## 3.1.2. Data Fusion Model

A large number of sensing signals in wireless sensor networks are transmitted from multiple nodes to aggregation nodes. According to the form of signal transmission and the processing level of network nodes, there are usually two types of data fusion modes: distributed and centralized. Figure1 shows the network structure of the wireless sensor network.Figure 1
Network structure of wireless sensor network.The distributed structure is also called the intranetwork data fusion structure, as shown in Figure2 [11]. When the source node forwards data to the intermediate node, the intermediate node will check the content of the data packet and make appropriate data fusion, and then the intermediate node will forward it to the sink node, and then the sink node will complete the data processing. These structures can improve the data acquisition efficiency of the entire network to a certain extent, thereby reducing the amount of data, reducing power consumption, increasing the channel utilization rate, and extending the life of the entire network.Figure 2
Data fusion model.The advantage of the centralized architecture is that the source node can directly transmit relevant data to the aggregation node and then cooperate with the aggregation node to achieve data fusion [12]. Although this architecture can reduce signal loss very well, it is because the nodes of the wireless sensor network are very tightly distributed. Many kinds of source nodes will be generated to characterize the data of the same event so that many redundant signals of similar size are generated, and the transmission of redundant signals also requires a lot of network power resources [13]. Therefore, the centralized architecture is not a good choice for wireless sensor networks with high energy-saving indicators.


## 3.2. Artificial Neural Networks

An artificial neural network, or neural network for short, is a computational model that mimics the animal brain. Compared with the most primitive methods of identifying objects, neural networks have the advantages of autonomous learning, computational parallelism, nonlinear representation of features, and good performance. At present, neural network has a very broad application prospect in image processing, text information processing, speech data processing, and other fields and has become an important processing model in current information data processing.


### 3.2.1. Single Neuron

Neurons are the basic building blocks of artificial neural networks. The network with only one neuron is the simplest artificial neural network, and its specific structure is shown in Figure3.Figure 3
Single neuron.For a neural network containing only one neuron, it can be expressed as(1)R=g∑n=1kAn∗Tn+cn.Among them,A1,A2,⋯,Ak represent the input of the neuron, T1,T2,⋯,Tk are the weight parameter corresponding to the neuron input, cn is the bias parameter corresponding to the neuron, g· represents an activation function, and R represents the output of the neural network [14].


### 3.2.2. Neural Network Model

A simple neural network structure is shown in Figure4. Using kh to represent the number of network layers, then kh=3, and Rh to represent the number of nodes in the hth layer, where layer H1 represents the input layer, layer H2 represents the hidden layer, and layer H3 represents the output layer [15]. Tnmh represents the connection weight parameter between the mth node of the hth layer and the mth node of the h + 1th layer, and cnh is the bias term of the nth node of the h + 1th layer. dnh is the activation value (output value) of the nth node of the hth layer, and rt,ca represents the output of the neural network.Figure 4
Simple neural network.The calculation process of a simple neural network is as follows:(2)d12=gT111a1+T121a2+T131a3+c11,d22=gT211a1+T221a2+T231a3+c21,d32=gT311a1+T321a2+T331a3+c31,rt,ca=d13=gT112a12+T122a22+T132a22+c12.In order to facilitate the calculation of the calculation formula,vnh is used to represent the weighted sum of the inputs of each node of the h − 1 layer to the nth node of the h layer, when h = 2.(3)vn2=∑m=1kTnm1am+cn1,dnh=gvnh.Then, the equation can be expressed as follows:(4)v2=t1a+c1,d2=gv2,v3=t2d2+c2,rt,ca=d2=gv3.When the activation valuedh of the hth layer is given, the activation value dh+1 of the h + 1th layer can be calculated according to the following formula:(5)vh+1=thdh+ch,dh+1=gvh+1.Assuming that the weight parametert and the bias parameter c are known, the activation value dnh of each node in the neural network can be obtained through the continuous iterative calculation of the above formula. The forward propagation process of the neural network is the calculation process of the above neural network [16].


