# Research on the Optimization of International News Communication Teaching Mode Based on Cluster Analysis under the Background of Big Data

Authors: Yu Cheng; Zongyi Zhou
Journal: Scientific Programming
 (2022)

---

## Abstract

With the rapid development of the Internet, the new and advanced teaching mode is becoming increasingly popular, which has become a focus in the field of education in recent years. Therefore, the research and development of online teaching platforms have become a hot topic. This article takes international news communication as the research background, aiming at the unclear existing level and weak target. The teaching mode of the single teaching form puts forward the “embedded online intelligent teaching platform.” A set of online innovative teaching platforms for large embedded systems is studied and established. The improved clustering analysis uses the GBKM clustering analysis algorithm to integrate grid clustering and K-means clustering and introduces the new meshing algorithm and the new function to calculate the density threshold. Through theoretical analysis and experimental verification, the superiority of the GBKM algorithm is demonstrated. As the GBKM clustering analysis algorithm is the core algorithm of a personalized smart learning system, it is applied to an online intelligent teaching platform. The experimental results show that the teaching effect of the optimized teaching mode is significantly improved.


---

## Body

## 1. Introduction

In October 2018, the Ministry of Education of China and the Propaganda Department of the Central Committee of the Communist Party of China issued “Opinions on Improving the Training Ability of Journalism and Communication Talents in Colleges and Universities and Implementing the Education and Training Plan 2.0 for Excellent Journalism and Communication Talents,” strengthening and improving the construction of international journalism and communication in colleges and universities; building first-class global journalism and communication specialty with Chinese characteristics and world level; fully implementing the fundamental task of moral education; educating people with the theory of socialist journalism with Chinese characteristics; and cultivating a large number of high-quality all-media compound expert news communication reserve talents with national feelings and global vision [1].Foreign scholars believe that teachers with news work experience must undertake international news communication teaching activities, and experienced teachers must guide students. Educators continue to work hard to cope with the transformation of the media industry, and journalism students in grades 2 and 3 of the University of Missouri have their newspapers in a real community work club [2]. In recent years, foreign news colleges and universities have paid particular attention to data journalism education. From “number one” to “visual first,” news technology has changed continuously, rapidly changing the media pattern, and even the flagship store of the New York Times is trying to keep up with the trend of the times [3].The traditional theoretical classroom still dominates the shaping of college students’ journalism concept in China. Domestic scholars have new thoughts on the whole journalism discipline [4]. Some theories of journalism have not adapted to the reality of media diversification, function diversification, and structure diversification. Re-understanding the media and communication will become the key to the teaching and research of news communication [5]. Journalism must make changes in two aspects. Firstly, journalism should not only open up new research fields but also break through the original professional and disciplinary restrictions; learn from and accommodate the research results, research methods, and theoretical systems of other disciplines; and achieve interdisciplinary integration [6]. Secondly, news colleges need to break through the teaching framework of mass communication; adjust the training scheme of news students; and change the curriculum system, teaching mode, and target orientation [7].Throughout the international journalism and communication professional teaching mode, too old teaching model is not in line with China’s national conditions, so they continue to propose new models. This article presents the “personalized intelligence” teaching model—implementing an embedded online intelligent teaching platform. By offering an improved GBKM clustering analysis algorithm, a new meshing algorithm, and a new function to calculate the density threshold, the GBKM clustering analysis algorithm is applied to the online intelligent teaching platform. In this article, a personalized, innovative learning system is designed. The GBKM clustering analysis algorithm is the core performance analysis algorithm. The students’ personality characteristics are obtained by clustering analysis of students’ performance records. According to the analysis results, the personalized intelligent learning system provides a personalized learning guide for students and achieves a satisfactory’ personalized intelligence’ teaching effect. Through theoretical analysis and experimental proof, the superiority of the GBKM algorithm is demonstrated, and the clustering process has achieved good results.


## 2. GBKM Clustering Analysis Algorithm

K-means is one of the most widely used clustering analysis algorithms and is a classical algorithm to solve the clustering problem [8, 9]. Because of the shortcomings of excessive dependence on the initial clustering center point of k and because of the large amount of computation, it is not efficient in time. At present, there are many improvements to the K-means algorithm. For example, the efficiency of grid clustering algorithms such as the K-means based on genetic algorithm, the K-means with penalty factor, and the PBKP algorithm is very high, and clusters with arbitrary shapes can be found. However, there are also various defects in grid clustering. For example, it depends on the selection of the density threshold. At present, there are many improved grid clustering algorithms, such as bipartite grid clustering, adaptive grid clustering, GCHL, etc [10, 11].In view of the shortcomings of the above algorithms, this article proposes an improved clustering analysis algorithm based on their advantages. GBKM clustering analysis algorithm has achieved satisfactory results through data testing and its application to practical systems.


### 2.1. Basic Concepts of Algorithm

IfA=D1,D2,…,Dn is n bounded domains, then S=D1×D2×⋯×Dn is an n-dimensional space, and D1,D2,…,Dn is regarded as the dimension of S. The algorithm's input is a set of points in n-dimensional space, defined as V=v1,v2,…,vn, where v1=vi1,vi2,…,vin represents the i-point. vij∈Dj represents the j-dimensional component of the i-point [12].Definition 1.
Network unit
Assuming that the value on thei dimension is in the interval li,hi, i=1,2,…,n each size is divided into p disjoint left-closed and right-open equal-length intervals, so the data space is divided into p″ grid cells. The length of the grid element on the i dimension is δi=hi−li/p, and the j interval on the i dimension can be obtained by the following formula:(1)Iij=li+j−1δi,li+jδi,j=1,2,…p.Definition 2.
Grid unit density
The densityDen（Ci）of the cell Ci is defined as the number of data points in the cell.Definition 3.
Density threshold
Set the density thresholdMinpts, the grid whose density is greater than the density threshold Minpts is “dense unit,” and the data in the unit whose density is less than the density threshold is “free data.”Definition 4.
Cluster focus
Given the clusterKi=ti1,ti2,…,tin, the mean cluster center is defined as(2)Zi=1ni∑x,y∈Kix,y2,where ni is the number of objects in cluster Ki, and x and y are pairwise different objects in cluster Ki.Definition 5.
Intermediate clustering
The sample space is divided, the unit grid set is defined, the density of the grid unit is calculated, and a grid clusters the dense team. This process, called intermediate clustering, only contains densely distributed points and does not contain all the books. The cluster generated by intermediate clustering is called a medium clustering cluster.Definition 6.
Postclustering
After the intermediate clustering, the center of each medium cluster is calculated as the initial center point, and then the remaining accessible data are K-means clustering. This process is called postclustering, and the clusters are called postclustering clusters.


