# Structural Analysis of PE Teaching Strategy and System Knowledge Management Based on Mobile Digital Information Technology

Authors: Fan Yang; Yu Shan
Journal: Mobile Information Systems
 (2022)

---

## Abstract

In order to solve the physical education strategy problem of mobile digital information technology, a structure analysis and research method of systematic knowledge management department is proposed. First of all, the arrival and continuous maturity of the economic era has provided technical support for the development of many fields, especially the progress of information technology to strengthen the advantages of information management. Now the information management has gradually risen to the development stage of knowledge management. Secondly, there are related group learning and teachers’ personal knowledge management questionnaire survey. The statistical results of 178 valid questionnaires collected showed that 100% of teachers admitted that they had participated in different forms and types of group learning, and 91% of preschool teachers believed that they had a basic fixed time and content of group learning. However, 64.2 percent of teachers admitted that they did not consider personal knowledge management at all. For what is a personal knowledge management, 43.8% of the teachers are only heard of the term “personal knowledge management” but do not know the specific meaning. 17.4% of the teachers had heard of it and knew something about it. Only 3.4% of teachers clearly understood the meaning of personal knowledge management, and 35.4% had never heard of the concept of personal knowledge management, let alone the application of personal knowledge management strategies. The results show that how to excavate the structure of knowledge management of sports subject systematically through the innovation of PE teaching strategy, and excavate tacit knowledge and comprehensive explicit knowledge more deeply is the focus of this study. Therefore, from the perspective of knowledge management, an educational information resource management system is proposed, which can classify subject knowledge effectively and intelligently by means of mobile information technology.


---

## Body

## 1. Introduction

The training mode is a kind of teaching knowledge and skills developed according to the current curriculum. It focuses on the integration of curriculum, equity and curriculum information, while giving priority to solving high-level problems.  Special attention should be paid to the application of knowledge and skills in physical education. Through a comprehensive process, knowledge management, and multidisciplinary teaching, students can gain a deeper understanding of the subject and build relationships in their path to success. The physical education curriculum should focus on knowledge management, and the curriculum should focus on improving driving skills, thinking, and integrity while focusing on learning in organization. We should start with physical education materials and organize physical education classes based on the main points of the curriculum. New teaching strategies should be integrated into physical education textbooks. Therefore, the purpose of this paper is to help the teaching and knowledge management of physical education courses by developing an education management record and use system so that all available data can be obtained. See Figure1.Figure 1
Physical education teaching strategy of mobile digital information technology.


## 2. Literature Review

A large number of experiments show that in the tide of new curriculum reform ideas, the ideas throughout the physical education curriculum are “student-oriented”, “to promote the initiative development of students” new ideas. Research-based learning, cooperative learning, doing middle school, and other sports teaching methods rise one after another, becoming the trend of sports teaching reform in the new era. These teaching methods all emphasize the students’ participation in activities and the cooperation and communication between students, so as to carry on the perception, understanding, and innovation of motor skills [1]. That is to emphasize the acquisition of students’ subjective experience and accumulation of individuals and also to emphasize the integration of explicit knowledge and tacit knowledge. This is where the value of knowledge management lies.As early as 1999, Ba and Qi [2] put forward the physical education teaching strategy of mobile digital information technology, thus starting the study of school domain knowledge management [2]. In 2000, Warnars published the report Knowledge Management in the Learning Society, clearly pointing out that education is a Knowledge business. In 2002, Zhang and Jasimuddin [3] wrote a monograph on Knowledge Management in Education, Knowledge Management in Education, which promoted the in-depth research in this field [3].According to the current research, the research of mobile digital information technology PE training concept and management knowledge is planned. Concerning the concept of knowledge management, various studies and various disciplines lead to differences in knowledge management, but still lack good understanding. Some scholars believe that the concept of knowledge management needs to be clarified and implemented in order to help people understand the meaning of knowledge management. Knowledge management is the effective identification, acquisition, storage, exchange, and application of knowledge. It is a way to use skills to transform and share knowledge about sexuality and to improve the organization’s ability to adapt and innovate [4, 5]. The goal of knowledge management is to eliminate secret knowledge, enrich certain knowledge, share knowledge through collaborative learning, turn all members’ secret knowledge into knowledge, gather, and thus apply new knowledge.


## 3. Construction of Subject Knowledge Intelligent Inquiry System Based on Mobile Information Technology

### 3.1. Knowledge Database Content Publishing and Retrieval

Define associations between various channels and background databases (one channel corresponds to one database or view): In the channel management interface of the TRS WAS management Console, click Add Channel, enter the channel name: biomedical Engineering, and then enter the corresponding values in the server IP address, server port, user account, and password. As for the template (template file to control the performance of the entire page) of the choice, because the system has its own overview, combination retrieval template is not completely corresponding to the new database, so we need to modify the template [6].Web module is a Web application server based on HTTP server. It automatically accesses the background database system according to the settings of the TRS management console and the access request of the client (Web browser), and dynamically organizes the information into Web pages and sends it to the client. TRS WAS uses the template technology to control the output format of web pages. The template itself is IN HTML format. You can use any HTML making tool to design the template, and the description of database information in the template can reach the field level [7].Input ‘biomechanics’ in the search box of ‘full text search’ on the TRS WAS40 release interface for unextended search, and the results are 11 records, 2 of which are related: 80% to 100%, 4 of which are related between 40% and 80%, 2 of which are related between 0%, and 3 of which are related between 0% and 40% [8]. Then, also entering biomechanics, select extended search, which results in 323 records. We can see that in addition to ‘biomechanics’, the results also contain the following words: ‘Compressive strength; Elastic; Friction; Dynamics; Ascension; Flexibility; Stress, Physics: Tensile strength: torque’. The records of vibration: load ‘were retrieved’. Similarly, enter the word “nano” in the search box of “full text search”, and the results contain three records. The correlation of the three records is 100%, 73%, and 43%, respectively. The results are sorted by the word frequency of the hit word and as correlation [9].Here are many key technologies for intelligent information retrieval technology on the network [10]. For example, text and nontext knowledge mining is described in this paper. Semiformatted Web pages and documents stored in HTML format are the main organizational forms of Internet information at present. Text knowledge mining is to discover the implicit rules in it, so as to realize the intellectualization of Internet knowledge mining [11]. Without text knowledge mining, intellectualization cannot be realized [12]. The most commonly used text knowledge mining method is based on document feature vector space model, as shown in Figure 2.Figure 2
CVSM model.