### 3.2.3. Back Propagation Algorithm

The training process of artificial neural network is a complex autonomous learning process [17]. The forward propagation process and the back propagation process constitute the training process of the artificial neural network [18]. The forward propagation process is to calculate the input image data, and the back propagation process mainly compares the output sample value calculated in the forward transfer process with the actual value of the sample. Then, the residual error is obtained by statistics and then propagated forward from the network, and the residual error of each node in the hidden layer of the neural network is counted. Then, the weight parameter t and bias parameter c of the neural network are continuously adjusted until the error value between the output predicted sample value of the model and the actual value is the smallest.Given a sample set (a, b), a total of i groups, namely, a1,b1,a2,b2,⋯,ai,bi , for a single sample ai,bi , the cost function is defined as(6)y=t,c;a,b=12rt,cai−bi2.The formula is a variance cost function. The overall cost function calculation expression is defined as(7)yt,c=1s∑n=1syt,c,ai,bi+φ2∑h=1kh−1∑n=1Rh∑m=1Rh+1Tnmh2.In the above formula,s represents the number of samples in the training sample set; the first term represents the mean square error term of the cost function t,c , and the second term represents a weight attenuation term of the cost function. The function of the weight attenuation parameter is to adjust the ratio of the two items before and after in the formula in the cost function so as to control the importance of each item in the cost function and prevent the network model from overfitting [19].The main goal of the backpropagation process is to minimize the value of the cost function of the calculated network model, which can be achieved by finding the optimal weightt and bias c in the process of continuously updating the network weight parameters. In order to realize the calculation of the neural network, the ownership value parameters can be initialized to random values close to 0 at first, but not all the ownership value parameters are set to 0. When the network parameters are initialized, if each weight parameter is set to the same value, all parameters and output values in the same layer in the network will be the same so that the model cannot achieve convergence through training. After initializing the parameters in the network model, optimization methods such as gradient descent are usually used to minimize the cost function of the network model [20].In each iteration process of network model training, the weight parametert and bias parameter c are fine-tuned using gradient descent method to obtain the network model with the minimum cost function. The process calculation formula is as follows:(8)Tnmh:=Tnmh−δ∂yt,c∂Tnmh,cnh:=cnh−δ∂yt,c∂cnh,whereδ represents the learning rate.The calculation formula of the partial derivative of the total cost functionyt,c of the entire sample data set is(9)∂∂Tnmhyt,c=1s∑n=1s∂∂Tnmhyt,c,ai,bi+φTnmh,∂∂cnhyt,c=1s∑n=1s∂∂cnhyt,c,ai,bi.The calculation of the above formula needs to first calculate the partial derivatives∂/∂Tnmhyt,c,a,b and ∂/∂cnhyt,c,a,b of the cost function y(t, c, a, b) corresponding to a single training sample (a, b), and the BP algorithm is very effective for calculating the partial derivative of the cost function [2]. The entire process of the back propagation algorithm is as follows:(1)
A sample set (a, b), a total of i groups, namely, a1,b1,a2,b2,⋯,ai,bi is given. Use the formula of the forward propagation process to calculate the activation value dh of each layer of h1,h2,⋯,hk , including the value of the output rt,ca of the neural network.(2)
For each output unitn of the last output layer of the neural network, the residual expression is(10)βnkh=∂∂vnkh12b−rt,ca2=−bn−dnkh∗gvnkh.(3)
Calculating the residuals of each layerhkh−1,hkh−2,⋯,h2 of the hidden layer in the neural network, the calculation formula is as follows:(11)βnh=∑y=1Sh+1Tynhβnh+1gvnh.(4)
The partial derivative numerical expression for calculating the cost function of a single sample is as follows:(12)∂∂Tnmhyt,c,a,b=dyhβnh+1,∂∂cnhyt,c,a,b=βnh+1.The partial derivative value of the cost function of a single sample is substituted into the partial derivative calculation formula of the overall sample cost function so that the weight parametert and the bias parameter c can be updated [22]. Figure 5 shows the training process of the neural network.Figure 5
Neural network training process.


## 3.2.1. Single Neuron

Neurons are the basic building blocks of artificial neural networks. The network with only one neuron is the simplest artificial neural network, and its specific structure is shown in Figure3.Figure 3
Single neuron.For a neural network containing only one neuron, it can be expressed as(1)R=g∑n=1kAn∗Tn+cn.Among them,A1,A2,⋯,Ak represent the input of the neuron, T1,T2,⋯,Tk are the weight parameter corresponding to the neuron input, cn is the bias parameter corresponding to the neuron, g· represents an activation function, and R represents the output of the neural network [14].


## 3.2.2. Neural Network Model

A simple neural network structure is shown in Figure4. Using kh to represent the number of network layers, then kh=3, and Rh to represent the number of nodes in the hth layer, where layer H1 represents the input layer, layer H2 represents the hidden layer, and layer H3 represents the output layer [15]. Tnmh represents the connection weight parameter between the mth node of the hth layer and the mth node of the h + 1th layer, and cnh is the bias term of the nth node of the h + 1th layer. dnh is the activation value (output value) of the nth node of the hth layer, and rt,ca represents the output of the neural network.Figure 4
Simple neural network.The calculation process of a simple neural network is as follows:(2)d12=gT111a1+T121a2+T131a3+c11,d22=gT211a1+T221a2+T231a3+c21,d32=gT311a1+T321a2+T331a3+c31,rt,ca=d13=gT112a12+T122a22+T132a22+c12.In order to facilitate the calculation of the calculation formula,vnh is used to represent the weighted sum of the inputs of each node of the h − 1 layer to the nth node of the h layer, when h = 2.(3)vn2=∑m=1kTnm1am+cn1,dnh=gvnh.Then, the equation can be expressed as follows:(4)v2=t1a+c1,d2=gv2,v3=t2d2+c2,rt,ca=d2=gv3.When the activation valuedh of the hth layer is given, the activation value dh+1 of the h + 1th layer can be calculated according to the following formula:(5)vh+1=thdh+ch,dh+1=gvh+1.Assuming that the weight parametert and the bias parameter c are known, the activation value dnh of each node in the neural network can be obtained through the continuous iterative calculation of the above formula. The forward propagation process of the neural network is the calculation process of the above neural network [16].


