# Intelligent Campus Resource Sharing System Based on Data Fusion Sensor

Authors: Guangshun Huang
Journal: Journal of Sensors
 (2022)

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## Abstract

In order to make full use of the large amount of teaching data existing in the data center, the author proposes a mining algorithm using classification technology and association rules in data mining technology; the teaching quality evaluation data and student achievement data were mined. By constructing the C4.5 decision tree algorithm, this method taps the potential links between teachers’ professional titles, degrees, ages, and teaching quality evaluation results, through correlation analysis theory; the correlation between professional courses was mined from the student’s course achievement database, and some reasonable and reliable course association rules were obtained. Experimental results show that the correct rate of C4.5 decision tree algorithm under different sampling times is above 80%. The conclusions drawn have certain guiding significance for college teaching and personnel training.


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## Body

## 1. Introduction

As the foundation of a country’s development and innovation, education and scientific research is receiving more and more attention from the whole society. With the continuous progress of information technology, integrating new information technology into the information construction of campus, it not only disseminates advanced educational and scientific research achievements but also improves educational achievements and accelerates the scientific process.The so-called smart campus is in order to improve the utilization rate of school resources, a campus management method that applies the campus card in the smart campus to access control management, public housing use, and links with scientific research teams [1]. The use of intelligent and informatized campus management can more effectively obtain campus information, campus information sharing, and services and realize an integrated and all-round intelligent corresponding system. It is convenient for the students and the management of the school. First of all, the smart campus can provide teachers and students with a fast and convenient information service platform and can formulate some services based on teachers and students, leaving some space for teachers and students to play freely [2]. Secondly, the information services relying on cloud computing and the network are integrated into various fields to achieve the purpose of information sharing and, furthermore, from the perspective of information sharing, provide a comprehensive information service platform for the teachers and students of the school to connect and communicate with the outside world. Smart campus is an advanced stage of education informatization; information technology has greatly improved the service level and ability of teaching, scientific research, management, service, and other fields and has unique advantages [3]. At present, the construction of smart campuses in my country is being stepped up, and many educational institutions and campuses are participating in the wave of smart campus construction.


## 2. Literature Review

In the “Twelfth Five-Year Plan”, Zhejiang University constructively put forward a blueprint for a “smart campus,” which depicts ubiquitous online learning, convenient and thoughtful campus life, transparent and efficient school governance, integration of innovative online scientific research, and colorful campus culture. The construction of Nanjing University of Posts and Telecommunications on the “smart campus” is based on the Internet of Things, with various application service systems as the carrier to build a new intelligent work, study, and living environment that integrates teaching, scientific research, management, and campus life. It is believed that “smart campus” should have three core characteristics: (1) provide teachers and students with a full range of intelligent perception environment and comprehensive information service platform, and provide personalized services; (2) integrate network-based information services into various applications and service areas of the campus to achieve interconnection and collaboration; (3) using the intelligent perception of the environment and the comprehensive information service platform, provide an interface for communication and perception between the school and the outside world.With the evolution of ubiquitous computing, some smart campus prototypes have been developed. For example, Booker and Jabbour proposed that a smart card can be used in the campus to access the services in the campus [4]. Li et al. proposed the ETHOC system, which uses various devices such as mobile phones or PADs to make the system itself support virtual peers with printed documents [5]. Dargan et al. proposed that a “smart campus” should have the following levels: first, sensors can sense, capture, and transmit information about equipment, people, resources, etc.; the second is the perception, capture, and transmission of learners’ individual characteristics such as learning preferences, attention states, learning styles, cognitive characteristics, and learning situations such as learning time, activities, space, and partners [6].With the advancement of digital campus construction in colleges and universities, application systems of various functions are constantly being built. In order to solve the current situation of the information island between various application systems in the school, in the first phase of digital campus construction, a shared data center was established to realize data sharing and synchronization between various systems [7]. At present, data centers have accumulated rich business data and have grown rapidly. There is a lot of important information hidden behind the proliferating data; if there is no means to discover the relationships and rules existing in the data, it is impossible to predict the future development trend based on the existing data, which will inevitably lead to “data explosion but poor knowledge” phenomenon. How to effectively manage and organize these data and convert data into knowledge to assist university administrators in making the next decision, this is an urgent problem to be solved; it is also how to further analyze and mine the data of various business information systems after the completion of the digital campus, so as to provide decision support for teaching management.Data mining technology provides a good solution for this [8]. Data mining is to extract from a large number of incomplete, noisy, fuzzy, and random practical application data, which is hidden in it and people do not know in advance, but it is also a process of potentially useful information and knowledge as shown in Figure 1 for the data security management framework [9]. The application of data mining in digital campus is undoubtedly of practical significance. It will become the trend of college informatization.Figure 1
Data security management framework.