### 2.2. The Basic Idea of the Algorithm

Firstly, based on grid clustering analysis, the sample space is divided, and the grid cell set is defined. The object is mapped to the corresponding grid cell, and the density of the grid cell is calculated. The unit with thickness is more incredible than the density thresholdr, and the data with a density less than the density threshold r are marked. The adjacent dense units are merged to form an “intermediate cluster,” which does not contain discrete free data. It calculates the center of gravity of the middle group as the initial cluster center [13], calculates the distance between free data and each cluster center, and the accessible data are assigned to the nearest intermediate cluster to form the “postcluster.” The initial clustering center of each postclustering is recalculated. The algorithm terminates if there is no change; if there is a change, the clustering is repeated until the clustering conditions are met [14].Grid definition itself is a complex problem. Grid size and placement have a significant impact on the clustering results. How to divide the grid is of great significance to the advantages and disadvantages of the algorithm. In the GBKM algorithm, a new function is introduced for meshing:δ=∑i=1nli/n and li are the lengths of the i component, i=1,2,…,n.At the same time, grid-based clustering is very dependent on the selection of density thresholdτ. Too large or too small τ will affect the algorithm's performance. A new algorithm is proposed for determining the density threshold in the GBKM algorithm: Minpts=∑i=1NDenCi21/2/N, where DenCi, i=1,2,…,N is the density value of N dense units with the highest density [15]. The value of N depends on the specific data. In general, DenCi is arranged in descending order. If DenCi display density has a significant jump, then N = I.


### 2.3. Steps of the Algorithm

According to a description of the basic idea, the basic steps are as follows:Step 1: Divide the data space intom disjoint, equal-length grid cells and define the grid cell set;Step 2: Assign the object to the appropriate unit;Step 3: Calculate the density of each unit;Step 4: Mark the grid with a density more remarkable than the density thresholdMinpts as “dense unit,” and mark the data in the division with thickness less than the density threshold as “free data.”Step 5: Repeat any dense unit that is not clustered, merge its adjacent thick units into a cluster until all wide units are clustered to form K “intermediate clusters”;Step 6: Calculate the center of gravityZi1 of the K intermediate clustering as the initial clustering center;Step 7: Repeat any accessible data object, calculate the distance betweendisxi,Ci and k initial clustering centers, where x is the accessible data object, Ci is the first class, if disx,Ci is the smallest, then x∈Ci; until no longer exists free data, forming “postclustering”;Step 8: Recalculate the center of gravity of the clusteringZi1, if Zi1−Zi0≤ε, the clustering ends. Otherwise, continue K-means clustering until Zii+1−Zii≤ε completes clustering.


### 2.4. Implementation of the Algorithm

The GBKM algorithm is written in Java language, and the editing environment is Eclipse. Eclipse is an open-source extensible application platform which provides a first-class Java integrated development environment for programmers [16]. The implementation of the algorithm is mainly divided into six parts. The first part defines various variables, methods, and classes, among which the most important is to define a public class cluster class, which describes the cluster generated by the clustering process, including the variable of cluster members, the variable of cluster centroid, and the method of updating the cluster centroid. The second part is the definition of the GetData (int cid, int analysis) method, the function of this method is to read data from the database and store it in the array folder after initialization; the third part is the definition of Grid () method. The function of this method is to divide the data space into grids and map the data objects to the Grid one by one. The fourth part is the method of clustering process preCluster (). The fifth part is the method of storing and displaying the clustering results. The sixth part is the main program entry (), which mainly generates the object of the above class and then calls the method of the thing to realize the whole clustering process.


## 2.1. Basic Concepts of Algorithm

IfA=D1,D2,…,Dn is n bounded domains, then S=D1×D2×⋯×Dn is an n-dimensional space, and D1,D2,…,Dn is regarded as the dimension of S. The algorithm's input is a set of points in n-dimensional space, defined as V=v1,v2,…,vn, where v1=vi1,vi2,…,vin represents the i-point. vij∈Dj represents the j-dimensional component of the i-point [12].Definition 1.
Network unit
Assuming that the value on thei dimension is in the interval li,hi, i=1,2,…,n each size is divided into p disjoint left-closed and right-open equal-length intervals, so the data space is divided into p″ grid cells. The length of the grid element on the i dimension is δi=hi−li/p, and the j interval on the i dimension can be obtained by the following formula:(1)Iij=li+j−1δi,li+jδi,j=1,2,…p.Definition 2.
Grid unit density
The densityDen（Ci）of the cell Ci is defined as the number of data points in the cell.Definition 3.
Density threshold
Set the density thresholdMinpts, the grid whose density is greater than the density threshold Minpts is “dense unit,” and the data in the unit whose density is less than the density threshold is “free data.”Definition 4.
Cluster focus
Given the clusterKi=ti1,ti2,…,tin, the mean cluster center is defined as(2)Zi=1ni∑x,y∈Kix,y2,where ni is the number of objects in cluster Ki, and x and y are pairwise different objects in cluster Ki.Definition 5.
Intermediate clustering
The sample space is divided, the unit grid set is defined, the density of the grid unit is calculated, and a grid clusters the dense team. This process, called intermediate clustering, only contains densely distributed points and does not contain all the books. The cluster generated by intermediate clustering is called a medium clustering cluster.Definition 6.
Postclustering
After the intermediate clustering, the center of each medium cluster is calculated as the initial center point, and then the remaining accessible data are K-means clustering. This process is called postclustering, and the clusters are called postclustering clusters.


