# Design of Distance English Teaching Application System Combined with Behavioral Cognitive Settlement

Authors: Wenqin Liu
Journal: Advances in Multimedia
 (2022)

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## Abstract

In order to improve the quality of Distance English teaching, this study studies the teaching application system combined with behavioral cognition. In the research, relevant technologies such as settlement system integration, automatic scoring, data encryption, authority control and problem matching are introduced, and the system is improved. This process promotes the social and economic benefits brought by this kind of system with the help of various verification methods. At the same time, it also summarizes the advantages of Distance English teaching, and finally realizes the effective analysis of this kind of system. The design of the Distance English teaching application system is of great help to the follow-up teaching work. Therefore, we need to actively design such a system to optimize the quality of English teaching and lay a good foundation for teachers’ follow-up teaching.


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## Body

## 1. Introduction

With the rapid development of information technology, training high-quality information technology talents has become an objective demand to accelerate the process of social high informatization. To this end, the examination center of the Ministry of Education launched the “national computer application technology certificate examination,” referred to as nit. As the number of people participating in NIT training and examination increases year by year, the level is uneven and geographically dispersed. The traditional education methods and learning methods restrict the development and popularization of nit [1]. According to the characteristics of nit and based on modern distance education, this study puts forward a “subject leading combination” teaching mode and task-driven teaching method for nit, and studies and establishes a real-time, dynamic, personalized, and diversified teaching environment for the self-made teaching tasks in NIT teaching. The NIT process is shown in Figure 1.Figure 1
NIT process.In the new generation of Distance Education—network education environment, from the perspective of the cognitive theory of pedagogy, it advocates students’ autonomous learning ability and improves students’ enthusiasm for active learning. Network education breaks through the limitations of time and space. It can use the rich teaching resources of the network to provide various forms of teaching activities. How to deal with the problems encountered by students in distance education is one of the keys of distance education evaluation [2]. The application of BBS/online Q&A on the Internet in distance education provides a good communication environment between students and teachers. The proposal of an intelligent Q&A system makes up for the need for synchronization between teachers and students, and is a useful supplement to BBS/online Q&A. At the same time, the intelligent question answering system also greatly improves students’ learning enthusiasm and enthusiasm for active learning. One of the cores of modern distance open education is autonomous learning, which refers to the ability of learners to master their own learning or be responsible for their own learning. However, in Modern Distance Open English teaching, the author finds that what many students lack in English learning is the ability of autonomous learning, and some even do not have the ability to autonomous learning at all. If some students get English teaching materials, they are very confused. They do not know how to read the teaching materials, make use of network multimedia resources, and how to seek the help of teachers and classmates. They do not have any learning plans in the process of learning. They learn when they are free today and give up when they do not have time; English learning is also intermittent, sometimes not, and learning is not systematic [3]. Therefore, in the learning process, there is no monitoring and implementation of their own learning plan, so they do not have the self-management consciousness of autonomous learning. Without a learning plan, self-management consciousness, and monitoring of the implementation of the learning plan, students will doubt the learning effect. If they do not know what useful knowledge they have learned, they cannot evaluate their learning effect and lack the motivation to continue learning. Therefore, many students regard English learning as a heavy burden. When it comes to English learning, they have a headache, which seems to be full of difficulties [4]. Therefore, the author proposes that in Modern Distance Open English teaching, we should pay attention to the introduction and guidance of students’ metacognitive level and improve students’ autonomous learning ability to adapt to Modern Distance Open English learning [5]. The task of an online education settlement system is to establish a network settlement system supporting various online education services to realize the financial settlement between schools and banks. The system should have the characteristics of accuracy, openness, universality, security, and expansibility. The key technologies include: the research and implementation of the settlement management system; Research on the interface between settlement system and bank; Research on identity authentication technology; Research on data security transmission, etc. The goal of the overall design and integration of the online education system is to provide a complete set of key technologies and a scalable and interoperable system integration support platform required for online education in the new generation of the high-speed Internet environment, and to integrate seven business subsystems including online education settlement system into the overall framework of online education system [6]. In order to achieve this goal, it is necessary to consider the design and implementation of the interface between the settlement system and the support platform. The subject of the online education settlement system has gone through many stages from the investigation, feasibility analysis, and formulation of specifications to formal project approval. In the existing work, the author discusses the mode of online payment, business process, SSL protocol, and SET payment protocol. The realized module diagram and data flow diagram are shown in Figure 2.Figure 2
Structure diagram of existing payment system.