### 3.2. HTML Page Text Intelligent Search Classification and Query

Data distribution is the process of dividing large amounts of data into one or more categories based on their content and context [13]. Automatic data sharing is an important part of data processing tools in a wide range of mailing applications, electronic conference, information filtering, and so on. The following network text classification technology and its implementation algorithm adopt intelligent knowledge mining technology, which combines artificial intelligence with database technology and uses computers to intelligently classify network text data [14]. Since most of the information on the Internet is not in the form of text, the use of text classification technology will greatly improve the efficiency of people’s use of network information resources [2].


#### 3.2.1. Network Text Data Classification Algorithm

First, document classification algorithm based on concept.Let the text be divided intoN categories, as shown in the following formula:(1)D=D′UD″,where A is the training text set and D is the text to be classified. The method compares the text to be classified with the text gravity center of each category and selects the category with the maximum similarity [15].Set the text asn class and the vector of class C is shown in the following formula:(2)C=α1,α2,…,αm.Documents to be classified represent the following formula:(3)d=ω1,ω2,…,ωm.Its similarity is shown in the following formula:(4)simd,c=1dc∑j=1mαjωj.The document category of the maximum value ofsimd,c is the document category to which D belongs.Second, Bayesian classification method.The Bayesian classification method classifies text data according to the value size. As shown in the following formula:(5)c′=argmaxlogPrcn+∑i=1mPrxidlogPrxi/cPrxi/d,where xi refers to the ith feature of c-type documents, Prxi/c is the probability of obtaining the feature word xi from C-type texts, Prxi/d is the probability of obtaining D is the special word in the text, n is the number of words in D, and M is the size of the dictionary. If the received initial value is higher than the initial value, the letter D is subdivided into C, otherwise it is incorrect. From the measurement results, the Bayesian distribution can be defined as follows [16].Assume that the vector fraction of dataD is the frequency of the occurrence of the specific word corresponding to the data, followed by the probability of D. Belonging to class C document is shown in the following:(6)pcd=pc∏⋅xj∈vpxj/cTFxj⋅d∑ipc∏⋅xj∈vpxj/cTFxj⋅d,(7)pxjc=1+TFxj,cv+∑iTFxj,c.pxj/c A is the Laplacian probability estimate of the conditional probability of feature word and occurrence in class C documents, TFxj,c is the frequency of occurrence of feature word xj in class C documents, TFxj,d is the frequency of occurrence of feature word xj in class D documents, and v is the total number of different features contained in the document representation.Third, k-nearest adjacency classification algorithm.The algorithm determines the category of the guest file through the probability distribution of the category of K documents closest to the documentd [17]. The probability that document D belongs to class C is shown in the following formula:(8)pcd=∑i=1ksimd,dipc/di∑j∑i=1ksimd,dipcj/di.Fourth, the classification method of conceptual inference network based on semantic network.The conceptual inference network uses information gain to measure the core word, that is, a word is considered as a core word when its category membership is greater than a predetermined min value. The evaluation function of core words is shown in the following formula:(9)InfGainF=PF∑iPψiF¯logPψi/FPψi+PF¯∑iPψiF¯logPψi/F¯Pψi.F is a word; P(F) is the probability of occurrence of word F;F¯ means that word F does not occur; Pψi/F is the probability of occurrence of type ψi , and Pψi/F is the probability of occurrence of ψi when word F occurs. InfGian is defined as category membership degree. When it is greater than a given idle value, it is considered as a core word. The specific semantic correlation degree is defined in the following formula:(10)coni,jDk=coni,jDkfreqFiDk+freqFjDk,(11)coni,jψi=∑kconi,jDkmaxi,j∑kconi,jDk.The larger the value ofconi,jψi is, the higher the semantic correlation degree of Fi and Fj in category ψi is. Similarly, the support degree of words Fi and Fj in category ψi document Dk is shown in the following formula:(12)supi,jDk=freqFiDk+freqFjDk∑kfreqFlDk,(13)supi,jψi=∑ksupi,jDkmaxi,jsupi,jDk.According to various relations in the inference network, nodes in the network are given different membership degrees and weightsW, and the concept of core node is set as shown in the following formula:(14)Cii=1,2,…,k.The corresponding adjacent node is child, and the association between the document and the category is shown in the following formula:(15)∑i=1k1ki×InfGain×∑i=1kiAi1×ωi1.Finally, category 1 of the following formula can be evaluated as the document category, as shown in the following formula:(16)maxq=1m∑i=1k1ki×InfGain×∑i=1kiAi1×ωi1.Concept-based text clustering compares the text to be classified with each category text center, and the text with the greatest likelihood is considered to belong to this category [18]. This algorithm has fast response speed and is easy to calculate. Because probability density is used as the weight, it has higher precision than pure speech class comparison. Both Bayesian classification and K-nearest adjacency classification algorithms are based on VSM (vector space model), which determine the attribution of text vectors according to the distance between them [19]. Because these algorithms fail to consider the interaction between feature vectors in the vector model, the classification accuracy is not very ideal. The conceptual inference network based on semantic network uses the relationship between key concepts and other concepts to simulate the process of human classification and achieve text classification. Traditional VSM-based classification algorithms use Euclidean distance cosine distance inner product similarity test [20]. Conceptual inference network takes into account the interference of useless features in vector space to distance calculation and the internal relationship between core concept words and their related words. The algorithm based on core concept has higher accuracy than distance calculation.


## 3.1. Knowledge Database Content Publishing and Retrieval

Define associations between various channels and background databases (one channel corresponds to one database or view): In the channel management interface of the TRS WAS management Console, click Add Channel, enter the channel name: biomedical Engineering, and then enter the corresponding values in the server IP address, server port, user account, and password. As for the template (template file to control the performance of the entire page) of the choice, because the system has its own overview, combination retrieval template is not completely corresponding to the new database, so we need to modify the template [6].Web module is a Web application server based on HTTP server. It automatically accesses the background database system according to the settings of the TRS management console and the access request of the client (Web browser), and dynamically organizes the information into Web pages and sends it to the client. TRS WAS uses the template technology to control the output format of web pages. The template itself is IN HTML format. You can use any HTML making tool to design the template, and the description of database information in the template can reach the field level [7].Input ‘biomechanics’ in the search box of ‘full text search’ on the TRS WAS40 release interface for unextended search, and the results are 11 records, 2 of which are related: 80% to 100%, 4 of which are related between 40% and 80%, 2 of which are related between 0%, and 3 of which are related between 0% and 40% [8]. Then, also entering biomechanics, select extended search, which results in 323 records. We can see that in addition to ‘biomechanics’, the results also contain the following words: ‘Compressive strength; Elastic; Friction; Dynamics; Ascension; Flexibility; Stress, Physics: Tensile strength: torque’. The records of vibration: load ‘were retrieved’. Similarly, enter the word “nano” in the search box of “full text search”, and the results contain three records. The correlation of the three records is 100%, 73%, and 43%, respectively. The results are sorted by the word frequency of the hit word and as correlation [9].Here are many key technologies for intelligent information retrieval technology on the network [10]. For example, text and nontext knowledge mining is described in this paper. Semiformatted Web pages and documents stored in HTML format are the main organizational forms of Internet information at present. Text knowledge mining is to discover the implicit rules in it, so as to realize the intellectualization of Internet knowledge mining [11]. Without text knowledge mining, intellectualization cannot be realized [12]. The most commonly used text knowledge mining method is based on document feature vector space model, as shown in Figure 2.Figure 2
CVSM model.