## 3.2.3. Back Propagation Algorithm

The training process of artificial neural network is a complex autonomous learning process [17]. The forward propagation process and the back propagation process constitute the training process of the artificial neural network [18]. The forward propagation process is to calculate the input image data, and the back propagation process mainly compares the output sample value calculated in the forward transfer process with the actual value of the sample. Then, the residual error is obtained by statistics and then propagated forward from the network, and the residual error of each node in the hidden layer of the neural network is counted. Then, the weight parameter t and bias parameter c of the neural network are continuously adjusted until the error value between the output predicted sample value of the model and the actual value is the smallest.Given a sample set (a, b), a total of i groups, namely, a1,b1,a2,b2,⋯,ai,bi , for a single sample ai,bi , the cost function is defined as(6)y=t,c;a,b=12rt,cai−bi2.The formula is a variance cost function. The overall cost function calculation expression is defined as(7)yt,c=1s∑n=1syt,c,ai,bi+φ2∑h=1kh−1∑n=1Rh∑m=1Rh+1Tnmh2.In the above formula,s represents the number of samples in the training sample set; the first term represents the mean square error term of the cost function t,c , and the second term represents a weight attenuation term of the cost function. The function of the weight attenuation parameter is to adjust the ratio of the two items before and after in the formula in the cost function so as to control the importance of each item in the cost function and prevent the network model from overfitting [19].The main goal of the backpropagation process is to minimize the value of the cost function of the calculated network model, which can be achieved by finding the optimal weightt and bias c in the process of continuously updating the network weight parameters. In order to realize the calculation of the neural network, the ownership value parameters can be initialized to random values close to 0 at first, but not all the ownership value parameters are set to 0. When the network parameters are initialized, if each weight parameter is set to the same value, all parameters and output values in the same layer in the network will be the same so that the model cannot achieve convergence through training. After initializing the parameters in the network model, optimization methods such as gradient descent are usually used to minimize the cost function of the network model [20].In each iteration process of network model training, the weight parametert and bias parameter c are fine-tuned using gradient descent method to obtain the network model with the minimum cost function. The process calculation formula is as follows:(8)Tnmh:=Tnmh−δ∂yt,c∂Tnmh,cnh:=cnh−δ∂yt,c∂cnh,whereδ represents the learning rate.The calculation formula of the partial derivative of the total cost functionyt,c of the entire sample data set is(9)∂∂Tnmhyt,c=1s∑n=1s∂∂Tnmhyt,c,ai,bi+φTnmh,∂∂cnhyt,c=1s∑n=1s∂∂cnhyt,c,ai,bi.The calculation of the above formula needs to first calculate the partial derivatives∂/∂Tnmhyt,c,a,b and ∂/∂cnhyt,c,a,b of the cost function y(t, c, a, b) corresponding to a single training sample (a, b), and the BP algorithm is very effective for calculating the partial derivative of the cost function [2]. The entire process of the back propagation algorithm is as follows:(1)
A sample set (a, b), a total of i groups, namely, a1,b1,a2,b2,⋯,ai,bi is given. Use the formula of the forward propagation process to calculate the activation value dh of each layer of h1,h2,⋯,hk , including the value of the output rt,ca of the neural network.(2)
For each output unitn of the last output layer of the neural network, the residual expression is(10)βnkh=∂∂vnkh12b−rt,ca2=−bn−dnkh∗gvnkh.(3)
Calculating the residuals of each layerhkh−1,hkh−2,⋯,h2 of the hidden layer in the neural network, the calculation formula is as follows:(11)βnh=∑y=1Sh+1Tynhβnh+1gvnh.(4)
The partial derivative numerical expression for calculating the cost function of a single sample is as follows:(12)∂∂Tnmhyt,c,a,b=dyhβnh+1,∂∂cnhyt,c,a,b=βnh+1.The partial derivative value of the cost function of a single sample is substituted into the partial derivative calculation formula of the overall sample cost function so that the weight parametert and the bias parameter c can be updated [22]. Figure 5 shows the training process of the neural network.Figure 5
Neural network training process.


## 4. The Experimental Results of the Teaching Research of Japanese Professional Writing Course Based on the Fusion of Big Data

### 4.1. Implementation of Contextualized Teaching of Japanese Major Courses

#### 4.1.1. Context Creation

The most important key point for the ease of use of contextual teaching methods in Japanese courses is the convenience of context creation. Because all the units of the Japanese overall curriculum are presented in the form of topics, it is very convenient to create situations and easy for teachers to prepare lessons. It is also more efficient to teach in situations. Knowledge does not need to be scattered and can be presented in a centralized manner. At the same time, creating a situation is the first and most important step in teaching because the created situation directly determines the efficiency of subsequent teaching and simulation training, and it also relates to the high ground of student participation. Therefore, in the process of initial setting of the situation, it is necessary to combine the content of the unit and the actual situation to create a good situation close to life. The creation of a situation is convenient for students to better understand and use knowledge in the follow-up process of knowledge explanation. Creating a situation is the first step to consider, and it is also the key to the overall situational teaching level.