## 2.2. The Basic Idea of the Algorithm

Firstly, based on grid clustering analysis, the sample space is divided, and the grid cell set is defined. The object is mapped to the corresponding grid cell, and the density of the grid cell is calculated. The unit with thickness is more incredible than the density thresholdr, and the data with a density less than the density threshold r are marked. The adjacent dense units are merged to form an “intermediate cluster,” which does not contain discrete free data. It calculates the center of gravity of the middle group as the initial cluster center [13], calculates the distance between free data and each cluster center, and the accessible data are assigned to the nearest intermediate cluster to form the “postcluster.” The initial clustering center of each postclustering is recalculated. The algorithm terminates if there is no change; if there is a change, the clustering is repeated until the clustering conditions are met [14].Grid definition itself is a complex problem. Grid size and placement have a significant impact on the clustering results. How to divide the grid is of great significance to the advantages and disadvantages of the algorithm. In the GBKM algorithm, a new function is introduced for meshing:δ=∑i=1nli/n and li are the lengths of the i component, i=1,2,…,n.At the same time, grid-based clustering is very dependent on the selection of density thresholdτ. Too large or too small τ will affect the algorithm's performance. A new algorithm is proposed for determining the density threshold in the GBKM algorithm: Minpts=∑i=1NDenCi21/2/N, where DenCi, i=1,2,…,N is the density value of N dense units with the highest density [15]. The value of N depends on the specific data. In general, DenCi is arranged in descending order. If DenCi display density has a significant jump, then N = I.


## 2.3. Steps of the Algorithm

According to a description of the basic idea, the basic steps are as follows:Step 1: Divide the data space intom disjoint, equal-length grid cells and define the grid cell set;Step 2: Assign the object to the appropriate unit;Step 3: Calculate the density of each unit;Step 4: Mark the grid with a density more remarkable than the density thresholdMinpts as “dense unit,” and mark the data in the division with thickness less than the density threshold as “free data.”Step 5: Repeat any dense unit that is not clustered, merge its adjacent thick units into a cluster until all wide units are clustered to form K “intermediate clusters”;Step 6: Calculate the center of gravityZi1 of the K intermediate clustering as the initial clustering center;Step 7: Repeat any accessible data object, calculate the distance betweendisxi,Ci and k initial clustering centers, where x is the accessible data object, Ci is the first class, if disx,Ci is the smallest, then x∈Ci; until no longer exists free data, forming “postclustering”;Step 8: Recalculate the center of gravity of the clusteringZi1, if Zi1−Zi0≤ε, the clustering ends. Otherwise, continue K-means clustering until Zii+1−Zii≤ε completes clustering.


## 2.4. Implementation of the Algorithm

The GBKM algorithm is written in Java language, and the editing environment is Eclipse. Eclipse is an open-source extensible application platform which provides a first-class Java integrated development environment for programmers [16]. The implementation of the algorithm is mainly divided into six parts. The first part defines various variables, methods, and classes, among which the most important is to define a public class cluster class, which describes the cluster generated by the clustering process, including the variable of cluster members, the variable of cluster centroid, and the method of updating the cluster centroid. The second part is the definition of the GetData (int cid, int analysis) method, the function of this method is to read data from the database and store it in the array folder after initialization; the third part is the definition of Grid () method. The function of this method is to divide the data space into grids and map the data objects to the Grid one by one. The fourth part is the method of clustering process preCluster (). The fifth part is the method of storing and displaying the clustering results. The sixth part is the main program entry (), which mainly generates the object of the above class and then calls the method of the thing to realize the whole clustering process.


## 3. International News Communication Intelligent Teaching Optimization

The development of an embedded network intelligent teaching system platform plays a good role in solving embedded professional theory teaching and market demand-oriented application teaching mode optimization. It can quickly improve embedded talents’ training effect and efficiency and solve the bottleneck of market demand for skills. At the same time, it is of great significance to the research and development of international news communication teaching platforms.


### 3.1. Development Tools and Models

#### 3.1.1. Development Tools

The system used JSP (Java Server Pages) technology framework to generate dynamic, interactive web server applications. JSP technology is the traditional web HTML file (∗.htm, ∗.html) into the Java program segment (scriptlet) and JSP tags, resulting in a JSP file (∗.jsp). JSP uses tags and scriptlets to encapsulate the processing logic that generates dynamic web pages, accesses the application logic of resources existing in the server, and separates the web logic from the web design and display. It supports the reusable component-based design and makes developing web-based applications rapid and easy [17].The web server of this system uses Tomcat 5.0 as a Servlet container, which passes page requests to Serverlets and returns the results to users. When a user requests access to a Servlet, it encapsulates the user’s request information in the ServletRequest object, then passes the Request object and Response object to the Servlet requested by the user, and the Servlet writes the results of the response to the ServletResponse object. Then Tomcat transmits the response results to the user.SQL Use server 2000 is d in the background database, and JavaBean is used to connect the database and query and update the data. Using advanced B/S (Browser/Server) architecture, page design uses the current international popular web production tool Dreamweaver 2004 and EditPlus for editing; the script language is JavaScript; page image processing uses PhotoshopCS and Flash 5.0.


#### 3.1.2. Development Model

This system is composed of people and computers for information collection, storage, knowledge transfer, and use. With the development of science and technology, the expansion of information, and the explosion of knowledge, how to effectively collect information and transfer knowledge has become the development purpose of this system. Therefore, it is imperative to develop a plan that considers the development and operation efficiency and meets the asynchronous real-time processing function. The development of this system uses the MVC model. MVC pattern is the abbreviation of “Model-View-Controller,” translated into Chinese as “Pattern-View-Controller.” That is, an application's input, processing, and output processes are divided into three layers according to model, view, and controller, namely a model layer, a view layer, and a control layer.View represents the user interaction interface. For web applications, it can be summarized as an HTML interface. An application may have many different views. The processing of the MVC design pattern for a view is limited to collecting and processing data on the view and the user’s request, but not the processing of business processes on the view. Handling of business processes is handed over to model processing. Model is the processing of business processes/states and formulating of business rules. The model accepts the data requested by the view and returns the final processing results. Model design is the core of MVC. When receiving requests from the user, the model is matched with the view and the user’s request together without any data processing is completed. The separation of model, view, and controller enables a model to have multiple display views. If the user changes the model’s data through a view controller, all views that depend on these data will change. When data changes, the controller notifies all views of the change. Models, ideas, and controllers are updated synchronously according to their relationships and primary functions [9].The view is in the web tier or client tier in this system. That is, JSP is the page display part. A controller is also in the web tier, implemented by Servlet, the logic part of page display. The model is in the middle tier, implemented with JavaBean on the server side, and implementing logical relationships, as shown in Figure1.Figure 1
The design of the model.