## 2. Literature Review

The development of settlement system integration focuses on the integration of data exchange with the supporting platform. Since the opening of the settlement module is a C++ bank direct link package based on the wirt32 platform, the settlement system must transmit data with the Fab-based data exchange module under Linux [7]. Considering the requirements of cross-platform and cross-language, it is necessary to use data transmission protocol with good interoperability to encapsulate communication. The open standard data carrier XML can fully express and apply all kinds of massive data on the Internet. Its characteristic is to separate the performance of data from the data itself. The value of data and the semantics of data can be combined, and it is self-describing and extensible. Therefore, it has become the preferred technology for data publishing, data integration, and exchange on Internet [8]. Simple object access protocol soap is the second generation XML protocol. The simplicity of the first-generation XML protocol and its dependence on XML leads to the problems of weak scalability and limited data expression ability. The second generation protocol uses XML namespace and XML schema mechanism to enhance the scalability of the protocol and combines description syntax and data type information to solve the problem of the first-generation protocol [9]. Soap protocol consists of three parts: defining the information package describing the message content and processing method architecture, defining the set of coding rules describing data types, and solving the conversion mechanism describing remote calls and responses. In essence, soap is a one-way transmission mode from sender to receiver. It provides many mechanisms to realize simple and flexible XML transmission, such as the mechanism of defining communication unit, error handling mechanism, extensible mechanism, flexible data expression mechanism, etc. [10]. Therefore, the settlement system selects soap as the data exchange and transmission protocol between it and other heterogeneous systems. For the security requirements of XML data transmission and storage, IETF and W3C jointly issued the XML encryption specification, which has become the W3C recommendation standard. XML encryption is not intended to replace SSL, but to provide a mechanism to solve the security requirements that SSL fails to cover, so as to provide end-to-end security assurance for applications that need to exchange structured data [11]. XML encryption technology can encrypt any data object, including any octet, XML document, an XML element, the content of an XML element, etc. in particular, it can encrypt different parts of the same XML document with different algorithms and keys [12]. XML encryption specifies the rules and processes of encryption and decryption operations and expresses the results in XML format so that partners can uniformly encrypt and decrypt and enhance interoperability [13]. Therefore, XML encryption technology has the characteristics of good flexibility, strong interoperability, and good compatibility. The encryption and decryption process flow is shown in Figures 3 and 4.Figure 3
XML encryption processing flow.Figure 4
XML decryption processing flow.As shown in Figure5, the J2EE framework is divided into five layers:Figure 5
J2EE framework.Persistence layer: the persistence layer is used to interact with persistent data such as a database. It operates persistent data according to the request of the domain layer. The implementation technologies of this layer include Dao, entitybean, 2do, and so on [14]. Domain layer: the domain layer is used to process message requests from users. It is generally based on the use case. Service layer: this layer is the entrance of the EJB server. Some of its functions are similar to the request layer. As the agent of the domain layer, it controls the transaction of each request according to different user requests and calls the corresponding business logic handler at the same time. In general, we will use the SessionFacade pattern to realize the function of this layer. Request layer: the main function of this layer is to connect the presentation layer with the service layer. In the process of connection, it is also responsible for saving the entrusted service layer to process the user request, the user’s status and the verification of the legitimacy of the user’s input data [15]. The representatives of this layer are Servlet, business delegate in Struts framework, action mode, and so on. Presentation layer: this is the user interface of the application. Users manipulate the application through this layer. In this layer, you can use JSP, Swing, HTML, Taglib, and other technologies to achieve dexterity.With the more and more extensive application of Internet, the original enterprise network based on LAN began to use Internet technology to build and reconstruct its own enterprise network, that is, intranet. Therefore, a new architecture B/s came into being and developed rapidly. It has become a new architecture used by many manufacturers. “Web-based” here mainly refers to “B/S structure.” B/S structure, that is, browser/server is the design mode of browser/server, which is usually called “thin client mode” [16]. As long as a browser is installed on the client, it can send requests to the server through HTTP protocol. The servers mainly include IIS, Apache and Tomcat (open source), 5boss (open source), websphere application server, Weblogic, etc. The server interacts with the database to complete all business logic. It can be said that B/S structure is based on TCP/IP protocol and is a design framework in line with the development of informatization and networking. In a sense, B/S is also a C/S structure. It is a special case of the application of a three-tier C/S structure on the web, which is developed from the traditional two-tier C/S structure. The browser three-tier structure model is shown in Table 1 [17].Table 1
Browser three-tier structure model.
BrowserCustom protocolApplication serverSQL languageDatabase serverUser interfaceApplication logicDatabase