#### 4.1.2. Perception of the Situation

After creating the situation, you cannot rush to utilize basic knowledge. This is counterproductive and will make students feel that the situational teaching method is the same as the traditional book teaching. The sentimental situation becomes the “bridge” between the communication situation and book knowledge and plays a vital role in transition. Helping students relax and to integrate into the situation in a pleasant and gentle way so as to learn basic knowledge, to integrate the basic knowledge learned with the situation and to bring it into the situation repeatedly experience and to perceive knowledge in the situation, and to strengthen understanding and perception. The key to perceiving a situation lies in combining reality and two-way interaction, allowing students to fully express their views and ideas about the situation so as to use this as an entry point to introduce important curriculum knowledge.


#### 4.1.3. Basic Knowledge Teaching

There are many misunderstandings of situational teaching: They abandon or even exclude basic knowledge course teaching; they believe that the effect of situational teaching varies from person to person and should not be interfered with, allowing students to play freely in a state of goal dissociation-free learning; such ideas and opinions are not rare. In fact, the curriculum teaching of basic knowledge is not inconsistent with the free, autonomous, and self-confident learning advocated by situational teaching. The function and purpose of basic knowledge teaching is to better serve contextual learning. Only by bringing the learning of basic knowledge into the context and fermenting in the context, can a huge learning efficiency be developed. At the same time, basic knowledge teaching will make students more confident in situational performance because they know what vocabulary and sentence patterns to use and to express, so they will not be overwhelmed in the situation. In a sense, basic knowledge learning is a part of situational teaching, as well as a catalyst and energy agent for situational learning.


#### 4.1.4. Contextual Teaching

The abovementioned multiple contextual methods are used to deepen basic knowledge learning in the context, to perceive knowledge, to comprehend knowledge, to learn knowledge, to deepen memory, to strengthen theory, and to provide one’s own Japanese use and operation ability.


#### 4.1.5. Situational Separation and Summary

After situational learning, teachers take the initiative to lead students to withdraw from the situation and lead the students to recall the situational learning. This is an important step for the deepening and summary of situational learning. Improving students’ metacognition level, helping students self-analyze and self-examine, making slight comments on their performance in the contextual learning process, and providing thought guidance on learning and review after class. This is the finishing touch of situational teaching, and it is also directly related to students’ follow-up self-evaluation and checking for omissions. Because situational teaching cannot have the same effect for every student, it is very important to review after class that varies from person to person. Through contextual learning, students will have a deep understanding of their own learning level and learning efficiency. The teacher’s guidance will help students build confidence, improve the learning situation of this unit and participate more actively in subsequent contextual learning, and do a good job for it, and make full and good preparations for it.


### 4.2. The Results of Contextualized Teaching of Japanese Major Courses

Arranging two exams in one semester, namely, midterm assessment and final assessment. The experiment lasted for one year and data were collected into four samples. The results of each student are presented in the form of a data table, and the average grade is counted by class as a unit. The analysis and comparison are carried out with the help of Excel and SPSS data statistics software so as to draw conclusions and explore the effect of contextual teaching and the changes in student performance. Based on this data, combined with observations and records during the experiment, the final experimental results are obtained, and relevant follow-up experiments and research suggestions are put forward. Class 1 is the experimental group and Class 2 is the control group. Figure6 is a statistical chart of Japanese course scores.Figure 6
Japanese course score statistics.From the perspective of educating experimenters and observers, and combining the above data, the author discussed and communicated with 4 teachers and 20 students (10 in each control group in the experimental group) from the Japanese language teaching and research section, and the following analysis was obtained: (1) The scores of the students in the control group were basically stable and with slight fluctuations. The scores of the students in the experimental group improved steadily, and the score difference between the two classes gradually widened. The scores of the students in the experimental group improved significantly in the two exams in the second semester. (2) Class 1’s class activity is always higher than class 2’s, students’ enthusiasm for participation is high, and students have a high degree of recognition of the teaching methods of situational teaching. After class, they have strong desires to participate in discussion and preview, especially the cooperation and discussion between groups have become particularly close, which has also become a prominent embodiment of the teaching “post effect” of contextualized teaching. (3) Class 1 students are particularly prominent in the middle and later class performance and course ability tests. Compared with the restraint and tension in the early period, they have made great progress. In this regard, teachers and students have the same obvious feelings. As educational observers and experimenters, they also clearly feel that the cooperation between students and teachers has improved significantly when the experiment is halfway through. (4) The class performance of the class 2 students in the control group is always in a relatively flat state. Figure7 shows the statistical graph of group performance analysis. Among them, A is the first semester of class 1, and B is the first semester of class 2, and so on.Figure 7
Grouped performance analysis statistics.In summary, it can be concluded that, compared with the control group, the class 1 students have obvious salient points in the following three aspects: first, the improvement of learning autonomy; second, the improvement of learning enthusiasm; and third, the improvement of basic Japanese ability and quality.The overall SPSS data analysis can clearly represent the overall improvement of the contextual teaching for Japanese majors in the Japanese course learning level. The summary table of the three test data can draw the following summary conclusions. Figure8 shows the SPSS software’s data detection of results.(1)
Through the independent samplet-test of SPSS software, it can be concluded that there is no significant difference between the first test and the second test of the two classes. In the third and fourth exams, the results of the experimental class were significantly higher than those of the control group.(2)
Through the repeated variance measurement analysis of SPSS software, it can be concluded that there is no significant change in the results of the control group for four times, the results of the experimental group have changed significantly, and the results of the third and fourth times are significantly improved compared with the previous two results.(3)
Through SPSS software postanalysis, the preliminary data and difference changes are calibrated, and it is confirmed that the data results are correct. The statistical quantitative characterization is obvious, and the experimental data can be judged to be valid, and the relevant experimental conclusions can be drawn on this basis.Figure 8
SPSS data detection.