## 3.2. HTML Page Text Intelligent Search Classification and Query

Data distribution is the process of dividing large amounts of data into one or more categories based on their content and context [13]. Automatic data sharing is an important part of data processing tools in a wide range of mailing applications, electronic conference, information filtering, and so on. The following network text classification technology and its implementation algorithm adopt intelligent knowledge mining technology, which combines artificial intelligence with database technology and uses computers to intelligently classify network text data [14]. Since most of the information on the Internet is not in the form of text, the use of text classification technology will greatly improve the efficiency of people’s use of network information resources [2].


### 3.2.1. Network Text Data Classification Algorithm

First, document classification algorithm based on concept.Let the text be divided intoN categories, as shown in the following formula:(1)D=D′UD″,where A is the training text set and D is the text to be classified. The method compares the text to be classified with the text gravity center of each category and selects the category with the maximum similarity [15].Set the text asn class and the vector of class C is shown in the following formula:(2)C=α1,α2,…,αm.Documents to be classified represent the following formula:(3)d=ω1,ω2,…,ωm.Its similarity is shown in the following formula:(4)simd,c=1dc∑j=1mαjωj.The document category of the maximum value ofsimd,c is the document category to which D belongs.Second, Bayesian classification method.The Bayesian classification method classifies text data according to the value size. As shown in the following formula:(5)c′=argmaxlogPrcn+∑i=1mPrxidlogPrxi/cPrxi/d,where xi refers to the ith feature of c-type documents, Prxi/c is the probability of obtaining the feature word xi from C-type texts, Prxi/d is the probability of obtaining D is the special word in the text, n is the number of words in D, and M is the size of the dictionary. If the received initial value is higher than the initial value, the letter D is subdivided into C, otherwise it is incorrect. From the measurement results, the Bayesian distribution can be defined as follows [16].Assume that the vector fraction of dataD is the frequency of the occurrence of the specific word corresponding to the data, followed by the probability of D. Belonging to class C document is shown in the following:(6)pcd=pc∏⋅xj∈vpxj/cTFxj⋅d∑ipc∏⋅xj∈vpxj/cTFxj⋅d,(7)pxjc=1+TFxj,cv+∑iTFxj,c.pxj/c A is the Laplacian probability estimate of the conditional probability of feature word and occurrence in class C documents, TFxj,c is the frequency of occurrence of feature word xj in class C documents, TFxj,d is the frequency of occurrence of feature word xj in class D documents, and v is the total number of different features contained in the document representation.Third, k-nearest adjacency classification algorithm.The algorithm determines the category of the guest file through the probability distribution of the category of K documents closest to the documentd [17]. The probability that document D belongs to class C is shown in the following formula:(8)pcd=∑i=1ksimd,dipc/di∑j∑i=1ksimd,dipcj/di.Fourth, the classification method of conceptual inference network based on semantic network.The conceptual inference network uses information gain to measure the core word, that is, a word is considered as a core word when its category membership is greater than a predetermined min value. The evaluation function of core words is shown in the following formula:(9)InfGainF=PF∑iPψiF¯logPψi/FPψi+PF¯∑iPψiF¯logPψi/F¯Pψi.F is a word; P(F) is the probability of occurrence of word F;F¯ means that word F does not occur; Pψi/F is the probability of occurrence of type ψi , and Pψi/F is the probability of occurrence of ψi when word F occurs. InfGian is defined as category membership degree. When it is greater than a given idle value, it is considered as a core word. The specific semantic correlation degree is defined in the following formula:(10)coni,jDk=coni,jDkfreqFiDk+freqFjDk,(11)coni,jψi=∑kconi,jDkmaxi,j∑kconi,jDk.The larger the value ofconi,jψi is, the higher the semantic correlation degree of Fi and Fj in category ψi is. Similarly, the support degree of words Fi and Fj in category ψi document Dk is shown in the following formula:(12)supi,jDk=freqFiDk+freqFjDk∑kfreqFlDk,(13)supi,jψi=∑ksupi,jDkmaxi,jsupi,jDk.According to various relations in the inference network, nodes in the network are given different membership degrees and weightsW, and the concept of core node is set as shown in the following formula:(14)Cii=1,2,…,k.The corresponding adjacent node is child, and the association between the document and the category is shown in the following formula:(15)∑i=1k1ki×InfGain×∑i=1kiAi1×ωi1.Finally, category 1 of the following formula can be evaluated as the document category, as shown in the following formula:(16)maxq=1m∑i=1k1ki×InfGain×∑i=1kiAi1×ωi1.Concept-based text clustering compares the text to be classified with each category text center, and the text with the greatest likelihood is considered to belong to this category [18]. This algorithm has fast response speed and is easy to calculate. Because probability density is used as the weight, it has higher precision than pure speech class comparison. Both Bayesian classification and K-nearest adjacency classification algorithms are based on VSM (vector space model), which determine the attribution of text vectors according to the distance between them [19]. Because these algorithms fail to consider the interaction between feature vectors in the vector model, the classification accuracy is not very ideal. The conceptual inference network based on semantic network uses the relationship between key concepts and other concepts to simulate the process of human classification and achieve text classification. Traditional VSM-based classification algorithms use Euclidean distance cosine distance inner product similarity test [20]. Conceptual inference network takes into account the interference of useless features in vector space to distance calculation and the internal relationship between core concept words and their related words. The algorithm based on core concept has higher accuracy than distance calculation.