## 3. Methods

### 3.1. Solutions to Research Problems

#### 3.1.1. Implementation of Main Algorithms for Automatic Scoring

If we want the examination system to have an automatic scoring function, we need to consider the following two problems: first, how to correctly extract information from the examinee’s documents; the second is how to use the extracted information to judge whether the examinee’s operation is correct. The basis of automatic scoring is to extract the characteristic values of the knowledge points investigated in the examinee document [18]. Based on the feature extraction method proposed in this study, the feature values of knowledge points in candidate documents are extracted according to the defined knowledge point features and known positioning feature values. The way to judge whether the examinee’s operation is correct is to match the eigenvalues of knowledge points in the examinee’s document with the standard answer. If the matching is consistent, it means that the operation is correct, and then score; otherwise, no score. Based on the method of feature extraction, in order to automatically extract the feature values of knowledge points, the system needs to obtain the positioning feature values of knowledge points [19]. This has been input into the system when the teacher makes a proposition and saved it to the database for extraction. When formulating the standard answer, first make a standard document according to the test question, and then extract the standard feature value from the standard document according to the defined knowledge point features and known positioning feature value, which is used as the standard answer and written into the database. Based on the above considerations, the whole automatic scoring process is shown in Figure 6. The left side of the Figure describes the process of extracting and scoring the eigenvalues of candidate documents, and the right side describes the process of extracting standard eigenvalues.Figure 6
Automatic scoring process.First, the teacher operates the document according to the test description to get the standard answer document. According to the description of the test question, allm knowledge points contained in the test question can be obtained. Then execute Algorithm 1.After the implementation of Algorithm 1, the positioning eigenvalues and knowledge point eigenvalues ofn knowledge points of the test question are extracted and stored in the database for scoring [20]. After extracting the characteristic value of the examinee document and scoring the examinee’s operation document, execute Algorithm 2.Algorithm 1:Extract standard eigenvalues.
j = 1;IfJ=1ifJ≤Mthen execute 3,otherwise execute 9;Find the positioning feature element information corresponding to thej-th knowledge point, and prompt the user to input these corresponding feature values LFj;Store the positioning eigenvalue LFj;Call the extraction rule corresponding to thej-th knowledge point, and extract the feature value KFj of the knowledge point from the standard answer document according to the positioning feature value;Store the characteristic value KFj of knowledge points;J=J+1;Execution 2;end.Algorithm 2:Extract the eigenvalues of the examinee documents and score.
J=1,sum=0;IfJ≤n,execute 3, otherwise execute 9;Extract the positioning feature value LFj of the j-th knowledge point from the database;Call the extraction rule corresponding to thej-th knowledge point, and extract the feature value KFj of the knowledge point from the candidate document according to the LFi;Extract the knowledge value of the knowledge point FJ from the database; If KFj matches KFj, the candidate’s score is accumulated into the total score sum;J=J+1;Execution 2;Output sum,end.


#### 3.1.2. Data Encryption and Authority Control

The network examination system is based on the web environment, and the web environment is a relatively unsafe environment, with viruses, Trojans, and hackers flooding the network. Therefore, the system must consider security issues to ensure the safety of the test questions and the fairness of the test. The system mainly adopts the following mechanisms to ensure the security of the system [21]. User identity authentication and authority control user identity authentication is mainly to identify the user’s identity and prevent illegal users from entering the system. The system mainly adopts the commonly used encryption mechanism to ensure the confidentiality of user information. The process is shown in Figure 7.Figure 7
Flowchart of user information assurance tightness.In addition to protecting user information with encryption, users of the system are also divided into different roles, mainly including three roles: administrator, teacher, and student. Each role has its own permissions. Therefore, the system must also control users’ access to action [22]. Otherwise, the user can directly enter the request of action in the address bar and directly access the resources after being forwarded by Struts.xml. Therefore, it is meaningless for the login module. Therefore, the system must provide corresponding permission control and access control. The struts 2 framework provides a corresponding interceptor mechanism. When users send requests, they can set some interceptors provided by the framework in Struts.xml or customize the interceptors. The activity diagram after introducing the interceptors is shown in Figure 8.Figure 8
Activity diagram of introducing interceptor framework.In the action layer, resources are organized by package. In the view layer, we also organize view resources according to the folder level. The three user roles of student, teacher, and admin put their resources into the corresponding folder. Only after passing through the permission interceptor can they access the resources inside. In the s2sh framework, action should be injected into the spring container. Therefore, after adding the interceptor for permission control, the sequence diagram is shown in Figure9 [23]. In case of power failure, illegal operation of students, or other unpredictable events during the examination, the examination cannot be carried out normally. Therefore, the second login is essential. After the second login, the continuity of the examinee’s examination environment before the power failure should be ensured, and the previous student’s operation should be retained. Therefore, the system uses the way of directory backup to retain the students’ examination environment and examination status. The system adopts a two-way backup scheme. The examinee’s examination machine directory backs it up and hides the backup directory. In order to realize synchronous update, the examination files are updated and stored periodically. When the examinee needs to log in again, it will be restored from the backup directory.Figure 9
Sequence diagram of interceptor control authority.