### 4.3. Contextualized Teaching on Japanese Major Writing

In order to further analyze the impact of the contextual teaching method on the students’ writing ability in terms of vocabulary, word frequency distribution, student composition scores, composition fluency, composition errors, and the nature of errors, SPSS software is used to describe all composition data statistics. For example, Table1 reflects the descriptive statistics of the experimental class and the control class.Table 1
Pretest descriptive statistics of experimental class and control class.
Experimental group composition scoreControl group composition scoreEffectiveNN202020Minimal value51.544.5—Maximum value8083—Mean value65.893265.9758—Standard deviation7.29837.2921—The statistical results in the table show that the average compositions of the two classes before the test are 65.8932 and 65.9758, and the standard deviations are 7.2983 and 7.2921, respectively. This shows that the average scores of the writing scores before the experiment are basically close, and the internal dispersion is also roughly the same. Therefore, the two classes are two parallel classes with roughly the same level of English writing, which ensures the validity and reliability of the experiment.Figure9 shows the descriptive statistics after the experimental class and the control class. From the analysis, the average composition scores of the two classes of students are 72.7833 points and 66.3333 points, the average composition fluency points are 156.27 points and 119.73 points, and the mean difference is equal to 6.45 points and 36.53333 points. It can be seen that after the experiment, the students’ composition score and composition fluency are higher than those of the control class.Figure 9
Descriptive statistics after the experimental class and the control class.In order to explore the data distribution of students’ composition vocabulary and word frequency after the experiment, this study made relevant statistics on the vocabulary usage and word frequency distribution of students in the experimental class and the control class. Table2 shows the vocabulary size and word frequency distribution of the experimental group, and Table 3 shows the vocabulary size and word frequency distribution of the control group.Table 2
Vocabulary size and word frequency distribution of the experimental group.
Vocabulary frequency tablePictogramPictogram (%)Class symbolClass symbol (%)1000th vocabulary368978.151959.012000th vocabulary3197.01919.71Vocabulary above 20002616.612914.78Words not in the vocabulary4698.3815116.5Table 3
Vocabulary size and word frequency distribution of the control group.
Vocabulary frequency tablePictogramPictogram (%)Class symbolClass symbol (%)1000th vocabulary289179.9841162.192000th vocabulary1694.916910.9Vocabulary above 20001995.62719.82Words not in the vocabulary3299.4910918It can be seen that the students in the experimental class not only have a larger amount of composition symbols than the students in the control class, but also the total amount of class symbols used is larger than that of the students in the control class. In other words, the overall vocabulary of the students in the experimental class is greater than that of the students in the control class, and the use of different vocabulary is also higher than that in the control class.In order to ensure the credibility of the experimental data, two Japanese teachers with experience in teaching were invited to make corrections to the students’ composition. Finally, the average of the three composition scores was taken as the final score of the students’ composition.It can be seen from the correlation between the composition scores of the experimental class and related indicators in Table4 that after the experimental class students have carried out the situational teaching method, the composition scores of the experimental class and symbol 2 and symbol 3 are significantly positively correlated at the level of 0.05, and the correlation coefficient is 0.531 and 0.571, respectively. Therefore, the composition scores of the students in the experimental class are affected by the 2000th vocabulary and vocabulary above the 2000th vocabulary. In other words, the more the 2000th vocabulary and the 2000th vocabulary or above in the students’ composition, the higher the composition score.Table 4
Correlation between experimental class composition scores and related indicators.
Number of wrong wordsPictogram 1Composition grades−0.4090.512Pictogram 2Pictogram 3Composition grades0.5310.571Class symbol 1Class symbol 2Composition grades0.4990.618


## 4.1. Implementation of Contextualized Teaching of Japanese Major Courses

### 4.1.1. Context Creation

The most important key point for the ease of use of contextual teaching methods in Japanese courses is the convenience of context creation. Because all the units of the Japanese overall curriculum are presented in the form of topics, it is very convenient to create situations and easy for teachers to prepare lessons. It is also more efficient to teach in situations. Knowledge does not need to be scattered and can be presented in a centralized manner. At the same time, creating a situation is the first and most important step in teaching because the created situation directly determines the efficiency of subsequent teaching and simulation training, and it also relates to the high ground of student participation. Therefore, in the process of initial setting of the situation, it is necessary to combine the content of the unit and the actual situation to create a good situation close to life. The creation of a situation is convenient for students to better understand and use knowledge in the follow-up process of knowledge explanation. Creating a situation is the first step to consider, and it is also the key to the overall situational teaching level.