## 3. Research Methods

### 3.1. Overall Framework of Shared Data Center

Data plays a very important role in the normal development of college teaching. In order to improve the daily office efficiency of the school, various departments have successively built their own information systems. However, information is managed independently, and there is no data interconnection and data sharing, a university officially started the construction of a digital campus in November 2018, and the construction of the shared data center platform began in the first phase of the digital campus. After three years of construction, the function of the shared data center has become more and more complete, and the amount of data has become more and more complete; based on the three main lines of integration of teacher information, student information and asset information, data integration, and information sharing are realized.All data in the data center has a unique data source; taking teacher information as an example, the source of its generation is the personnel management system; in order to ensure the consistency of the data, other systems have blocked the authority to modify the basic information of teachers; the data center will regularly extract the changed faculty and staff information from the personnel management system and push it to other application systems to achieve data sharing and synchronization. Similarly, the source of student information is the educational administration system, only the educational administration system can maintain student information, and other systems use student information and cannot maintain student information.The shared data center stores the shared data extracted from various application systems, such as the basic information of personnel extracted from the personnel management system, the teaching workload information, student performance data, teaching evaluation data extracted from the educational administration system, and the thesis results, patents, and reward information extracted from the scientific research system. These data are currently only simple summary and statistics without further analysis and utilization of the data. In view of this, we have selected teachers and students as the theme and discuss the in-depth application of data mining technology in shared data centers.


### 3.2. Application of Classification Technology in Teaching Quality Evaluation and Analysis

Online evaluation of the teaching quality of teachers is an important means of teaching quality monitoring. At present, the system has accumulated a large amount of evaluation data and uses classification algorithms to construct decision trees [10], it can excavate the relationship between teachers’ educational background, professional title, age and other attributes, and evaluation results and apply the research results to practice to provide more help for teaching managers.


#### 3.2.1. Overview of Decision Tree Algorithm

Decision tree is an instance-based inductive learning algorithm; it infers classification rules in a decision tree representation from a set of unordered, random tuples [11]. A decision tree consists of three parts: nodes, branches, and leaves; nodes represent attributes, leaf nodes represent categories, and the topmost node of the tree is the root node, a path from the root node to the leaf node forms a classification rule, which is widely used. At present, a variety of decision tree algorithms have been formed, such as CLS, ID3, CHAID, CART, FACT, C4.5, GINI, SEE5, SLIQ, and SPRINT [12]. One of the most famous algorithms is the ID3 algorithm proposed by J.R. Quinlan in the “Induction of Decision Trees” paper in 1986 and the improved C4.5 algorithm in 1993. The C4.5 algorithm is an improved version of the ID3 algorithm; it uses the gain ratio to overcome the insufficiency of selecting attributes with many values when selecting attributes with information gain; during the tree construction process or after the construction is completed, pruning is performed; the discrete processing of continuous attributes can be completed, able to handle incomplete data, and can eventually form production rules [13, 14].Figure2 shows a simple decision tree classification model.Figure 2
Simple decision tree model.