#### 3.1.3. Problem Matching Technology

In the question answering system, how to accurately find the answer in the question base according to the natural language sentences entered by the user? In addition to the word segmentation technology introduced above, question matching is also the key technology to realizing the question answering system. The quality of the matching algorithm directly affects the recall and accuracy of the system. Here are some common problem matching methods:TF.IDF method based on vector space model:Vector space model was proposed and developed in the 1950s. It is a statistical model about literature representation. It has strong computability and operability. It has been widely used in various applications in the field of information retrieval, such as text retrieval, automatic summarization, keyword self-extraction, text classification, and search engine. It lead the model to achieve good results [24].The basic idea of the vector space model is to form an n-dimensional vector space(1)TT1,T2,…,Tn,from all the feature words in the m documents involved, where Nii=1,2,…,m is the total number of feature words. For each document(2)ii=1,2,…,m,define vector(3)Pi=Wi1,Wi2,…,Win,where win represents the importance of feature word Ti in document I, that is, the weight. The weight calculation method mainly uses TF.IDF formula. At present, there are many TF.IDF formulas. Now a common normalization formula is given:(4)W=tfik×logN/dfk∑tkdtfik×logN/dfk2,where k represents the number of times the feature word Tk appears in the document Di (i.e., the frequency of the feature word), tf; The higher k means that the feature word Tk is more important to the document Dj; dfk indicates the number of documents containing the feature word Tk (i.e. the document frequency of the feature word). The higher dfk means that the feature word Tk plays a lower role in measuring the similarity between documents; N = IDl, that is, the number of all documents, and the denominator is the normalization factor; idfk = log(NIdfk) is the frequency of reverse documents. The higher the idfk, the greater the distinguishing effect of the feature word Tk on documents. If a feature word only appears in one document, idfk = log(n′); If a feature word appears in all documents, idfk = logl = O.


### 3.2. Experimental Method for Verifying the Scheme

Building a distance education platform on the Internet and providing distance teaching services is a widely used way. Through Internet technology, teachers and students can easily realize the process of distance teaching. In inemet, providing various dynamic application services for teachers and students in the form of web is a main form in the future. The dynamic application developed based on BIS mode can easily provide services for Internet users. On the basis of HTML static web pages, dynamic application development is realized through various dynamic web page development technologies. At present, the mainstream dynamic web page technologies include JSP, ASP, PHP, etc. these dynamic web page technologies are briefly introduced below. SP is more precisely a middleware, which transfers the requests on the web into an interpreter, analyzes all scripts in the interpreter, and then executes them. At this time, you can create a new COM object in the middleware, operate and call the properties and methods of the object, and complete more work through these COM components. Therefore, the strength of ASP lies not in its VBScript, but in its background COM components, which infinitely expand the ability of ASP [25]. SP is easy to learn and easy to install and use. In the Windows system, ASP can be used as long as it is installed. However, because ASP uses COM components, it is vulnerable to external attacks, and can not realize cross-operating system applications. PHP is an HTML-embedded language. PHP’s unique syntax combines C, Java, Perl, and PHP-style new syntax. It can execute dynamic web pages faster than CGI or Perl. PHP supports a variety of databases, such as Microsoft SQI, Server, Mysql, Sybase, Oracle, etc. It is a development language that can learn cross-platform and have good database interaction ability. The installation of a PHP application is complex, but it is unable to realize the commercialization of a PHP application. JSP pages are composed of HTML code and embedded Java code. The server processes these Java codes after the page is requested by the client, and later the generated Htnil page returns to the browser of the client. Javaservlet is the foundation of JSP technology and the foundation of large-scale web applications. Sending requires the cooperation of JavaServlet and JSP. JSP has all the characteristics of Java technology, such as being easy to use, completely object-oriented, platform-independent, safe and reliable, and mainly oriented to imemet. JSP can be written once and run everywhere. From Table 2, we can see the characteristics and advantages of JSP, ASI’ and PHP.Table 2
Performance comparison.
JSPASPPHPRunning speedFastFastFastRunning lossLessLargeLargeDegree of difficultyEasy to masterSimpleSimpleOperation platformMost canWindows platformWindows platform\UNIX platformExpansibilityGoodGoodPoorSecurityGoodPoorGoodFunction supportManyLessManyCombined with various dynamic web page development technologies and the development environment of distance education, this study selects JSP, tomcat5.0 for web server and J2SDK 1.4 for the JDK development package.