### 4.1.2. Perception of the Situation

After creating the situation, you cannot rush to utilize basic knowledge. This is counterproductive and will make students feel that the situational teaching method is the same as the traditional book teaching. The sentimental situation becomes the “bridge” between the communication situation and book knowledge and plays a vital role in transition. Helping students relax and to integrate into the situation in a pleasant and gentle way so as to learn basic knowledge, to integrate the basic knowledge learned with the situation and to bring it into the situation repeatedly experience and to perceive knowledge in the situation, and to strengthen understanding and perception. The key to perceiving a situation lies in combining reality and two-way interaction, allowing students to fully express their views and ideas about the situation so as to use this as an entry point to introduce important curriculum knowledge.


### 4.1.3. Basic Knowledge Teaching

There are many misunderstandings of situational teaching: They abandon or even exclude basic knowledge course teaching; they believe that the effect of situational teaching varies from person to person and should not be interfered with, allowing students to play freely in a state of goal dissociation-free learning; such ideas and opinions are not rare. In fact, the curriculum teaching of basic knowledge is not inconsistent with the free, autonomous, and self-confident learning advocated by situational teaching. The function and purpose of basic knowledge teaching is to better serve contextual learning. Only by bringing the learning of basic knowledge into the context and fermenting in the context, can a huge learning efficiency be developed. At the same time, basic knowledge teaching will make students more confident in situational performance because they know what vocabulary and sentence patterns to use and to express, so they will not be overwhelmed in the situation. In a sense, basic knowledge learning is a part of situational teaching, as well as a catalyst and energy agent for situational learning.


### 4.1.4. Contextual Teaching

The abovementioned multiple contextual methods are used to deepen basic knowledge learning in the context, to perceive knowledge, to comprehend knowledge, to learn knowledge, to deepen memory, to strengthen theory, and to provide one’s own Japanese use and operation ability.


### 4.1.5. Situational Separation and Summary

After situational learning, teachers take the initiative to lead students to withdraw from the situation and lead the students to recall the situational learning. This is an important step for the deepening and summary of situational learning. Improving students’ metacognition level, helping students self-analyze and self-examine, making slight comments on their performance in the contextual learning process, and providing thought guidance on learning and review after class. This is the finishing touch of situational teaching, and it is also directly related to students’ follow-up self-evaluation and checking for omissions. Because situational teaching cannot have the same effect for every student, it is very important to review after class that varies from person to person. Through contextual learning, students will have a deep understanding of their own learning level and learning efficiency. The teacher’s guidance will help students build confidence, improve the learning situation of this unit and participate more actively in subsequent contextual learning, and do a good job for it, and make full and good preparations for it.


## 4.1.1. Context Creation

The most important key point for the ease of use of contextual teaching methods in Japanese courses is the convenience of context creation. Because all the units of the Japanese overall curriculum are presented in the form of topics, it is very convenient to create situations and easy for teachers to prepare lessons. It is also more efficient to teach in situations. Knowledge does not need to be scattered and can be presented in a centralized manner. At the same time, creating a situation is the first and most important step in teaching because the created situation directly determines the efficiency of subsequent teaching and simulation training, and it also relates to the high ground of student participation. Therefore, in the process of initial setting of the situation, it is necessary to combine the content of the unit and the actual situation to create a good situation close to life. The creation of a situation is convenient for students to better understand and use knowledge in the follow-up process of knowledge explanation. Creating a situation is the first step to consider, and it is also the key to the overall situational teaching level.


## 4.1.2. Perception of the Situation

After creating the situation, you cannot rush to utilize basic knowledge. This is counterproductive and will make students feel that the situational teaching method is the same as the traditional book teaching. The sentimental situation becomes the “bridge” between the communication situation and book knowledge and plays a vital role in transition. Helping students relax and to integrate into the situation in a pleasant and gentle way so as to learn basic knowledge, to integrate the basic knowledge learned with the situation and to bring it into the situation repeatedly experience and to perceive knowledge in the situation, and to strengthen understanding and perception. The key to perceiving a situation lies in combining reality and two-way interaction, allowing students to fully express their views and ideas about the situation so as to use this as an entry point to introduce important curriculum knowledge.


## 4.1.3. Basic Knowledge Teaching

There are many misunderstandings of situational teaching: They abandon or even exclude basic knowledge course teaching; they believe that the effect of situational teaching varies from person to person and should not be interfered with, allowing students to play freely in a state of goal dissociation-free learning; such ideas and opinions are not rare. In fact, the curriculum teaching of basic knowledge is not inconsistent with the free, autonomous, and self-confident learning advocated by situational teaching. The function and purpose of basic knowledge teaching is to better serve contextual learning. Only by bringing the learning of basic knowledge into the context and fermenting in the context, can a huge learning efficiency be developed. At the same time, basic knowledge teaching will make students more confident in situational performance because they know what vocabulary and sentence patterns to use and to express, so they will not be overwhelmed in the situation. In a sense, basic knowledge learning is a part of situational teaching, as well as a catalyst and energy agent for situational learning.