### 3.2. Design and Implementation of a Resource Center

As a learning website, providing students with as many learning materials and software download as possible is the proper service function of the website. The resource center module of this website includes nine submodules, as shown in Figure2: search module, e-book module, curriculum resource module, software download module, learning material module, free resource area module, the latest resource area module, the hottest resource area module, and other resource areas.Figure 2
The module system of resource center plans.Based on the above module system diagram:Search module: the user inputs keywords, and the system will display the matching records in the database according to fuzzy matching.Electronic book module: provides users free downloads of electronic books, such as PDF, superstar, and other formats, covering all aspects of embedded learning.Curriculum resource module: it lets users download free courses or other courses produced by this website. With the curriculum resource center, students can easily find free learning courseware.Software download module: Module more professional in the embedded aspect can obtain the professional information common download site that does not provide..Updated resource area: the resources provided by this area are the latest with the help of this website.Learning material module: the module mainly provides students with more whole, suitable learning materials, such as related technology frontier articles, excellent students' learning notes, etc.Free resource area: this area provides free downloads to students.The hottest resource zone: the zone aims to make the most popular resources present in a unique position on the website so that students can understand the current trend of resources.Other resource areas: it lets users download a window of resources other than free, up-to-date, and hottest resources to display the site's resources more fully on the page.According to the above description, the logical function of the resource center is clear. First, users select the module after entering the resource center, view the resource information, and then download it online, which can also view the resource's relevant resources and the website's outstanding recommendations.


## 3.1. Development Tools and Models

### 3.1.1. Development Tools

The system used JSP (Java Server Pages) technology framework to generate dynamic, interactive web server applications. JSP technology is the traditional web HTML file (∗.htm, ∗.html) into the Java program segment (scriptlet) and JSP tags, resulting in a JSP file (∗.jsp). JSP uses tags and scriptlets to encapsulate the processing logic that generates dynamic web pages, accesses the application logic of resources existing in the server, and separates the web logic from the web design and display. It supports the reusable component-based design and makes developing web-based applications rapid and easy [17].The web server of this system uses Tomcat 5.0 as a Servlet container, which passes page requests to Serverlets and returns the results to users. When a user requests access to a Servlet, it encapsulates the user’s request information in the ServletRequest object, then passes the Request object and Response object to the Servlet requested by the user, and the Servlet writes the results of the response to the ServletResponse object. Then Tomcat transmits the response results to the user.SQL Use server 2000 is d in the background database, and JavaBean is used to connect the database and query and update the data. Using advanced B/S (Browser/Server) architecture, page design uses the current international popular web production tool Dreamweaver 2004 and EditPlus for editing; the script language is JavaScript; page image processing uses PhotoshopCS and Flash 5.0.


### 3.1.2. Development Model

This system is composed of people and computers for information collection, storage, knowledge transfer, and use. With the development of science and technology, the expansion of information, and the explosion of knowledge, how to effectively collect information and transfer knowledge has become the development purpose of this system. Therefore, it is imperative to develop a plan that considers the development and operation efficiency and meets the asynchronous real-time processing function. The development of this system uses the MVC model. MVC pattern is the abbreviation of “Model-View-Controller,” translated into Chinese as “Pattern-View-Controller.” That is, an application's input, processing, and output processes are divided into three layers according to model, view, and controller, namely a model layer, a view layer, and a control layer.View represents the user interaction interface. For web applications, it can be summarized as an HTML interface. An application may have many different views. The processing of the MVC design pattern for a view is limited to collecting and processing data on the view and the user’s request, but not the processing of business processes on the view. Handling of business processes is handed over to model processing. Model is the processing of business processes/states and formulating of business rules. The model accepts the data requested by the view and returns the final processing results. Model design is the core of MVC. When receiving requests from the user, the model is matched with the view and the user’s request together without any data processing is completed. The separation of model, view, and controller enables a model to have multiple display views. If the user changes the model’s data through a view controller, all views that depend on these data will change. When data changes, the controller notifies all views of the change. Models, ideas, and controllers are updated synchronously according to their relationships and primary functions [9].The view is in the web tier or client tier in this system. That is, JSP is the page display part. A controller is also in the web tier, implemented by Servlet, the logic part of page display. The model is in the middle tier, implemented with JavaBean on the server side, and implementing logical relationships, as shown in Figure1.Figure 1
The design of the model.


## 3.1.1. Development Tools

The system used JSP (Java Server Pages) technology framework to generate dynamic, interactive web server applications. JSP technology is the traditional web HTML file (∗.htm, ∗.html) into the Java program segment (scriptlet) and JSP tags, resulting in a JSP file (∗.jsp). JSP uses tags and scriptlets to encapsulate the processing logic that generates dynamic web pages, accesses the application logic of resources existing in the server, and separates the web logic from the web design and display. It supports the reusable component-based design and makes developing web-based applications rapid and easy [17].The web server of this system uses Tomcat 5.0 as a Servlet container, which passes page requests to Serverlets and returns the results to users. When a user requests access to a Servlet, it encapsulates the user’s request information in the ServletRequest object, then passes the Request object and Response object to the Servlet requested by the user, and the Servlet writes the results of the response to the ServletResponse object. Then Tomcat transmits the response results to the user.SQL Use server 2000 is d in the background database, and JavaBean is used to connect the database and query and update the data. Using advanced B/S (Browser/Server) architecture, page design uses the current international popular web production tool Dreamweaver 2004 and EditPlus for editing; the script language is JavaScript; page image processing uses PhotoshopCS and Flash 5.0.