## 3.1. Solutions to Research Problems

### 3.1.1. Implementation of Main Algorithms for Automatic Scoring

If we want the examination system to have an automatic scoring function, we need to consider the following two problems: first, how to correctly extract information from the examinee’s documents; the second is how to use the extracted information to judge whether the examinee’s operation is correct. The basis of automatic scoring is to extract the characteristic values of the knowledge points investigated in the examinee document [18]. Based on the feature extraction method proposed in this study, the feature values of knowledge points in candidate documents are extracted according to the defined knowledge point features and known positioning feature values. The way to judge whether the examinee’s operation is correct is to match the eigenvalues of knowledge points in the examinee’s document with the standard answer. If the matching is consistent, it means that the operation is correct, and then score; otherwise, no score. Based on the method of feature extraction, in order to automatically extract the feature values of knowledge points, the system needs to obtain the positioning feature values of knowledge points [19]. This has been input into the system when the teacher makes a proposition and saved it to the database for extraction. When formulating the standard answer, first make a standard document according to the test question, and then extract the standard feature value from the standard document according to the defined knowledge point features and known positioning feature value, which is used as the standard answer and written into the database. Based on the above considerations, the whole automatic scoring process is shown in Figure 6. The left side of the Figure describes the process of extracting and scoring the eigenvalues of candidate documents, and the right side describes the process of extracting standard eigenvalues.Figure 6
Automatic scoring process.First, the teacher operates the document according to the test description to get the standard answer document. According to the description of the test question, allm knowledge points contained in the test question can be obtained. Then execute Algorithm 1.After the implementation of Algorithm 1, the positioning eigenvalues and knowledge point eigenvalues ofn knowledge points of the test question are extracted and stored in the database for scoring [20]. After extracting the characteristic value of the examinee document and scoring the examinee’s operation document, execute Algorithm 2.Algorithm 1:Extract standard eigenvalues.
j = 1;IfJ=1ifJ≤Mthen execute 3,otherwise execute 9;Find the positioning feature element information corresponding to thej-th knowledge point, and prompt the user to input these corresponding feature values LFj;Store the positioning eigenvalue LFj;Call the extraction rule corresponding to thej-th knowledge point, and extract the feature value KFj of the knowledge point from the standard answer document according to the positioning feature value;Store the characteristic value KFj of knowledge points;J=J+1;Execution 2;end.Algorithm 2:Extract the eigenvalues of the examinee documents and score.
J=1,sum=0;IfJ≤n,execute 3, otherwise execute 9;Extract the positioning feature value LFj of the j-th knowledge point from the database;Call the extraction rule corresponding to thej-th knowledge point, and extract the feature value KFj of the knowledge point from the candidate document according to the LFi;Extract the knowledge value of the knowledge point FJ from the database; If KFj matches KFj, the candidate’s score is accumulated into the total score sum;J=J+1;Execution 2;Output sum,end.


### 3.1.2. Data Encryption and Authority Control

The network examination system is based on the web environment, and the web environment is a relatively unsafe environment, with viruses, Trojans, and hackers flooding the network. Therefore, the system must consider security issues to ensure the safety of the test questions and the fairness of the test. The system mainly adopts the following mechanisms to ensure the security of the system [21]. User identity authentication and authority control user identity authentication is mainly to identify the user’s identity and prevent illegal users from entering the system. The system mainly adopts the commonly used encryption mechanism to ensure the confidentiality of user information. The process is shown in Figure 7.Figure 7
Flowchart of user information assurance tightness.In addition to protecting user information with encryption, users of the system are also divided into different roles, mainly including three roles: administrator, teacher, and student. Each role has its own permissions. Therefore, the system must also control users’ access to action [22]. Otherwise, the user can directly enter the request of action in the address bar and directly access the resources after being forwarded by Struts.xml. Therefore, it is meaningless for the login module. Therefore, the system must provide corresponding permission control and access control. The struts 2 framework provides a corresponding interceptor mechanism. When users send requests, they can set some interceptors provided by the framework in Struts.xml or customize the interceptors. The activity diagram after introducing the interceptors is shown in Figure 8.Figure 8
Activity diagram of introducing interceptor framework.In the action layer, resources are organized by package. In the view layer, we also organize view resources according to the folder level. The three user roles of student, teacher, and admin put their resources into the corresponding folder. Only after passing through the permission interceptor can they access the resources inside. In the s2sh framework, action should be injected into the spring container. Therefore, after adding the interceptor for permission control, the sequence diagram is shown in Figure9 [23]. In case of power failure, illegal operation of students, or other unpredictable events during the examination, the examination cannot be carried out normally. Therefore, the second login is essential. After the second login, the continuity of the examinee’s examination environment before the power failure should be ensured, and the previous student’s operation should be retained. Therefore, the system uses the way of directory backup to retain the students’ examination environment and examination status. The system adopts a two-way backup scheme. The examinee’s examination machine directory backs it up and hides the backup directory. In order to realize synchronous update, the examination files are updated and stored periodically. When the examinee needs to log in again, it will be restored from the backup directory.Figure 9
Sequence diagram of interceptor control authority.