## 3.2.1. Network Text Data Classification Algorithm

First, document classification algorithm based on concept.Let the text be divided intoN categories, as shown in the following formula:(1)D=D′UD″,where A is the training text set and D is the text to be classified. The method compares the text to be classified with the text gravity center of each category and selects the category with the maximum similarity [15].Set the text asn class and the vector of class C is shown in the following formula:(2)C=α1,α2,…,αm.Documents to be classified represent the following formula:(3)d=ω1,ω2,…,ωm.Its similarity is shown in the following formula:(4)simd,c=1dc∑j=1mαjωj.The document category of the maximum value ofsimd,c is the document category to which D belongs.Second, Bayesian classification method.The Bayesian classification method classifies text data according to the value size. As shown in the following formula:(5)c′=argmaxlogPrcn+∑i=1mPrxidlogPrxi/cPrxi/d,where xi refers to the ith feature of c-type documents, Prxi/c is the probability of obtaining the feature word xi from C-type texts, Prxi/d is the probability of obtaining D is the special word in the text, n is the number of words in D, and M is the size of the dictionary. If the received initial value is higher than the initial value, the letter D is subdivided into C, otherwise it is incorrect. From the measurement results, the Bayesian distribution can be defined as follows [16].Assume that the vector fraction of dataD is the frequency of the occurrence of the specific word corresponding to the data, followed by the probability of D. Belonging to class C document is shown in the following:(6)pcd=pc∏⋅xj∈vpxj/cTFxj⋅d∑ipc∏⋅xj∈vpxj/cTFxj⋅d,(7)pxjc=1+TFxj,cv+∑iTFxj,c.pxj/c A is the Laplacian probability estimate of the conditional probability of feature word and occurrence in class C documents, TFxj,c is the frequency of occurrence of feature word xj in class C documents, TFxj,d is the frequency of occurrence of feature word xj in class D documents, and v is the total number of different features contained in the document representation.Third, k-nearest adjacency classification algorithm.The algorithm determines the category of the guest file through the probability distribution of the category of K documents closest to the documentd [17]. The probability that document D belongs to class C is shown in the following formula:(8)pcd=∑i=1ksimd,dipc/di∑j∑i=1ksimd,dipcj/di.Fourth, the classification method of conceptual inference network based on semantic network.The conceptual inference network uses information gain to measure the core word, that is, a word is considered as a core word when its category membership is greater than a predetermined min value. The evaluation function of core words is shown in the following formula:(9)InfGainF=PF∑iPψiF¯logPψi/FPψi+PF¯∑iPψiF¯logPψi/F¯Pψi.F is a word; P(F) is the probability of occurrence of word F;F¯ means that word F does not occur; Pψi/F is the probability of occurrence of type ψi , and Pψi/F is the probability of occurrence of ψi when word F occurs. InfGian is defined as category membership degree. When it is greater than a given idle value, it is considered as a core word. The specific semantic correlation degree is defined in the following formula:(10)coni,jDk=coni,jDkfreqFiDk+freqFjDk,(11)coni,jψi=∑kconi,jDkmaxi,j∑kconi,jDk.The larger the value ofconi,jψi is, the higher the semantic correlation degree of Fi and Fj in category ψi is. Similarly, the support degree of words Fi and Fj in category ψi document Dk is shown in the following formula:(12)supi,jDk=freqFiDk+freqFjDk∑kfreqFlDk,(13)supi,jψi=∑ksupi,jDkmaxi,jsupi,jDk.According to various relations in the inference network, nodes in the network are given different membership degrees and weightsW, and the concept of core node is set as shown in the following formula:(14)Cii=1,2,…,k.The corresponding adjacent node is child, and the association between the document and the category is shown in the following formula:(15)∑i=1k1ki×InfGain×∑i=1kiAi1×ωi1.Finally, category 1 of the following formula can be evaluated as the document category, as shown in the following formula:(16)maxq=1m∑i=1k1ki×InfGain×∑i=1kiAi1×ωi1.Concept-based text clustering compares the text to be classified with each category text center, and the text with the greatest likelihood is considered to belong to this category [18]. This algorithm has fast response speed and is easy to calculate. Because probability density is used as the weight, it has higher precision than pure speech class comparison. Both Bayesian classification and K-nearest adjacency classification algorithms are based on VSM (vector space model), which determine the attribution of text vectors according to the distance between them [19]. Because these algorithms fail to consider the interaction between feature vectors in the vector model, the classification accuracy is not very ideal. The conceptual inference network based on semantic network uses the relationship between key concepts and other concepts to simulate the process of human classification and achieve text classification. Traditional VSM-based classification algorithms use Euclidean distance cosine distance inner product similarity test [20]. Conceptual inference network takes into account the interference of useless features in vector space to distance calculation and the internal relationship between core concept words and their related words. The algorithm based on core concept has higher accuracy than distance calculation.


## 4. Analysis of PE Teaching Strategy and Systematic Knowledge Structure Management

### 4.1. Knowledge Structure Management

#### 4.1.1. Constituent Elements

The elements of knowledge management are schematically described by a model, as shown in Figure3.Figure 3
Components of knowledge management.We make a distinction between data and information and knowledge and wisdom, as shown in Figure4.(1)
Data: Data are discrete objective facts about events, which constitute the basis of information and knowledge, and have no inherent meaning in themselves [21].(2)
Information: Information is structured and systematic data. It is meaningful data after classifying, analyzing, and summarizing data. Information is an important part of knowledge, which can be formed by connecting, organizing, and comparing with other information [22].(3)
Knowledge: Knowledge is a dynamic process of development. It is the dynamic cognition of the objective world which the subject processes information on the basis of practice and develops infinitely.(4)
Wisdom: The ability to use information and knowledge effectively. People acquire wisdom when they master and apply knowledge. See Figure4.Figure 4
The relationship among data, information, knowledge and wisdom.


### 4.2. Optimization of Knowledge Management Technology in Organizational Management

The practice of knowledge management cannot be separated from the promotion of technology, which is a tool to promote the extraction, consolidation, and dissemination of knowledge. When applying business management knowledge, the use of technology through the exchange of knowledge makes it easier and faster to communicate with internal staff and partners of the industry [23]. The development of information technology has given information and liability for knowledge management, improving the exchange of human information, expanding the collection of information, and the rapid exchange of information. All of this creates a good environment for developing and implementing knowledge and is an integral part of equipment and management knowledge. For example, in the transformation phase of intelligence, information and communication technologies such as video conferencing and e-mail strengthen human communication and support the process of transforming hiding knowledge into knowledge [24]. Computer simulation technology and virtual reality technology can be used as a bridge for real world communication.Discovery of educational knowledge EKDD is the whole process of extracting valuable knowledge from information technology. This is a topic discussed in database technology and machine learning. Knowledge-Based Research is a multi-step process that includes the following steps, as shown in Figure5.Figure 5
Flowchart of educational knowledge discovery.OLAM is a combination of online analytical processing and data mining [25]. Online analytical processing is based on multidimensional view, which focuses on linking a large amount of relevant information directly from different information sources in the data warehouse according to existing patterns to give analysts a clear and consistent view, emphasizing execution efficiency and quick response to users, and its direct data source is generally the data warehouse. Mining technology is based on a variety of data sources and focuses on discovering useful patterns hidden in the deep layer of original data for learners. Generally, execution efficiency and response speed are not considered too much [26]. Therefore, OLAM has the linearity, flexibility, and depth of data processing of multidimensional analysis, which is suitable for the organization of network information resources with large amount of data, complex information types, and various forms of expression.