#### 3.2.2. Data Acquisition and Preprocessing

This application research mainly analyzes the relationship between teachers’ basic situation and evaluation results and establishes an excellent teacher model, so that schools can have an exact basis for teacher incentives. The research process uses data from two aspects: the basic situation of teachers and the results of teaching evaluation in the second semester of the 2019-2020 school year; the data structure is shown in Tables1 and 2.Table 1
Basic information of teachers.
Employee numberGenderAgeEducationJob titlePart100033Woman39PostgraduateAssociate professorBusiness school100047Man48PostgraduateProfessorIT academy100056Man41UndergraduateAssociate professorLaw school100105Man46PhD studentProfessorLaw school100111Woman35PostgraduateLecturerSchool of humanities100116Woman48UndergraduateAssociate professorSchool of education100105Man46PhD studentAssociate professorSchool of geography………………Table 2
Evaluation results of teachers’ classroom teaching quality.
Employee numberSchool yearSemesterEvaluation of teachers/%Evaluation level1000332019~2020193.864Excellent1000472019~2020194.278Excellent1000562019~2020189.656Good1001052019~2020178.754Middle1001112019~2020189.235Good……………


#### 3.2.3. Teaching Quality Evaluation Model Based on C4.5 Decision Tree

Assume that the training dataset containsm categories, namely, T=t1,t2,⋯,tm. An attribute in the training dataset may have a total of k values of a1,a2,⋯,ak, according to the attribute is divided into T′=t1′,t2′,⋯,tr′; other properties are similar to property A. According to the training set, the information of its ideal division can be obtained as follows (1) [15].
(1)HT=−∑i=1mptilog2pti.Among them,pti=ti/∑i=1mti.The information entropy obtained by dividing the training set by attributes is as follows (2) [16].
(2)HAT′=∑i=1rpti′Hti′=−∑i=1r∑j=1mpti′pti∣ti′log2pti∣ti′=HT∣T′.Among them,pt′i=ti′/∑i=1rti′; ptj∣t′i=t′i∩tj/t′i.ptj∣t′i represents the probability that samples belonging to t′i in partition T′ belong to subset tj in an ideal partition [17]. It can be obtained that the information gain of attribute A for the division of the training set is the following.
(3)HGainT′=HT−HAT′.The segmentation information entropy of attributeA is the following.
(4)HT′=−∑i=1mpti′log2pti′.From Equations (3) and (4), the information gain rate of attribute A can be expressed as the following.
(5)RatioT′=HGainT′HT′=HT−HAT′HT′.Similarly, the information gain rate of other attributes can be calculated. By calculating the information gain rate of all attributes, the attribute with the largest information gain rate value is selected as the root node of the decision tree [18]. Then, determine the nodes of each layer of the decision tree in the same way, and the calculation method is the same as the above steps.Select the C4.5 decision tree algorithm provided by TipDM data mining platform of Taipu Company, mining 3000 pieces of teaching quality evaluation data in the first semester of the 2019-2020 school year, and extracting some classification rules [19, 20].Rule 1: IF degree = master student AND professional title = associate professor ANDage≤50, THEN teaching evaluation is “excellent” ratio of 86.3%.Rule 2: IF academic background = undergraduate AND professional title = associate professor ANDage>37, THEN proportion of the teaching evaluation is “excellent” which is 79.6%.Rule 3: IF academic background = doctoral student AND professional title = associate professor ANDage≤40, THEN proportion of teaching evaluation is “excellent” which is 95.6%.


#### 3.2.4. Interpretation and Practical Application of the Rules

From the extracted classification rule 3, it can be seen that the young- and middle-aged teachers with the title of associate professor and doctoral degree are the backbone of the teaching staff. This part of the team should be enriched to the front line of teaching and play a leading and exemplary role in the teaching team; teachers under the age of 50 with the title of associate professor and a master’s degree due to the rich teaching experience, the teaching experience, and ability of young teachers can be improved through their help, transmission, and introduction. In the introduction of talents, we mainly focus on highly educated, high professional titles and young doctors to improve the level of the entire teaching staff.


### 3.3. Application of Association Rules in Student Score Data Analysis

Student achievement is the basis for evaluating the quality of teaching, and it is also an important sign to test whether students have mastered the knowledge they have learned in school, by applying association rule mining technology to a large amount of student achievement data; interesting connections between these data can be found. For example, in the curriculum system, a certain precourse course is excellent, and its follow-up courses are excellent, and the proportion of excellent is also high. For example, students with excellent performance in discrete mathematics have a higher proportion of students with excellent performance in data structure; under the credit system, students can choose courses according to these rules and provide guidance for the revision of the existing curriculum system.Association analysis is a mining method for discovering hidden relationships between data [21]. In a large database, many similar rules can be analyzed without screening, in order to remove useless association rules, two thresholds are generally set: support and confidence. Support is an important measure. If the support is very low, it means that this rule only appears by chance, which is basically meaningless [22]. Therefore, support is often used to remove those meaningless rules. Confidence is the reliability of reasoning through rules. The user can define two thresholds, requiring that the support and confidence of the rules mined by the mining system are not less than the given thresholds.