### 3.1.3. Problem Matching Technology

In the question answering system, how to accurately find the answer in the question base according to the natural language sentences entered by the user? In addition to the word segmentation technology introduced above, question matching is also the key technology to realizing the question answering system. The quality of the matching algorithm directly affects the recall and accuracy of the system. Here are some common problem matching methods:TF.IDF method based on vector space model:Vector space model was proposed and developed in the 1950s. It is a statistical model about literature representation. It has strong computability and operability. It has been widely used in various applications in the field of information retrieval, such as text retrieval, automatic summarization, keyword self-extraction, text classification, and search engine. It lead the model to achieve good results [24].The basic idea of the vector space model is to form an n-dimensional vector space(1)TT1,T2,…,Tn,from all the feature words in the m documents involved, where Nii=1,2,…,m is the total number of feature words. For each document(2)ii=1,2,…,m,define vector(3)Pi=Wi1,Wi2,…,Win,where win represents the importance of feature word Ti in document I, that is, the weight. The weight calculation method mainly uses TF.IDF formula. At present, there are many TF.IDF formulas. Now a common normalization formula is given:(4)W=tfik×logN/dfk∑tkdtfik×logN/dfk2,where k represents the number of times the feature word Tk appears in the document Di (i.e., the frequency of the feature word), tf; The higher k means that the feature word Tk is more important to the document Dj; dfk indicates the number of documents containing the feature word Tk (i.e. the document frequency of the feature word). The higher dfk means that the feature word Tk plays a lower role in measuring the similarity between documents; N = IDl, that is, the number of all documents, and the denominator is the normalization factor; idfk = log(NIdfk) is the frequency of reverse documents. The higher the idfk, the greater the distinguishing effect of the feature word Tk on documents. If a feature word only appears in one document, idfk = log(n′); If a feature word appears in all documents, idfk = logl = O.


## 3.1.1. Implementation of Main Algorithms for Automatic Scoring

If we want the examination system to have an automatic scoring function, we need to consider the following two problems: first, how to correctly extract information from the examinee’s documents; the second is how to use the extracted information to judge whether the examinee’s operation is correct. The basis of automatic scoring is to extract the characteristic values of the knowledge points investigated in the examinee document [18]. Based on the feature extraction method proposed in this study, the feature values of knowledge points in candidate documents are extracted according to the defined knowledge point features and known positioning feature values. The way to judge whether the examinee’s operation is correct is to match the eigenvalues of knowledge points in the examinee’s document with the standard answer. If the matching is consistent, it means that the operation is correct, and then score; otherwise, no score. Based on the method of feature extraction, in order to automatically extract the feature values of knowledge points, the system needs to obtain the positioning feature values of knowledge points [19]. This has been input into the system when the teacher makes a proposition and saved it to the database for extraction. When formulating the standard answer, first make a standard document according to the test question, and then extract the standard feature value from the standard document according to the defined knowledge point features and known positioning feature value, which is used as the standard answer and written into the database. Based on the above considerations, the whole automatic scoring process is shown in Figure 6. The left side of the Figure describes the process of extracting and scoring the eigenvalues of candidate documents, and the right side describes the process of extracting standard eigenvalues.Figure 6
Automatic scoring process.First, the teacher operates the document according to the test description to get the standard answer document. According to the description of the test question, allm knowledge points contained in the test question can be obtained. Then execute Algorithm 1.After the implementation of Algorithm 1, the positioning eigenvalues and knowledge point eigenvalues ofn knowledge points of the test question are extracted and stored in the database for scoring [20]. After extracting the characteristic value of the examinee document and scoring the examinee’s operation document, execute Algorithm 2.Algorithm 1:Extract standard eigenvalues.
j = 1;IfJ=1ifJ≤Mthen execute 3,otherwise execute 9;Find the positioning feature element information corresponding to thej-th knowledge point, and prompt the user to input these corresponding feature values LFj;Store the positioning eigenvalue LFj;Call the extraction rule corresponding to thej-th knowledge point, and extract the feature value KFj of the knowledge point from the standard answer document according to the positioning feature value;Store the characteristic value KFj of knowledge points;J=J+1;Execution 2;end.Algorithm 2:Extract the eigenvalues of the examinee documents and score.
J=1,sum=0;IfJ≤n,execute 3, otherwise execute 9;Extract the positioning feature value LFj of the j-th knowledge point from the database;Call the extraction rule corresponding to thej-th knowledge point, and extract the feature value KFj of the knowledge point from the candidate document according to the LFi;Extract the knowledge value of the knowledge point FJ from the database; If KFj matches KFj, the candidate’s score is accumulated into the total score sum;J=J+1;Execution 2;Output sum,end.