### 4.3. Architecture Design of KMIS System Based on Knowledge Grid

#### 4.3.1. Architectural Model

The system will consist of a central node and N district-level server clusters, and the whole system will connect different district-level clusters to the central grid portal server via LAN or Internet [27]. Here are three roles in the system: system user, grid central node portal server (knowledge base server), and district cluster portal server (database server). See Figure 6.Figure 6
KMIS architecture based on knowledge grid.We use bottom-up and top-down software modeling methods to divide the system into three layers: knowledge resource layer, structured knowledge and heterogeneous knowledge collection, and preliminary processing, located at the bottom of the architecture, which includes: KMS database, knowledge cases, video files, and other computing, storage, network resources. The middle layer is the grid service layer, which is the core of KMIS and provides ontology knowledge base service, knowledge ontology construction service, and security policy service. See Figure7.Figure 7
KMIS three-layer framework system based on KG.


#### 4.3.2. The Man-Machine Layer

The core part of the man-machine layer is the intelligent browser, which provides a powerful visual interface and strengthens the personalized search and knowledge recommendation function by using Cookie technology. It is aimed at four different levels of users: general users, decision makers, management, and employees, with different levels of users having different access rights to the smart browser. Intelligent browser has the following general functions for all users: personalized search; knowledge recommendation; knowledge metalinks.


#### 4.3.3. Grid Service Layer

The grid service layer is the core of KMIS and its main function is to express, redefine, interoperate, and store domain knowledge using knowledge ontology. The grid service layer avoids the direct interaction between knowledge users and knowledge resources, and users do not need to care about the form and location of knowledge resources. The temporary change of knowledge resources will not have immediate impact on users’ decisions, thus enhancing the stability of the system. The grid service layer can be divided into ontology knowledge base service, knowledge ontology construction service, and security policy service. See Figure8.Figure 8
KMIS grid resource layer architecture.


#### 4.3.4. Knowledge Resource Layer

KMS knowledge source is the foundation of KMIS construction. The data in KMS database is well structured, so it does not need great work intensity. A series of knowledge rules can be obtained by redefining and redescribing each relatively independent module in the original KMS through indirect integration technology. These knowledge rules are stored in the knowledge rule base and play a guiding role in data mining. The knowledge after mining processing can conform to such knowledge rules. The knowledge of knowledge mart can be realized by data mining algorithm. In addition, the knowledge community on the LAN or the information on the Web is different from the information in the database, it is mainly a large number of heterogeneous information resources, the document structure is poor, and its storage is mostly semistructured and unstructured data. Because such types of data structures are difficult to be clearly represented by data models, some potential knowledge rules can be found through some algorithms of data mining. It represents the whole service process of knowledge resource layer data mining. See Figure9.Figure 9
Data mining services.


#### 4.3.5. Knowledge Element Design and Knowledge Element Link

Knowledge of historical theory, knowledge of database history, and knowledge of connectivity play an important role in the development of KMIS. The essence of knowledge is the minimum of freedom of knowledge that reflects the laws of microscopy, such as circumstances, human life, and scientific knowledge. A knowledge meta-database is a knowledge base that is independent, impacted by knowledge gained from knowledge resources after developing content knowledge. Therefore, knowledge meta-database is knowledge-oriented. Each knowledge element automatically forms a knowledge association network through the knowledge element link. Different link methods constitute different knowledge expression, which makes the knowledge meta-database not only used for basic knowledge learning but also used to support the study and research of the discipline and interdisciplinary, and provides a prerequisite guarantee for the realization of interconnection and communication of ontology knowledge base through the knowledge meta-database. By dividing the organizational knowledge structure into the smallest units that can be used independently, the department general knowledge meta-structure is constructed. Knowledge element forms knowledge chain through knowledge link, which is the link to form the whole knowledge structure. See Figure10.Figure 10
Knowledge chain.


#### 4.3.6. Knowledge Storage Intensifies and Innovation Spirals

Knowledge innovation is produced in the process of knowledge flow and is the most effective way to expand knowledge resources. Some scholars divide the transformation of knowledge into four different modes: (1) socialization, from latent knowledge to latent knowledge; (2) externalization, the transformation of secret knowledge into specialized knowledge; (3) the general concept, the transformation of some knowledge into specialized knowledge; (4) inner knowledge, from special knowledge to secret knowledge. See Figure11.Figure 11
Knowledge transformation mode.


### 4.4. System Function

The functional requirements of resource management system in the technical specifications for educational resource construction mainly include three subsystems: (1) resource management; (2) system management; (3) resource service and use communication. In order to meet the basic functional requirements of the resource management system, this paper designs the architecture of the educational information resource management system from the perspective of management as shown in Figure12.Figure 12
Architecture of educational information resource management system.The resource management subsystem is mainly responsible for navigation, browsing, retrieval, classification, uploading, input, review and release, sorting and reclassification, download, and so on.


#### 4.4.1. Navigation through

Navigation browsing refers to the operation guidance of the system for users. In order for users to quickly and accurately find the resources they need, we can present the resources according to their purpose, such as media material bank, question bank, case bank, and lesson bank.


#### 4.4.2. Resources Retrieval

The educational information resource retrieval module is divided into three parts: basic retrieval, advanced retrieval, and resource library. Basic search is a fuzzy search method. Users can search for required resources based on keywords set during resource storage, such as resource name, author, keyword, and resource attribute. The advanced retrieval part provides more retrieval information for users to choose. During operation, the logical relationship between resource metadata information is improved through the selection of resource retrieval items, and the precision of searching resources is increased. Resource library search is categorized by discipline, resource type, resource format, grade level, and so on.