## 4.1.4. Contextual Teaching

The abovementioned multiple contextual methods are used to deepen basic knowledge learning in the context, to perceive knowledge, to comprehend knowledge, to learn knowledge, to deepen memory, to strengthen theory, and to provide one’s own Japanese use and operation ability.


## 4.1.5. Situational Separation and Summary

After situational learning, teachers take the initiative to lead students to withdraw from the situation and lead the students to recall the situational learning. This is an important step for the deepening and summary of situational learning. Improving students’ metacognition level, helping students self-analyze and self-examine, making slight comments on their performance in the contextual learning process, and providing thought guidance on learning and review after class. This is the finishing touch of situational teaching, and it is also directly related to students’ follow-up self-evaluation and checking for omissions. Because situational teaching cannot have the same effect for every student, it is very important to review after class that varies from person to person. Through contextual learning, students will have a deep understanding of their own learning level and learning efficiency. The teacher’s guidance will help students build confidence, improve the learning situation of this unit and participate more actively in subsequent contextual learning, and do a good job for it, and make full and good preparations for it.


## 4.2. The Results of Contextualized Teaching of Japanese Major Courses

Arranging two exams in one semester, namely, midterm assessment and final assessment. The experiment lasted for one year and data were collected into four samples. The results of each student are presented in the form of a data table, and the average grade is counted by class as a unit. The analysis and comparison are carried out with the help of Excel and SPSS data statistics software so as to draw conclusions and explore the effect of contextual teaching and the changes in student performance. Based on this data, combined with observations and records during the experiment, the final experimental results are obtained, and relevant follow-up experiments and research suggestions are put forward. Class 1 is the experimental group and Class 2 is the control group. Figure6 is a statistical chart of Japanese course scores.Figure 6
Japanese course score statistics.From the perspective of educating experimenters and observers, and combining the above data, the author discussed and communicated with 4 teachers and 20 students (10 in each control group in the experimental group) from the Japanese language teaching and research section, and the following analysis was obtained: (1) The scores of the students in the control group were basically stable and with slight fluctuations. The scores of the students in the experimental group improved steadily, and the score difference between the two classes gradually widened. The scores of the students in the experimental group improved significantly in the two exams in the second semester. (2) Class 1’s class activity is always higher than class 2’s, students’ enthusiasm for participation is high, and students have a high degree of recognition of the teaching methods of situational teaching. After class, they have strong desires to participate in discussion and preview, especially the cooperation and discussion between groups have become particularly close, which has also become a prominent embodiment of the teaching “post effect” of contextualized teaching. (3) Class 1 students are particularly prominent in the middle and later class performance and course ability tests. Compared with the restraint and tension in the early period, they have made great progress. In this regard, teachers and students have the same obvious feelings. As educational observers and experimenters, they also clearly feel that the cooperation between students and teachers has improved significantly when the experiment is halfway through. (4) The class performance of the class 2 students in the control group is always in a relatively flat state. Figure7 shows the statistical graph of group performance analysis. Among them, A is the first semester of class 1, and B is the first semester of class 2, and so on.Figure 7
Grouped performance analysis statistics.In summary, it can be concluded that, compared with the control group, the class 1 students have obvious salient points in the following three aspects: first, the improvement of learning autonomy; second, the improvement of learning enthusiasm; and third, the improvement of basic Japanese ability and quality.The overall SPSS data analysis can clearly represent the overall improvement of the contextual teaching for Japanese majors in the Japanese course learning level. The summary table of the three test data can draw the following summary conclusions. Figure8 shows the SPSS software’s data detection of results.(1)
Through the independent samplet-test of SPSS software, it can be concluded that there is no significant difference between the first test and the second test of the two classes. In the third and fourth exams, the results of the experimental class were significantly higher than those of the control group.(2)
Through the repeated variance measurement analysis of SPSS software, it can be concluded that there is no significant change in the results of the control group for four times, the results of the experimental group have changed significantly, and the results of the third and fourth times are significantly improved compared with the previous two results.(3)
Through SPSS software postanalysis, the preliminary data and difference changes are calibrated, and it is confirmed that the data results are correct. The statistical quantitative characterization is obvious, and the experimental data can be judged to be valid, and the relevant experimental conclusions can be drawn on this basis.Figure 8
SPSS data detection.