## 3.1.2. Data Encryption and Authority Control

The network examination system is based on the web environment, and the web environment is a relatively unsafe environment, with viruses, Trojans, and hackers flooding the network. Therefore, the system must consider security issues to ensure the safety of the test questions and the fairness of the test. The system mainly adopts the following mechanisms to ensure the security of the system [21]. User identity authentication and authority control user identity authentication is mainly to identify the user’s identity and prevent illegal users from entering the system. The system mainly adopts the commonly used encryption mechanism to ensure the confidentiality of user information. The process is shown in Figure 7.Figure 7
Flowchart of user information assurance tightness.In addition to protecting user information with encryption, users of the system are also divided into different roles, mainly including three roles: administrator, teacher, and student. Each role has its own permissions. Therefore, the system must also control users’ access to action [22]. Otherwise, the user can directly enter the request of action in the address bar and directly access the resources after being forwarded by Struts.xml. Therefore, it is meaningless for the login module. Therefore, the system must provide corresponding permission control and access control. The struts 2 framework provides a corresponding interceptor mechanism. When users send requests, they can set some interceptors provided by the framework in Struts.xml or customize the interceptors. The activity diagram after introducing the interceptors is shown in Figure 8.Figure 8
Activity diagram of introducing interceptor framework.In the action layer, resources are organized by package. In the view layer, we also organize view resources according to the folder level. The three user roles of student, teacher, and admin put their resources into the corresponding folder. Only after passing through the permission interceptor can they access the resources inside. In the s2sh framework, action should be injected into the spring container. Therefore, after adding the interceptor for permission control, the sequence diagram is shown in Figure9 [23]. In case of power failure, illegal operation of students, or other unpredictable events during the examination, the examination cannot be carried out normally. Therefore, the second login is essential. After the second login, the continuity of the examinee’s examination environment before the power failure should be ensured, and the previous student’s operation should be retained. Therefore, the system uses the way of directory backup to retain the students’ examination environment and examination status. The system adopts a two-way backup scheme. The examinee’s examination machine directory backs it up and hides the backup directory. In order to realize synchronous update, the examination files are updated and stored periodically. When the examinee needs to log in again, it will be restored from the backup directory.Figure 9
Sequence diagram of interceptor control authority.


## 3.1.3. Problem Matching Technology

In the question answering system, how to accurately find the answer in the question base according to the natural language sentences entered by the user? In addition to the word segmentation technology introduced above, question matching is also the key technology to realizing the question answering system. The quality of the matching algorithm directly affects the recall and accuracy of the system. Here are some common problem matching methods:TF.IDF method based on vector space model:Vector space model was proposed and developed in the 1950s. It is a statistical model about literature representation. It has strong computability and operability. It has been widely used in various applications in the field of information retrieval, such as text retrieval, automatic summarization, keyword self-extraction, text classification, and search engine. It lead the model to achieve good results [24].The basic idea of the vector space model is to form an n-dimensional vector space(1)TT1,T2,…,Tn,from all the feature words in the m documents involved, where Nii=1,2,…,m is the total number of feature words. For each document(2)ii=1,2,…,m,define vector(3)Pi=Wi1,Wi2,…,Win,where win represents the importance of feature word Ti in document I, that is, the weight. The weight calculation method mainly uses TF.IDF formula. At present, there are many TF.IDF formulas. Now a common normalization formula is given:(4)W=tfik×logN/dfk∑tkdtfik×logN/dfk2,where k represents the number of times the feature word Tk appears in the document Di (i.e., the frequency of the feature word), tf; The higher k means that the feature word Tk is more important to the document Dj; dfk indicates the number of documents containing the feature word Tk (i.e. the document frequency of the feature word). The higher dfk means that the feature word Tk plays a lower role in measuring the similarity between documents; N = IDl, that is, the number of all documents, and the denominator is the normalization factor; idfk = log(NIdfk) is the frequency of reverse documents. The higher the idfk, the greater the distinguishing effect of the feature word Tk on documents. If a feature word only appears in one document, idfk = log(n′); If a feature word appears in all documents, idfk = logl = O.