#### 4.4.3. Resource Registration Upload Module

The resource registration module in the education information resource management system provides users with a complete page of registered resources, which can be divided into three categories: basic information, advanced information, and other information. Each category also explains the metadata information of educational information resources.


#### 4.4.4. The Resource Classification

In the education information resource management system, resources are applied to primary schools, junior high schools, senior high schools, and universities. Types of resources are materials, pictures, sounds, courseware, etc. Subject classifications are Chinese, mathematics, English, physics, as well as subject resources classification.


#### 4.4.5. Resource Entry

The standardized input of educational information resources is the basis of effective management of resources by resource management system. In the process of inputting educational information resources, metadata information of resources should be standardized description and stored in detail, so as to facilitate users’ retrieval of resources and provide users with various forms and acquisition methods to find resources. The standardization of educational information resource input is the key step of resource sharing.


#### 4.4.6. Resource Audit

The resources uploaded by users are automatically marked to be audited and stored in the temporary repository for audit by resource auditors of all disciplines. The system administrator confirms the resources to be audited and uploaded in the temporary resource repository. After cataloging the qualified resources, the resources are stored in the repository. The system automatically deletes unqualified resources to ensure resource reliability.


#### 4.4.7. Resources Downloading

Users can find the target resource by searching the resource according to the actual needs. For the convenience of users, the system provides download function. Users can download the resources stored in the resource management system to their local computers for free, increasing the frequency of resource usage.


#### 4.4.8. Resource Collation and Reclassification

The main purpose of collating educational information resources is to check the accuracy of resource description.


## 4.1. Knowledge Structure Management

### 4.1.1. Constituent Elements

The elements of knowledge management are schematically described by a model, as shown in Figure3.Figure 3
Components of knowledge management.We make a distinction between data and information and knowledge and wisdom, as shown in Figure4.(1)
Data: Data are discrete objective facts about events, which constitute the basis of information and knowledge, and have no inherent meaning in themselves [21].(2)
Information: Information is structured and systematic data. It is meaningful data after classifying, analyzing, and summarizing data. Information is an important part of knowledge, which can be formed by connecting, organizing, and comparing with other information [22].(3)
Knowledge: Knowledge is a dynamic process of development. It is the dynamic cognition of the objective world which the subject processes information on the basis of practice and develops infinitely.(4)
Wisdom: The ability to use information and knowledge effectively. People acquire wisdom when they master and apply knowledge. See Figure4.Figure 4
The relationship among data, information, knowledge and wisdom.


## 4.1.1. Constituent Elements

The elements of knowledge management are schematically described by a model, as shown in Figure3.Figure 3
Components of knowledge management.We make a distinction between data and information and knowledge and wisdom, as shown in Figure4.(1)
Data: Data are discrete objective facts about events, which constitute the basis of information and knowledge, and have no inherent meaning in themselves [21].(2)
Information: Information is structured and systematic data. It is meaningful data after classifying, analyzing, and summarizing data. Information is an important part of knowledge, which can be formed by connecting, organizing, and comparing with other information [22].(3)
Knowledge: Knowledge is a dynamic process of development. It is the dynamic cognition of the objective world which the subject processes information on the basis of practice and develops infinitely.(4)
Wisdom: The ability to use information and knowledge effectively. People acquire wisdom when they master and apply knowledge. See Figure4.Figure 4
The relationship among data, information, knowledge and wisdom.


## 4.2. Optimization of Knowledge Management Technology in Organizational Management

The practice of knowledge management cannot be separated from the promotion of technology, which is a tool to promote the extraction, consolidation, and dissemination of knowledge. When applying business management knowledge, the use of technology through the exchange of knowledge makes it easier and faster to communicate with internal staff and partners of the industry [23]. The development of information technology has given information and liability for knowledge management, improving the exchange of human information, expanding the collection of information, and the rapid exchange of information. All of this creates a good environment for developing and implementing knowledge and is an integral part of equipment and management knowledge. For example, in the transformation phase of intelligence, information and communication technologies such as video conferencing and e-mail strengthen human communication and support the process of transforming hiding knowledge into knowledge [24]. Computer simulation technology and virtual reality technology can be used as a bridge for real world communication.Discovery of educational knowledge EKDD is the whole process of extracting valuable knowledge from information technology. This is a topic discussed in database technology and machine learning. Knowledge-Based Research is a multi-step process that includes the following steps, as shown in Figure5.Figure 5
Flowchart of educational knowledge discovery.OLAM is a combination of online analytical processing and data mining [25]. Online analytical processing is based on multidimensional view, which focuses on linking a large amount of relevant information directly from different information sources in the data warehouse according to existing patterns to give analysts a clear and consistent view, emphasizing execution efficiency and quick response to users, and its direct data source is generally the data warehouse. Mining technology is based on a variety of data sources and focuses on discovering useful patterns hidden in the deep layer of original data for learners. Generally, execution efficiency and response speed are not considered too much [26]. Therefore, OLAM has the linearity, flexibility, and depth of data processing of multidimensional analysis, which is suitable for the organization of network information resources with large amount of data, complex information types, and various forms of expression.


## 4.3. Architecture Design of KMIS System Based on Knowledge Grid

### 4.3.1. Architectural Model

The system will consist of a central node and N district-level server clusters, and the whole system will connect different district-level clusters to the central grid portal server via LAN or Internet [27]. Here are three roles in the system: system user, grid central node portal server (knowledge base server), and district cluster portal server (database server). See Figure 6.Figure 6
KMIS architecture based on knowledge grid.We use bottom-up and top-down software modeling methods to divide the system into three layers: knowledge resource layer, structured knowledge and heterogeneous knowledge collection, and preliminary processing, located at the bottom of the architecture, which includes: KMS database, knowledge cases, video files, and other computing, storage, network resources. The middle layer is the grid service layer, which is the core of KMIS and provides ontology knowledge base service, knowledge ontology construction service, and security policy service. See Figure7.Figure 7
KMIS three-layer framework system based on KG.


### 4.3.2. The Man-Machine Layer

The core part of the man-machine layer is the intelligent browser, which provides a powerful visual interface and strengthens the personalized search and knowledge recommendation function by using Cookie technology. It is aimed at four different levels of users: general users, decision makers, management, and employees, with different levels of users having different access rights to the smart browser. Intelligent browser has the following general functions for all users: personalized search; knowledge recommendation; knowledge metalinks.