## 3.1.2. Development Model

This system is composed of people and computers for information collection, storage, knowledge transfer, and use. With the development of science and technology, the expansion of information, and the explosion of knowledge, how to effectively collect information and transfer knowledge has become the development purpose of this system. Therefore, it is imperative to develop a plan that considers the development and operation efficiency and meets the asynchronous real-time processing function. The development of this system uses the MVC model. MVC pattern is the abbreviation of “Model-View-Controller,” translated into Chinese as “Pattern-View-Controller.” That is, an application's input, processing, and output processes are divided into three layers according to model, view, and controller, namely a model layer, a view layer, and a control layer.View represents the user interaction interface. For web applications, it can be summarized as an HTML interface. An application may have many different views. The processing of the MVC design pattern for a view is limited to collecting and processing data on the view and the user’s request, but not the processing of business processes on the view. Handling of business processes is handed over to model processing. Model is the processing of business processes/states and formulating of business rules. The model accepts the data requested by the view and returns the final processing results. Model design is the core of MVC. When receiving requests from the user, the model is matched with the view and the user’s request together without any data processing is completed. The separation of model, view, and controller enables a model to have multiple display views. If the user changes the model’s data through a view controller, all views that depend on these data will change. When data changes, the controller notifies all views of the change. Models, ideas, and controllers are updated synchronously according to their relationships and primary functions [9].The view is in the web tier or client tier in this system. That is, JSP is the page display part. A controller is also in the web tier, implemented by Servlet, the logic part of page display. The model is in the middle tier, implemented with JavaBean on the server side, and implementing logical relationships, as shown in Figure1.Figure 1
The design of the model.


## 3.2. Design and Implementation of a Resource Center

As a learning website, providing students with as many learning materials and software download as possible is the proper service function of the website. The resource center module of this website includes nine submodules, as shown in Figure2: search module, e-book module, curriculum resource module, software download module, learning material module, free resource area module, the latest resource area module, the hottest resource area module, and other resource areas.Figure 2
The module system of resource center plans.Based on the above module system diagram:Search module: the user inputs keywords, and the system will display the matching records in the database according to fuzzy matching.Electronic book module: provides users free downloads of electronic books, such as PDF, superstar, and other formats, covering all aspects of embedded learning.Curriculum resource module: it lets users download free courses or other courses produced by this website. With the curriculum resource center, students can easily find free learning courseware.Software download module: Module more professional in the embedded aspect can obtain the professional information common download site that does not provide..Updated resource area: the resources provided by this area are the latest with the help of this website.Learning material module: the module mainly provides students with more whole, suitable learning materials, such as related technology frontier articles, excellent students' learning notes, etc.Free resource area: this area provides free downloads to students.The hottest resource zone: the zone aims to make the most popular resources present in a unique position on the website so that students can understand the current trend of resources.Other resource areas: it lets users download a window of resources other than free, up-to-date, and hottest resources to display the site's resources more fully on the page.According to the above description, the logical function of the resource center is clear. First, users select the module after entering the resource center, view the resource information, and then download it online, which can also view the resource's relevant resources and the website's outstanding recommendations.


## 4. Case Study

One of the main applications of improved clustering analysis algorithms is the personalized intelligent learning system of embedded online innovative teaching platforms. GBKM clustering algorithm is used to cluster the students’ scores. According to the clustering analysis results, different learning plans and suggestions are given for international students. At the same time, the results of the analysis are transmitted to the intelligent test paper generation system. The system changes the structure of the following examination paper of the students, truly achieves “individualized teaching”, improves the intelligence of the website, and truly realizes the “personalized” and “intelligence” of online teaching.The page display of performance analysis is designed in JSP and CSS style. GBKM algorithm uses Java to compile JavaBean file from Eclipse environment and calls this bean file by JSP page.In the specific application of the performance analysis system, the GBKM clustering algorithm not only completes the primary function of clustering the relevant data but also completes the analysis of the characteristics of the cluster formed after clustering and how to display it on the page. At the same time, it stores the analysis results in the corresponding database table. The related database table records are updated, such as updating the proportion of each chapter in the testStru table CHAPTERSCORE. For subsequent use, after completing the clustering and analyzing the characteristics of the cluster, the analysis of students’ personality characteristics and the display of various learning suggestions are realized on the JSP page.Calling a bean file in Jsp is done by introducing the package util where the bean file resides. The specific presented method:When calling, a GetData object is created in the analysis package and the object is instantiated. Then call various methods in GetData class are called to implement multiple mechanisms. The code segment is as follows:analyse.GetData gbkm = new analyse.GetData (cido,ido);gbkm.GetDatal ();gbkm.Grid(); System.out.println (“main”); gbkm.preCluster(); gbkm.getPreCluster();out.print (gbkm.ShowOut());The realization of viewing the previous performance analysis results mainly depends on the analysis of the performance analysis result record table. The USERID, COURSE ID, and TEST TIME fields in the table store the student's latest test time for a specific course. These three fields are consistent with the three fields of USERID, COURSE ID, and TEST TIME in the test history information table. The three fields uniquely identify the analysis results of the previous test and obtain the values of these three fields in the JSP page of analysis. The last examination results can be viewed for study and reference by calling the corresponding records in test history.


### 4.1. Database Table Design

This section mainly introduces the database table design involved in the personalized intelligent learning system, including the test paper structure table, the score analysis result record table, and the test history information table.Table1 is the test paper structure table, identified as testStru, and the data source is the change and addition of the background paper structure. This table mainly controls the composition structure of the test paper. CHAPTERSCORE is the proportion of each chapter in the intelligent test paper generation. According to the value of this field, the savvy test paper generation can adjust the structure of the test paper obtained by each savvy test paper generation, namely the proportion of each chapter.Table 1
Structure of examination paper table.
Field nameField ID sectionPropertyLengthPkeyNullSerial numberIDInt4ΟΟCourse numberSUBJECTIDInt4Category nameSTRUTEXTVarchar100Total number of sectionsCHAPTERNUMBERChar3Chapter scoresCHAPTERSCOREVarchar50Table2 is a record of the performance analysis results, which is identified as analysis. The data source is updated after the performance analysis. It records the course number and examination time of the students’ last performance analysis, mainly for calling the corresponding information when the students want to view the results of the previous research.Table 2
The pre-analyze table.
FieldnameField ID sectionPropertyLengthPkeyNullSerial numberIDInt4ΟΟStudent no.USERIDVarchar4ΟCourse numberCOURSEIDVarchar100ΟTest timeTESTTIMDatetime3ΟTable3 is the test history information table identified as test history. The data source is generated automatically by the system after online testing. CHAPTERPER is the proportion of the following test section: CLUSTERED is the student's class number, CLUSTERMEMBER is the class member the student belongs to, and CLUSTERCENTER is the clustering center of the type that the student belongs. SCORE records the examination results, and PNSCORE records each chapter's scores. The table records the information after each examination, including the information obtained from the scoring and the analysis of the result.Table 3
Test history table.
Field nameField IDPropertyLengthPkeyNullSerial numberIDInt4ΟΟTest no.TIDVarchar50Examinee numberUSERIDVarchar20Course numberCIDInt4Test timeTEST TIMEDatetime8Array of test numbersANSWERText16Test scoreSCOREDecimal9Chapter scorePNSCOREVarchar200Class memberCLUSTERMEMBERVarchan200Class-centerCLUSTERCENTERVarchan200Class-markCLUSTERIDVarchar200Section question ratioCHAPTERPERVarchar200Degree of difficultyHARDChan1