## 3.2. Experimental Method for Verifying the Scheme

Building a distance education platform on the Internet and providing distance teaching services is a widely used way. Through Internet technology, teachers and students can easily realize the process of distance teaching. In inemet, providing various dynamic application services for teachers and students in the form of web is a main form in the future. The dynamic application developed based on BIS mode can easily provide services for Internet users. On the basis of HTML static web pages, dynamic application development is realized through various dynamic web page development technologies. At present, the mainstream dynamic web page technologies include JSP, ASP, PHP, etc. these dynamic web page technologies are briefly introduced below. SP is more precisely a middleware, which transfers the requests on the web into an interpreter, analyzes all scripts in the interpreter, and then executes them. At this time, you can create a new COM object in the middleware, operate and call the properties and methods of the object, and complete more work through these COM components. Therefore, the strength of ASP lies not in its VBScript, but in its background COM components, which infinitely expand the ability of ASP [25]. SP is easy to learn and easy to install and use. In the Windows system, ASP can be used as long as it is installed. However, because ASP uses COM components, it is vulnerable to external attacks, and can not realize cross-operating system applications. PHP is an HTML-embedded language. PHP’s unique syntax combines C, Java, Perl, and PHP-style new syntax. It can execute dynamic web pages faster than CGI or Perl. PHP supports a variety of databases, such as Microsoft SQI, Server, Mysql, Sybase, Oracle, etc. It is a development language that can learn cross-platform and have good database interaction ability. The installation of a PHP application is complex, but it is unable to realize the commercialization of a PHP application. JSP pages are composed of HTML code and embedded Java code. The server processes these Java codes after the page is requested by the client, and later the generated Htnil page returns to the browser of the client. Javaservlet is the foundation of JSP technology and the foundation of large-scale web applications. Sending requires the cooperation of JavaServlet and JSP. JSP has all the characteristics of Java technology, such as being easy to use, completely object-oriented, platform-independent, safe and reliable, and mainly oriented to imemet. JSP can be written once and run everywhere. From Table 2, we can see the characteristics and advantages of JSP, ASI’ and PHP.Table 2
Performance comparison.
JSPASPPHPRunning speedFastFastFastRunning lossLessLargeLargeDegree of difficultyEasy to masterSimpleSimpleOperation platformMost canWindows platformWindows platform\UNIX platformExpansibilityGoodGoodPoorSecurityGoodPoorGoodFunction supportManyLessManyCombined with various dynamic web page development technologies and the development environment of distance education, this study selects JSP, tomcat5.0 for web server and J2SDK 1.4 for the JDK development package.


## 4. Results

Natural language questioning is an embodiment of intelligence in an intelligent question answering system. Now all kinds of search engines are based on the questions of keywords and construct query conditions through the logical operation between keywords (Boolean logic retrieval technology). Natural language recognition and processing involve the knowledge and technology of artificial intelligence. Because there is no obvious difference sign between Chinese and other languages words (English and various foreign languages can be distinguished by spaces), it increases the difficulty of Chinese recognition. It is generally realized by word segmentation. This method should have a thesaurus as the basis of word segmentation. The natural language recognition used in this study is to match the natural language through keywords and express the natural language with keywords. The feasibility of this method in the intelligent question answering system is mainly due to the strong pertinence of course question answering documents or students’ questions. The semantics of various natural language documents based on courses can be expressed by the course keywords extracted by teachers. The particularity of this course document determines the feasibility of this method, or the special course teaching environment of distance education determines the feasibility of this method. Q&A resource database, which is another embodiment of intelligence in an intelligent Q&A system. On the one hand, it is the Q&A resource database, specifically the Q&A resource document of the course, which can be easily obtained from teachers. The teachers who teach this course are very familiar with this course. If they teach this course for a long time, they must have a lot of ready-made materials about the key points and difficulties of this course. If a teacher who has just started teaching such a course will sort out a lot of materials for classroom use in order to teach such a course well. Most simply, the ppt documents taught by teachers can be used as Q&A materials. After a little sorting out of these familiar materials, teachers can make HTML documents with pictures and texts, or even audio and video. These documents have a greater effect on answering students’ questions than pure text, and are of great benefit to improving the learning effect. On the other hand, it is also very convenient to expand the curriculum resource database. The course semantic web of intelligent question answering systems is based on their own courses, and the association between each course is connected through keywords. It can be said that the Q&A resource database is an independent resource database of courses, but the keywords between courses play a bridge role in the resource database. Therefore, it is very convenient to add, modify, delete, and other extensions to the Q&A resource database of the intelligent Q&A system, which will not affect the normal operation of the system.


## 5. Conclusion

The prototype of the English distance education system is based on JavaBean components and software architecture. After comprehensive and multi-directional testing, the cost of software development in this field has been reduced to the greatest extent. This not only improves the reliability of the system, but also shortens the development cycle, simplifies the maintenance of the system, and finally has maximum scalability and adaptability. The good or bad of the demand part is often one of the key factors to determine the success of software development. This study carefully analyzes the characteristics and related business processes of network teaching in Colleges and universities before software development, which lays a good foundation for the applicability and ease of use of the system. This study makes an overall demand analysis from the perspectives of feasibility analysis, use case analysis, business data process outline, system outline, and so on, which lays a good foundation for the later work.



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*Source: 6220368-2022-08-24.xml*