## 3.1. Overall Framework of Shared Data Center

Data plays a very important role in the normal development of college teaching. In order to improve the daily office efficiency of the school, various departments have successively built their own information systems. However, information is managed independently, and there is no data interconnection and data sharing, a university officially started the construction of a digital campus in November 2018, and the construction of the shared data center platform began in the first phase of the digital campus. After three years of construction, the function of the shared data center has become more and more complete, and the amount of data has become more and more complete; based on the three main lines of integration of teacher information, student information and asset information, data integration, and information sharing are realized.All data in the data center has a unique data source; taking teacher information as an example, the source of its generation is the personnel management system; in order to ensure the consistency of the data, other systems have blocked the authority to modify the basic information of teachers; the data center will regularly extract the changed faculty and staff information from the personnel management system and push it to other application systems to achieve data sharing and synchronization. Similarly, the source of student information is the educational administration system, only the educational administration system can maintain student information, and other systems use student information and cannot maintain student information.The shared data center stores the shared data extracted from various application systems, such as the basic information of personnel extracted from the personnel management system, the teaching workload information, student performance data, teaching evaluation data extracted from the educational administration system, and the thesis results, patents, and reward information extracted from the scientific research system. These data are currently only simple summary and statistics without further analysis and utilization of the data. In view of this, we have selected teachers and students as the theme and discuss the in-depth application of data mining technology in shared data centers.


## 3.2. Application of Classification Technology in Teaching Quality Evaluation and Analysis

Online evaluation of the teaching quality of teachers is an important means of teaching quality monitoring. At present, the system has accumulated a large amount of evaluation data and uses classification algorithms to construct decision trees [10], it can excavate the relationship between teachers’ educational background, professional title, age and other attributes, and evaluation results and apply the research results to practice to provide more help for teaching managers.


### 3.2.1. Overview of Decision Tree Algorithm

Decision tree is an instance-based inductive learning algorithm; it infers classification rules in a decision tree representation from a set of unordered, random tuples [11]. A decision tree consists of three parts: nodes, branches, and leaves; nodes represent attributes, leaf nodes represent categories, and the topmost node of the tree is the root node, a path from the root node to the leaf node forms a classification rule, which is widely used. At present, a variety of decision tree algorithms have been formed, such as CLS, ID3, CHAID, CART, FACT, C4.5, GINI, SEE5, SLIQ, and SPRINT [12]. One of the most famous algorithms is the ID3 algorithm proposed by J.R. Quinlan in the “Induction of Decision Trees” paper in 1986 and the improved C4.5 algorithm in 1993. The C4.5 algorithm is an improved version of the ID3 algorithm; it uses the gain ratio to overcome the insufficiency of selecting attributes with many values when selecting attributes with information gain; during the tree construction process or after the construction is completed, pruning is performed; the discrete processing of continuous attributes can be completed, able to handle incomplete data, and can eventually form production rules [13, 14].Figure2 shows a simple decision tree classification model.Figure 2
Simple decision tree model.


### 3.2.2. Data Acquisition and Preprocessing

This application research mainly analyzes the relationship between teachers’ basic situation and evaluation results and establishes an excellent teacher model, so that schools can have an exact basis for teacher incentives. The research process uses data from two aspects: the basic situation of teachers and the results of teaching evaluation in the second semester of the 2019-2020 school year; the data structure is shown in Tables1 and 2.Table 1
Basic information of teachers.
Employee numberGenderAgeEducationJob titlePart100033Woman39PostgraduateAssociate professorBusiness school100047Man48PostgraduateProfessorIT academy100056Man41UndergraduateAssociate professorLaw school100105Man46PhD studentProfessorLaw school100111Woman35PostgraduateLecturerSchool of humanities100116Woman48UndergraduateAssociate professorSchool of education100105Man46PhD studentAssociate professorSchool of geography………………Table 2
Evaluation results of teachers’ classroom teaching quality.
Employee numberSchool yearSemesterEvaluation of teachers/%Evaluation level1000332019~2020193.864Excellent1000472019~2020194.278Excellent1000562019~2020189.656Good1001052019~2020178.754Middle1001112019~2020189.235Good……………