## 4.3. Contextualized Teaching on Japanese Major Writing

In order to further analyze the impact of the contextual teaching method on the students’ writing ability in terms of vocabulary, word frequency distribution, student composition scores, composition fluency, composition errors, and the nature of errors, SPSS software is used to describe all composition data statistics. For example, Table1 reflects the descriptive statistics of the experimental class and the control class.Table 1
Pretest descriptive statistics of experimental class and control class.
Experimental group composition scoreControl group composition scoreEffectiveNN202020Minimal value51.544.5—Maximum value8083—Mean value65.893265.9758—Standard deviation7.29837.2921—The statistical results in the table show that the average compositions of the two classes before the test are 65.8932 and 65.9758, and the standard deviations are 7.2983 and 7.2921, respectively. This shows that the average scores of the writing scores before the experiment are basically close, and the internal dispersion is also roughly the same. Therefore, the two classes are two parallel classes with roughly the same level of English writing, which ensures the validity and reliability of the experiment.Figure9 shows the descriptive statistics after the experimental class and the control class. From the analysis, the average composition scores of the two classes of students are 72.7833 points and 66.3333 points, the average composition fluency points are 156.27 points and 119.73 points, and the mean difference is equal to 6.45 points and 36.53333 points. It can be seen that after the experiment, the students’ composition score and composition fluency are higher than those of the control class.Figure 9
Descriptive statistics after the experimental class and the control class.In order to explore the data distribution of students’ composition vocabulary and word frequency after the experiment, this study made relevant statistics on the vocabulary usage and word frequency distribution of students in the experimental class and the control class. Table2 shows the vocabulary size and word frequency distribution of the experimental group, and Table 3 shows the vocabulary size and word frequency distribution of the control group.Table 2
Vocabulary size and word frequency distribution of the experimental group.
Vocabulary frequency tablePictogramPictogram (%)Class symbolClass symbol (%)1000th vocabulary368978.151959.012000th vocabulary3197.01919.71Vocabulary above 20002616.612914.78Words not in the vocabulary4698.3815116.5Table 3
Vocabulary size and word frequency distribution of the control group.
Vocabulary frequency tablePictogramPictogram (%)Class symbolClass symbol (%)1000th vocabulary289179.9841162.192000th vocabulary1694.916910.9Vocabulary above 20001995.62719.82Words not in the vocabulary3299.4910918It can be seen that the students in the experimental class not only have a larger amount of composition symbols than the students in the control class, but also the total amount of class symbols used is larger than that of the students in the control class. In other words, the overall vocabulary of the students in the experimental class is greater than that of the students in the control class, and the use of different vocabulary is also higher than that in the control class.In order to ensure the credibility of the experimental data, two Japanese teachers with experience in teaching were invited to make corrections to the students’ composition. Finally, the average of the three composition scores was taken as the final score of the students’ composition.It can be seen from the correlation between the composition scores of the experimental class and related indicators in Table4 that after the experimental class students have carried out the situational teaching method, the composition scores of the experimental class and symbol 2 and symbol 3 are significantly positively correlated at the level of 0.05, and the correlation coefficient is 0.531 and 0.571, respectively. Therefore, the composition scores of the students in the experimental class are affected by the 2000th vocabulary and vocabulary above the 2000th vocabulary. In other words, the more the 2000th vocabulary and the 2000th vocabulary or above in the students’ composition, the higher the composition score.Table 4
Correlation between experimental class composition scores and related indicators.
Number of wrong wordsPictogram 1Composition grades−0.4090.512Pictogram 2Pictogram 3Composition grades0.5310.571Class symbol 1Class symbol 2Composition grades0.4990.618


## 5. Discussion

To sum up, even though the experimental class students received the situational teaching method, the number of errors was greater than that of the control class students. However, the overall composition score and composition fluency are higher than those of the students in the control class. “Errors are no longer regarded as a harmful form, but as an effective form for learners to devote themselves to learning.” It can be seen that part of the reason why the students in the experimental class make mistakes is that the students have invested in the situational teaching method, which is an effective form for the learners to invest in the new teaching method. It is not terrible for students to make mistakes. Teachers should encourage students to make mistakes and guide learning to actively correct them so as to better understand language block teaching. Mistakes are the beginning of progress. Just like in this study, the researchers found that the students in the experimental class made mistakes not the low-frequency words, but the more common high-frequency words within the 1000th vocabulary. Therefore, educators should strengthen the vocabulary spelling and memory skills in context teaching.


## 6. Conclusion

There are no prescribed procedures and templates for the contextualized teaching research of Japanese-related courses. In addition to the experimental principles and basic experimental procedures that must be grasped, there are no shortcuts and no routine models. Each research has its specific era background and regional background, student status background, and teacher status background. In the process of carrying out such research, it is inevitable to learn from previous experience. But an important principle and standard for grasping is that everything should proceed from reality and take reality as the foothold. On the one hand, before conducting experiments and research, we must make sufficient preparations, conduct adequate research work, understand the actual learning situation of the students in the school, and fully communicate with the instructors. And detailed consultations on the experimental research plan should be carried out, as well as jointly formulating a reasonable operation plan. In this process, the opinions of students can be integrated to be of great help to follow-up research. Not only that, but also to carry out subtle research work on the students’ past student performance and learning status and to make a student’s academic profile. In the process of follow-up experiments, comparisons can be made to discover specific changes in students, and the changes can be implemented in detail to individuals so as to form a more comprehensive and complete record for follow-up research. On the other hand, in the research process, different emergency measures should be set up according to the actual situation to ensure the smooth progress of the experiment, and the observation and collected data should be recorded in detail. Educational researchers occasionally bring subjective feelings and emotions into the results of the research. For example, after the results of an educational experiment, the results that educators think subjectively are often inconsistent with the results of data analysis, or even inconsistent with students’ intuitive feelings. Such results are subjectively imposed by educational researchers, not actual research results. Researchers must take reality as their foothold, maintain an objective and calm attitude, and conduct detailed summary analysis.



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*Source: 2838008-2022-05-19.xml*