### 4.2. Evaluation of Personalized Intelligent Learning System

In the specific application of the GBKM clustering algorithm in the personalized intelligent school system, as the core algorithm of performance analysis, the corresponding records in the database are successfully read, and the GBKM clustering completes analysis effectively. The characteristics of clusters formed after clustering are analyzed, and the results are displayed on the browser page. At the same time, stored the analysis results in the corresponding database table test history and updated the records of the corresponding database table, such as updating the CHAPTERSCORE of each chapter in the testStru table for subsequent use.Multiple GBKM clustering analyses were conducted on 78 test records in the database, and the clustering results were roughly the same, as shown in Table4.Table 4
The clustering of GBKM.
Class-markMembers of the class035, 36, 42, 43, 45, 58, 61, 65, 67, 7418, 26, 30, 31, 32, 33, 34, 37, 38, 44, 46, 47, 49, 50, 51, 52, 54, 55, 56, 59, 60, 62, 64, 66, 69, 70, 71, 72, 81248, 75, 76, 77, 78, 79, 80, 82316, 17, 18, 19, 53, 63, 73, 83, 9347, 9, 10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25, 27, 28, 29, 39, 40, 41, 57, 68By comparing the characteristics of each class, the course members are very similar to those of the class. In contrast, the differences between the course and the style are significant, and each type’s clustering center can represent each class's characteristics. Due to the fast and effective clustering and analysis of the GBKM clustering algorithm, the system can understand the factors of each student’s grace and give personalized learning suggestions and guidance according to this feature, increase the system’s intelligence, and improve students’ learning efficiency and effect. Taking “Introduction to Journalism” as an example, this article compares a series of data. The change of data can better and more intuitively see the transformation of test paper parameters, concluding that the evolution of test paper parameters impacts students’ learning.Firstly, the proportion of original questions in this course is given and then this information is stored in the database table testStru (Table5).Table 5
Original question’s proportion of every chapter.
The section12345678Proportion of questions per chapter (%)1010201515101010When students do not perform performance analysis, the structure of the test paper, i.e., the number of points in each chapter, is carried out in following Table6. The system will set the number of questions according to the specified percentage and will not increase or decrease the number of questions considering students' factors.Table 6
Student’s score of every chapter.
Students IDExamination of timeScores per chapter00012021-5-301@9.5#2@9.0#3@16.5#4@11.0#5@11.5#6@9.0#7@7.0#8@9.0#00012021-6-111@6.0#2@7.5#3@15.0#4@12.5#5@11.5#6@8.5#7@7.5#8@9.5#00012021-10-81@6.5#2@10.0#3@19.5#4@14.0#5@13.5#6@9.5#7@8.0#8@10.0#00022021-6-121@6.3#2@7.6#3@15.4#4@12.0#5@12.0#6@10.0#7@9.0#8@6.0#00032021-6-121@8.3#2@9.2#3@19.5#4@15.0#5@14.5#6@8.5#7@9.5#8@8.0#00042021-6-121@8.5#2@9.0#3@17.0#4@13.0#5@12.5#6@6.5#7@6.5#8@8.0#Observe the scores of each chapter derived from the database test history table, as shown in Table6. In this table, the scores of each chapter are expressed in the form of “1 @ 9.5 # 2 @ 9.0 # 3 @ 16.5 # 4 @ 11.0 # 5 @ 11.5 # 6 @ 9.0 # 7 @ 7.0 # 8 @ 9.0 #,” where @ is the number of chapters and the equal score of this chapter, for example, 1 @ 9.5 is the score of the first chapter 9.5; # is separated from chapters, such as 1 @ 9.5 # 2 @ 9.0, # is the first chapter, # is the second chapter. In Table 6, the test scores of different students and each student at different times are not the same. That is, different and the same students have different degrees of mastery of knowledge at different times. Suppose all the questions are with the proportion of questions in Table 5. In that case, the examination will lead to different levels of students with different foundations to test the same topic. The same student always repeats the examination of familiar knowledge points, and unfamiliar knowledge points may not be tested. Therefore, how to change the proportion of questions for different students at different test times to achieve targeted testing has become the focus of the personalized intelligence of this system. Therefore, after the performance analysis, the intelligent learning system will change the proportion of each chapter of the students according to the analysis results. For example, Table 7 shows the proportion of each class chapter after cluster analysis. As Table 6, 1 @ 11% # 2 @ 8% means that the proportion of the first chapter is 11% and the second chapter is 8%.Table 7
Question’s proportion of every chapter after clustering.
Kind IDProportion of each chapterClass size01@11%#2@8%#3@18%#4@13%#5@18%6@12%7@12%8@10%#1011@10%#2@9%#3@20%#4@16%#5@18%#6@12%#7@8%#8@7%#2921@11%#2@11%#3@19%#4@14%#5@16%#6@7%#7@11%8#@11%#831@12%#2@9%#3@18.5%#4@13.5%#5@15%#6@9%#7@15%#8@9%#941@7.9%#2@7%#3@21.7%#4@16.3%#5@15%#6@11%#7@10.6%#8@10.5%#22Table8 shows the classes of different students and the proportion of each chapter. From this table, we can see the differences among students and their different balance of questions. The system can carry out individualized intelligent education for international students.Table 8
Different student’s cluster.
Student IDKind IDProportion of each chapterAdmin41@7.9%#2@7%#3@21.7%#4@16.3%#5@15%#6@11%#7@10.6%#8@10.5%#000901@11%#2@8%#3@18%#4@13%#5@18%6@12%7@12%8@10%#002031@12%#2@9%#3@18.5%#4@13.5%#5@15%#6@9%#7@15%#8@9%#001901@11%#2@8%#3@18%#4@13%#5@18%6@12%7@12%8@10%#002121@11%#2@11%#3@19%#4@14%#5@16%#6@7%#7@11%8#@11%#000711@10%#2@9%#3@20%#4@16%#5@18%#6@12%#7@8%#8@7%#000811@10%#2@9%#3@20%#4@16%#5@18%#6@12%#7@8%#8@7%#Table9 shows that students have achieved different results at different test times, belonging to different categories and proportions of questions. Over time, the students’ knowledge of each chapter also changes. The ratio of students’ questions in each chapter also changes, which realizes the function of individual questions.Table 9
Question’s proportion of every chapter of 0009.
Examination of timeKind IDQuestion proportion2021-6-1241@10%#2@9%#3@20%#4@16%#5@18%#6@12%#7@8%#8@7%#2021-6-1201@7.9%#2@7%#3@21.7%#4@16.3%#5@15%#6@11%#7@10.6%#8@10.5%#2021-6-1211@11%#2@8%#3@18%#4@13%#5@18%6@12%7@12%8@10%#From the above analysis, the GBKM algorithm is applied to the personalized intelligent learning system and the implementation of personalized philosophical teaching for different students in different periods. It dramatically enhances the intelligence of embedded online innovative teaching platforms.