### 3.2.3. Teaching Quality Evaluation Model Based on C4.5 Decision Tree

Assume that the training dataset containsm categories, namely, T=t1,t2,⋯,tm. An attribute in the training dataset may have a total of k values of a1,a2,⋯,ak, according to the attribute is divided into T′=t1′,t2′,⋯,tr′; other properties are similar to property A. According to the training set, the information of its ideal division can be obtained as follows (1) [15].
(1)HT=−∑i=1mptilog2pti.Among them,pti=ti/∑i=1mti.The information entropy obtained by dividing the training set by attributes is as follows (2) [16].
(2)HAT′=∑i=1rpti′Hti′=−∑i=1r∑j=1mpti′pti∣ti′log2pti∣ti′=HT∣T′.Among them,pt′i=ti′/∑i=1rti′; ptj∣t′i=t′i∩tj/t′i.ptj∣t′i represents the probability that samples belonging to t′i in partition T′ belong to subset tj in an ideal partition [17]. It can be obtained that the information gain of attribute A for the division of the training set is the following.
(3)HGainT′=HT−HAT′.The segmentation information entropy of attributeA is the following.
(4)HT′=−∑i=1mpti′log2pti′.From Equations (3) and (4), the information gain rate of attribute A can be expressed as the following.
(5)RatioT′=HGainT′HT′=HT−HAT′HT′.Similarly, the information gain rate of other attributes can be calculated. By calculating the information gain rate of all attributes, the attribute with the largest information gain rate value is selected as the root node of the decision tree [18]. Then, determine the nodes of each layer of the decision tree in the same way, and the calculation method is the same as the above steps.Select the C4.5 decision tree algorithm provided by TipDM data mining platform of Taipu Company, mining 3000 pieces of teaching quality evaluation data in the first semester of the 2019-2020 school year, and extracting some classification rules [19, 20].Rule 1: IF degree = master student AND professional title = associate professor ANDage≤50, THEN teaching evaluation is “excellent” ratio of 86.3%.Rule 2: IF academic background = undergraduate AND professional title = associate professor ANDage>37, THEN proportion of the teaching evaluation is “excellent” which is 79.6%.Rule 3: IF academic background = doctoral student AND professional title = associate professor ANDage≤40, THEN proportion of teaching evaluation is “excellent” which is 95.6%.


### 3.2.4. Interpretation and Practical Application of the Rules

From the extracted classification rule 3, it can be seen that the young- and middle-aged teachers with the title of associate professor and doctoral degree are the backbone of the teaching staff. This part of the team should be enriched to the front line of teaching and play a leading and exemplary role in the teaching team; teachers under the age of 50 with the title of associate professor and a master’s degree due to the rich teaching experience, the teaching experience, and ability of young teachers can be improved through their help, transmission, and introduction. In the introduction of talents, we mainly focus on highly educated, high professional titles and young doctors to improve the level of the entire teaching staff.


## 3.2.1. Overview of Decision Tree Algorithm

Decision tree is an instance-based inductive learning algorithm; it infers classification rules in a decision tree representation from a set of unordered, random tuples [11]. A decision tree consists of three parts: nodes, branches, and leaves; nodes represent attributes, leaf nodes represent categories, and the topmost node of the tree is the root node, a path from the root node to the leaf node forms a classification rule, which is widely used. At present, a variety of decision tree algorithms have been formed, such as CLS, ID3, CHAID, CART, FACT, C4.5, GINI, SEE5, SLIQ, and SPRINT [12]. One of the most famous algorithms is the ID3 algorithm proposed by J.R. Quinlan in the “Induction of Decision Trees” paper in 1986 and the improved C4.5 algorithm in 1993. The C4.5 algorithm is an improved version of the ID3 algorithm; it uses the gain ratio to overcome the insufficiency of selecting attributes with many values when selecting attributes with information gain; during the tree construction process or after the construction is completed, pruning is performed; the discrete processing of continuous attributes can be completed, able to handle incomplete data, and can eventually form production rules [13, 14].Figure2 shows a simple decision tree classification model.Figure 2
Simple decision tree model.