### 4.3.3. Grid Service Layer

The grid service layer is the core of KMIS and its main function is to express, redefine, interoperate, and store domain knowledge using knowledge ontology. The grid service layer avoids the direct interaction between knowledge users and knowledge resources, and users do not need to care about the form and location of knowledge resources. The temporary change of knowledge resources will not have immediate impact on users’ decisions, thus enhancing the stability of the system. The grid service layer can be divided into ontology knowledge base service, knowledge ontology construction service, and security policy service. See Figure8.Figure 8
KMIS grid resource layer architecture.


### 4.3.4. Knowledge Resource Layer

KMS knowledge source is the foundation of KMIS construction. The data in KMS database is well structured, so it does not need great work intensity. A series of knowledge rules can be obtained by redefining and redescribing each relatively independent module in the original KMS through indirect integration technology. These knowledge rules are stored in the knowledge rule base and play a guiding role in data mining. The knowledge after mining processing can conform to such knowledge rules. The knowledge of knowledge mart can be realized by data mining algorithm. In addition, the knowledge community on the LAN or the information on the Web is different from the information in the database, it is mainly a large number of heterogeneous information resources, the document structure is poor, and its storage is mostly semistructured and unstructured data. Because such types of data structures are difficult to be clearly represented by data models, some potential knowledge rules can be found through some algorithms of data mining. It represents the whole service process of knowledge resource layer data mining. See Figure9.Figure 9
Data mining services.


### 4.3.5. Knowledge Element Design and Knowledge Element Link

Knowledge of historical theory, knowledge of database history, and knowledge of connectivity play an important role in the development of KMIS. The essence of knowledge is the minimum of freedom of knowledge that reflects the laws of microscopy, such as circumstances, human life, and scientific knowledge. A knowledge meta-database is a knowledge base that is independent, impacted by knowledge gained from knowledge resources after developing content knowledge. Therefore, knowledge meta-database is knowledge-oriented. Each knowledge element automatically forms a knowledge association network through the knowledge element link. Different link methods constitute different knowledge expression, which makes the knowledge meta-database not only used for basic knowledge learning but also used to support the study and research of the discipline and interdisciplinary, and provides a prerequisite guarantee for the realization of interconnection and communication of ontology knowledge base through the knowledge meta-database. By dividing the organizational knowledge structure into the smallest units that can be used independently, the department general knowledge meta-structure is constructed. Knowledge element forms knowledge chain through knowledge link, which is the link to form the whole knowledge structure. See Figure10.Figure 10
Knowledge chain.


### 4.3.6. Knowledge Storage Intensifies and Innovation Spirals

Knowledge innovation is produced in the process of knowledge flow and is the most effective way to expand knowledge resources. Some scholars divide the transformation of knowledge into four different modes: (1) socialization, from latent knowledge to latent knowledge; (2) externalization, the transformation of secret knowledge into specialized knowledge; (3) the general concept, the transformation of some knowledge into specialized knowledge; (4) inner knowledge, from special knowledge to secret knowledge. See Figure11.Figure 11
Knowledge transformation mode.


## 4.3.1. Architectural Model

The system will consist of a central node and N district-level server clusters, and the whole system will connect different district-level clusters to the central grid portal server via LAN or Internet [27]. Here are three roles in the system: system user, grid central node portal server (knowledge base server), and district cluster portal server (database server). See Figure 6.Figure 6
KMIS architecture based on knowledge grid.We use bottom-up and top-down software modeling methods to divide the system into three layers: knowledge resource layer, structured knowledge and heterogeneous knowledge collection, and preliminary processing, located at the bottom of the architecture, which includes: KMS database, knowledge cases, video files, and other computing, storage, network resources. The middle layer is the grid service layer, which is the core of KMIS and provides ontology knowledge base service, knowledge ontology construction service, and security policy service. See Figure7.Figure 7
KMIS three-layer framework system based on KG.


## 4.3.2. The Man-Machine Layer

The core part of the man-machine layer is the intelligent browser, which provides a powerful visual interface and strengthens the personalized search and knowledge recommendation function by using Cookie technology. It is aimed at four different levels of users: general users, decision makers, management, and employees, with different levels of users having different access rights to the smart browser. Intelligent browser has the following general functions for all users: personalized search; knowledge recommendation; knowledge metalinks.


## 4.3.3. Grid Service Layer

The grid service layer is the core of KMIS and its main function is to express, redefine, interoperate, and store domain knowledge using knowledge ontology. The grid service layer avoids the direct interaction between knowledge users and knowledge resources, and users do not need to care about the form and location of knowledge resources. The temporary change of knowledge resources will not have immediate impact on users’ decisions, thus enhancing the stability of the system. The grid service layer can be divided into ontology knowledge base service, knowledge ontology construction service, and security policy service. See Figure8.Figure 8
KMIS grid resource layer architecture.


## 4.3.4. Knowledge Resource Layer

KMS knowledge source is the foundation of KMIS construction. The data in KMS database is well structured, so it does not need great work intensity. A series of knowledge rules can be obtained by redefining and redescribing each relatively independent module in the original KMS through indirect integration technology. These knowledge rules are stored in the knowledge rule base and play a guiding role in data mining. The knowledge after mining processing can conform to such knowledge rules. The knowledge of knowledge mart can be realized by data mining algorithm. In addition, the knowledge community on the LAN or the information on the Web is different from the information in the database, it is mainly a large number of heterogeneous information resources, the document structure is poor, and its storage is mostly semistructured and unstructured data. Because such types of data structures are difficult to be clearly represented by data models, some potential knowledge rules can be found through some algorithms of data mining. It represents the whole service process of knowledge resource layer data mining. See Figure9.Figure 9
Data mining services.


## 4.3.5. Knowledge Element Design and Knowledge Element Link

Knowledge of historical theory, knowledge of database history, and knowledge of connectivity play an important role in the development of KMIS. The essence of knowledge is the minimum of freedom of knowledge that reflects the laws of microscopy, such as circumstances, human life, and scientific knowledge. A knowledge meta-database is a knowledge base that is independent, impacted by knowledge gained from knowledge resources after developing content knowledge. Therefore, knowledge meta-database is knowledge-oriented. Each knowledge element automatically forms a knowledge association network through the knowledge element link. Different link methods constitute different knowledge expression, which makes the knowledge meta-database not only used for basic knowledge learning but also used to support the study and research of the discipline and interdisciplinary, and provides a prerequisite guarantee for the realization of interconnection and communication of ontology knowledge base through the knowledge meta-database. By dividing the organizational knowledge structure into the smallest units that can be used independently, the department general knowledge meta-structure is constructed. Knowledge element forms knowledge chain through knowledge link, which is the link to form the whole knowledge structure. See Figure10.Figure 10
Knowledge chain.