## 4.1. Database Table Design

This section mainly introduces the database table design involved in the personalized intelligent learning system, including the test paper structure table, the score analysis result record table, and the test history information table.Table1 is the test paper structure table, identified as testStru, and the data source is the change and addition of the background paper structure. This table mainly controls the composition structure of the test paper. CHAPTERSCORE is the proportion of each chapter in the intelligent test paper generation. According to the value of this field, the savvy test paper generation can adjust the structure of the test paper obtained by each savvy test paper generation, namely the proportion of each chapter.Table 1
Structure of examination paper table.
Field nameField ID sectionPropertyLengthPkeyNullSerial numberIDInt4ΟΟCourse numberSUBJECTIDInt4Category nameSTRUTEXTVarchar100Total number of sectionsCHAPTERNUMBERChar3Chapter scoresCHAPTERSCOREVarchar50Table2 is a record of the performance analysis results, which is identified as analysis. The data source is updated after the performance analysis. It records the course number and examination time of the students’ last performance analysis, mainly for calling the corresponding information when the students want to view the results of the previous research.Table 2
The pre-analyze table.
FieldnameField ID sectionPropertyLengthPkeyNullSerial numberIDInt4ΟΟStudent no.USERIDVarchar4ΟCourse numberCOURSEIDVarchar100ΟTest timeTESTTIMDatetime3ΟTable3 is the test history information table identified as test history. The data source is generated automatically by the system after online testing. CHAPTERPER is the proportion of the following test section: CLUSTERED is the student's class number, CLUSTERMEMBER is the class member the student belongs to, and CLUSTERCENTER is the clustering center of the type that the student belongs. SCORE records the examination results, and PNSCORE records each chapter's scores. The table records the information after each examination, including the information obtained from the scoring and the analysis of the result.Table 3
Test history table.
Field nameField IDPropertyLengthPkeyNullSerial numberIDInt4ΟΟTest no.TIDVarchar50Examinee numberUSERIDVarchar20Course numberCIDInt4Test timeTEST TIMEDatetime8Array of test numbersANSWERText16Test scoreSCOREDecimal9Chapter scorePNSCOREVarchar200Class memberCLUSTERMEMBERVarchan200Class-centerCLUSTERCENTERVarchan200Class-markCLUSTERIDVarchar200Section question ratioCHAPTERPERVarchar200Degree of difficultyHARDChan1