## 4.3.6. Knowledge Storage Intensifies and Innovation Spirals

Knowledge innovation is produced in the process of knowledge flow and is the most effective way to expand knowledge resources. Some scholars divide the transformation of knowledge into four different modes: (1) socialization, from latent knowledge to latent knowledge; (2) externalization, the transformation of secret knowledge into specialized knowledge; (3) the general concept, the transformation of some knowledge into specialized knowledge; (4) inner knowledge, from special knowledge to secret knowledge. See Figure11.Figure 11
Knowledge transformation mode.


## 4.4. System Function

The functional requirements of resource management system in the technical specifications for educational resource construction mainly include three subsystems: (1) resource management; (2) system management; (3) resource service and use communication. In order to meet the basic functional requirements of the resource management system, this paper designs the architecture of the educational information resource management system from the perspective of management as shown in Figure12.Figure 12
Architecture of educational information resource management system.The resource management subsystem is mainly responsible for navigation, browsing, retrieval, classification, uploading, input, review and release, sorting and reclassification, download, and so on.


### 4.4.1. Navigation through

Navigation browsing refers to the operation guidance of the system for users. In order for users to quickly and accurately find the resources they need, we can present the resources according to their purpose, such as media material bank, question bank, case bank, and lesson bank.


### 4.4.2. Resources Retrieval

The educational information resource retrieval module is divided into three parts: basic retrieval, advanced retrieval, and resource library. Basic search is a fuzzy search method. Users can search for required resources based on keywords set during resource storage, such as resource name, author, keyword, and resource attribute. The advanced retrieval part provides more retrieval information for users to choose. During operation, the logical relationship between resource metadata information is improved through the selection of resource retrieval items, and the precision of searching resources is increased. Resource library search is categorized by discipline, resource type, resource format, grade level, and so on.


### 4.4.3. Resource Registration Upload Module

The resource registration module in the education information resource management system provides users with a complete page of registered resources, which can be divided into three categories: basic information, advanced information, and other information. Each category also explains the metadata information of educational information resources.


### 4.4.4. The Resource Classification

In the education information resource management system, resources are applied to primary schools, junior high schools, senior high schools, and universities. Types of resources are materials, pictures, sounds, courseware, etc. Subject classifications are Chinese, mathematics, English, physics, as well as subject resources classification.


### 4.4.5. Resource Entry

The standardized input of educational information resources is the basis of effective management of resources by resource management system. In the process of inputting educational information resources, metadata information of resources should be standardized description and stored in detail, so as to facilitate users’ retrieval of resources and provide users with various forms and acquisition methods to find resources. The standardization of educational information resource input is the key step of resource sharing.


### 4.4.6. Resource Audit

The resources uploaded by users are automatically marked to be audited and stored in the temporary repository for audit by resource auditors of all disciplines. The system administrator confirms the resources to be audited and uploaded in the temporary resource repository. After cataloging the qualified resources, the resources are stored in the repository. The system automatically deletes unqualified resources to ensure resource reliability.


### 4.4.7. Resources Downloading

Users can find the target resource by searching the resource according to the actual needs. For the convenience of users, the system provides download function. Users can download the resources stored in the resource management system to their local computers for free, increasing the frequency of resource usage.


### 4.4.8. Resource Collation and Reclassification

The main purpose of collating educational information resources is to check the accuracy of resource description.


## 4.4.1. Navigation through

Navigation browsing refers to the operation guidance of the system for users. In order for users to quickly and accurately find the resources they need, we can present the resources according to their purpose, such as media material bank, question bank, case bank, and lesson bank.


## 4.4.2. Resources Retrieval

The educational information resource retrieval module is divided into three parts: basic retrieval, advanced retrieval, and resource library. Basic search is a fuzzy search method. Users can search for required resources based on keywords set during resource storage, such as resource name, author, keyword, and resource attribute. The advanced retrieval part provides more retrieval information for users to choose. During operation, the logical relationship between resource metadata information is improved through the selection of resource retrieval items, and the precision of searching resources is increased. Resource library search is categorized by discipline, resource type, resource format, grade level, and so on.


## 4.4.3. Resource Registration Upload Module

The resource registration module in the education information resource management system provides users with a complete page of registered resources, which can be divided into three categories: basic information, advanced information, and other information. Each category also explains the metadata information of educational information resources.


## 4.4.4. The Resource Classification

In the education information resource management system, resources are applied to primary schools, junior high schools, senior high schools, and universities. Types of resources are materials, pictures, sounds, courseware, etc. Subject classifications are Chinese, mathematics, English, physics, as well as subject resources classification.


## 4.4.5. Resource Entry

The standardized input of educational information resources is the basis of effective management of resources by resource management system. In the process of inputting educational information resources, metadata information of resources should be standardized description and stored in detail, so as to facilitate users’ retrieval of resources and provide users with various forms and acquisition methods to find resources. The standardization of educational information resource input is the key step of resource sharing.


## 4.4.6. Resource Audit

The resources uploaded by users are automatically marked to be audited and stored in the temporary repository for audit by resource auditors of all disciplines. The system administrator confirms the resources to be audited and uploaded in the temporary resource repository. After cataloging the qualified resources, the resources are stored in the repository. The system automatically deletes unqualified resources to ensure resource reliability.


## 4.4.7. Resources Downloading

Users can find the target resource by searching the resource according to the actual needs. For the convenience of users, the system provides download function. Users can download the resources stored in the resource management system to their local computers for free, increasing the frequency of resource usage.


## 4.4.8. Resource Collation and Reclassification

The main purpose of collating educational information resources is to check the accuracy of resource description.


## 5. Conclusion

Curriculum development is an important part of developing educational resources. This is a major project that focuses on the acquisition, sharing, and updating of educational materials. Based on the current situation of the development of educational information, the development of information management system will be designed to provide educational information, disseminate information about confidential learning, improve the understanding and function of educational materials, and provide a system based on knowledge management. From the perspective of knowledge management, based on the concept of knowledge management, this article presents some ideas to create a curriculum. According to the relevant construction standards, a model of curriculum management information has been prepared. The goal is to maximize the effectiveness of the management of educational information, establish a basis for the use of all educational information, understand the quality and current nature of the information, improve educational materials, and do not publish information. The first concept of the research was to design and implement a comprehensive data management system to support the sharing of educational data, hidden resources, and other applicable issues. However, due to the level of knowledge and expertise of the author, it was not developed in practice, but only a model of curriculum management information management. Because of further improvement of the system, the author will continue to study them in the future and try to complete the system.



---

*Source: 2877927-2022-08-10.xml*