## 4.2. Evaluation of Personalized Intelligent Learning System

In the specific application of the GBKM clustering algorithm in the personalized intelligent school system, as the core algorithm of performance analysis, the corresponding records in the database are successfully read, and the GBKM clustering completes analysis effectively. The characteristics of clusters formed after clustering are analyzed, and the results are displayed on the browser page. At the same time, stored the analysis results in the corresponding database table test history and updated the records of the corresponding database table, such as updating the CHAPTERSCORE of each chapter in the testStru table for subsequent use.Multiple GBKM clustering analyses were conducted on 78 test records in the database, and the clustering results were roughly the same, as shown in Table4.Table 4
The clustering of GBKM.
Class-markMembers of the class035, 36, 42, 43, 45, 58, 61, 65, 67, 7418, 26, 30, 31, 32, 33, 34, 37, 38, 44, 46, 47, 49, 50, 51, 52, 54, 55, 56, 59, 60, 62, 64, 66, 69, 70, 71, 72, 81248, 75, 76, 77, 78, 79, 80, 82316, 17, 18, 19, 53, 63, 73, 83, 9347, 9, 10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25, 27, 28, 29, 39, 40, 41, 57, 68By comparing the characteristics of each class, the course members are very similar to those of the class. In contrast, the differences between the course and the style are significant, and each type’s clustering center can represent each class's characteristics. Due to the fast and effective clustering and analysis of the GBKM clustering algorithm, the system can understand the factors of each student’s grace and give personalized learning suggestions and guidance according to this feature, increase the system’s intelligence, and improve students’ learning efficiency and effect. Taking “Introduction to Journalism” as an example, this article compares a series of data. The change of data can better and more intuitively see the transformation of test paper parameters, concluding that the evolution of test paper parameters impacts students’ learning.Firstly, the proportion of original questions in this course is given and then this information is stored in the database table testStru (Table5).Table 5
Original question’s proportion of every chapter.
The section12345678Proportion of questions per chapter (%)1010201515101010When students do not perform performance analysis, the structure of the test paper, i.e., the number of points in each chapter, is carried out in following Table6. The system will set the number of questions according to the specified percentage and will not increase or decrease the number of questions considering students' factors.Table 6
Student’s score of every chapter.
Students IDExamination of timeScores per chapter00012021-5-301@9.5#2@9.0#3@16.5#4@11.0#5@11.5#6@9.0#7@7.0#8@9.0#00012021-6-111@6.0#2@7.5#3@15.0#4@12.5#5@11.5#6@8.5#7@7.5#8@9.5#00012021-10-81@6.5#2@10.0#3@19.5#4@14.0#5@13.5#6@9.5#7@8.0#8@10.0#00022021-6-121@6.3#2@7.6#3@15.4#4@12.0#5@12.0#6@10.0#7@9.0#8@6.0#00032021-6-121@8.3#2@9.2#3@19.5#4@15.0#5@14.5#6@8.5#7@9.5#8@8.0#00042021-6-121@8.5#2@9.0#3@17.0#4@13.0#5@12.5#6@6.5#7@6.5#8@8.0#Observe the scores of each chapter derived from the database test history table, as shown in Table6. In this table, the scores of each chapter are expressed in the form of “1 @ 9.5 # 2 @ 9.0 # 3 @ 16.5 # 4 @ 11.0 # 5 @ 11.5 # 6 @ 9.0 # 7 @ 7.0 # 8 @ 9.0 #,” where @ is the number of chapters and the equal score of this chapter, for example, 1 @ 9.5 is the score of the first chapter 9.5; # is separated from chapters, such as 1 @ 9.5 # 2 @ 9.0, # is the first chapter, # is the second chapter. In Table 6, the test scores of different students and each student at different times are not the same. That is, different and the same students have different degrees of mastery of knowledge at different times. Suppose all the questions are with the proportion of questions in Table 5. In that case, the examination will lead to different levels of students with different foundations to test the same topic. The same student always repeats the examination of familiar knowledge points, and unfamiliar knowledge points may not be tested. Therefore, how to change the proportion of questions for different students at different test times to achieve targeted testing has become the focus of the personalized intelligence of this system. Therefore, after the performance analysis, the intelligent learning system will change the proportion of each chapter of the students according to the analysis results. For example, Table 7 shows the proportion of each class chapter after cluster analysis. As Table 6, 1 @ 11% # 2 @ 8% means that the proportion of the first chapter is 11% and the second chapter is 8%.Table 7
Question’s proportion of every chapter after clustering.
Kind IDProportion of each chapterClass size01@11%#2@8%#3@18%#4@13%#5@18%6@12%7@12%8@10%#1011@10%#2@9%#3@20%#4@16%#5@18%#6@12%#7@8%#8@7%#2921@11%#2@11%#3@19%#4@14%#5@16%#6@7%#7@11%8#@11%#831@12%#2@9%#3@18.5%#4@13.5%#5@15%#6@9%#7@15%#8@9%#941@7.9%#2@7%#3@21.7%#4@16.3%#5@15%#6@11%#7@10.6%#8@10.5%#22Table8 shows the classes of different students and the proportion of each chapter. From this table, we can see the differences among students and their different balance of questions. The system can carry out individualized intelligent education for international students.Table 8
Different student’s cluster.
Student IDKind IDProportion of each chapterAdmin41@7.9%#2@7%#3@21.7%#4@16.3%#5@15%#6@11%#7@10.6%#8@10.5%#000901@11%#2@8%#3@18%#4@13%#5@18%6@12%7@12%8@10%#002031@12%#2@9%#3@18.5%#4@13.5%#5@15%#6@9%#7@15%#8@9%#001901@11%#2@8%#3@18%#4@13%#5@18%6@12%7@12%8@10%#002121@11%#2@11%#3@19%#4@14%#5@16%#6@7%#7@11%8#@11%#000711@10%#2@9%#3@20%#4@16%#5@18%#6@12%#7@8%#8@7%#000811@10%#2@9%#3@20%#4@16%#5@18%#6@12%#7@8%#8@7%#Table9 shows that students have achieved different results at different test times, belonging to different categories and proportions of questions. Over time, the students’ knowledge of each chapter also changes. The ratio of students’ questions in each chapter also changes, which realizes the function of individual questions.Table 9
Question’s proportion of every chapter of 0009.
Examination of timeKind IDQuestion proportion2021-6-1241@10%#2@9%#3@20%#4@16%#5@18%#6@12%#7@8%#8@7%#2021-6-1201@7.9%#2@7%#3@21.7%#4@16.3%#5@15%#6@11%#7@10.6%#8@10.5%#2021-6-1211@11%#2@8%#3@18%#4@13%#5@18%6@12%7@12%8@10%#From the above analysis, the GBKM algorithm is applied to the personalized intelligent learning system and the implementation of personalized philosophical teaching for different students in different periods. It dramatically enhances the intelligence of embedded online innovative teaching platforms.


## 5. Conclusion

Because the teaching mode of international news communication is too single, the teaching mode and implementation method are improved. The application of cluster analysis algorithms in teaching is studied in depth. GBKM clustering analysis algorithm is proposed to improve the traditional clustering analysis algorithm. Standard tests verify the convergence and effectiveness of the GBKM clustering analysis algorithm, which is successfully applied to the personalized intelligent learning system of an embedded online smart teaching platform. The main innovative work of this article is as follows:(1)
By studying the implementation of the online teaching platform and related technologies, the concept of “personalized intelligent teaching is proposed,” the embedded online intelligent teaching platform is realized using various popular technologies, and the overall style and architecture of the online teaching platform are designed asd.(2)
The clustering analysis algorithm is studied, and an improved clustering analysis algorithm—GBKM clustering analysis algorithm—is proposed, which overcomes the shortcomings of the traditional clustering analysis algorithm and demonstrates the effectiveness and superiority of the algorithm ared through theoretical proof and experimental verification.(3)
The improved clustering analysis algorithm is applied to the embedded online intelligent teaching platform to realize the personalized, innovative learning system of the embedded online smart teaching platform. It reflects the individualized and brilliant teaching concept and dramatically enhances the intelligence of the embedded online intelligent teaching platform. The application of the system also proves the effectiveness and practicability of the GBKM algorithm. It optimized the teaching mode of international news communication.



---

*Source: 2120264-2022-08-29.xml*