## 3.2.2. Data Acquisition and Preprocessing

This application research mainly analyzes the relationship between teachers’ basic situation and evaluation results and establishes an excellent teacher model, so that schools can have an exact basis for teacher incentives. The research process uses data from two aspects: the basic situation of teachers and the results of teaching evaluation in the second semester of the 2019-2020 school year; the data structure is shown in Tables1 and 2.Table 1
Basic information of teachers.
Employee numberGenderAgeEducationJob titlePart100033Woman39PostgraduateAssociate professorBusiness school100047Man48PostgraduateProfessorIT academy100056Man41UndergraduateAssociate professorLaw school100105Man46PhD studentProfessorLaw school100111Woman35PostgraduateLecturerSchool of humanities100116Woman48UndergraduateAssociate professorSchool of education100105Man46PhD studentAssociate professorSchool of geography………………Table 2
Evaluation results of teachers’ classroom teaching quality.
Employee numberSchool yearSemesterEvaluation of teachers/%Evaluation level1000332019~2020193.864Excellent1000472019~2020194.278Excellent1000562019~2020189.656Good1001052019~2020178.754Middle1001112019~2020189.235Good……………


## 3.2.3. Teaching Quality Evaluation Model Based on C4.5 Decision Tree

Assume that the training dataset containsm categories, namely, T=t1,t2,⋯,tm. An attribute in the training dataset may have a total of k values of a1,a2,⋯,ak, according to the attribute is divided into T′=t1′,t2′,⋯,tr′; other properties are similar to property A. According to the training set, the information of its ideal division can be obtained as follows (1) [15].
(1)HT=−∑i=1mptilog2pti.Among them,pti=ti/∑i=1mti.The information entropy obtained by dividing the training set by attributes is as follows (2) [16].
(2)HAT′=∑i=1rpti′Hti′=−∑i=1r∑j=1mpti′pti∣ti′log2pti∣ti′=HT∣T′.Among them,pt′i=ti′/∑i=1rti′; ptj∣t′i=t′i∩tj/t′i.ptj∣t′i represents the probability that samples belonging to t′i in partition T′ belong to subset tj in an ideal partition [17]. It can be obtained that the information gain of attribute A for the division of the training set is the following.
(3)HGainT′=HT−HAT′.The segmentation information entropy of attributeA is the following.
(4)HT′=−∑i=1mpti′log2pti′.From Equations (3) and (4), the information gain rate of attribute A can be expressed as the following.
(5)RatioT′=HGainT′HT′=HT−HAT′HT′.Similarly, the information gain rate of other attributes can be calculated. By calculating the information gain rate of all attributes, the attribute with the largest information gain rate value is selected as the root node of the decision tree [18]. Then, determine the nodes of each layer of the decision tree in the same way, and the calculation method is the same as the above steps.Select the C4.5 decision tree algorithm provided by TipDM data mining platform of Taipu Company, mining 3000 pieces of teaching quality evaluation data in the first semester of the 2019-2020 school year, and extracting some classification rules [19, 20].Rule 1: IF degree = master student AND professional title = associate professor ANDage≤50, THEN teaching evaluation is “excellent” ratio of 86.3%.Rule 2: IF academic background = undergraduate AND professional title = associate professor ANDage>37, THEN proportion of the teaching evaluation is “excellent” which is 79.6%.Rule 3: IF academic background = doctoral student AND professional title = associate professor ANDage≤40, THEN proportion of teaching evaluation is “excellent” which is 95.6%.


## 3.2.4. Interpretation and Practical Application of the Rules

From the extracted classification rule 3, it can be seen that the young- and middle-aged teachers with the title of associate professor and doctoral degree are the backbone of the teaching staff. This part of the team should be enriched to the front line of teaching and play a leading and exemplary role in the teaching team; teachers under the age of 50 with the title of associate professor and a master’s degree due to the rich teaching experience, the teaching experience, and ability of young teachers can be improved through their help, transmission, and introduction. In the introduction of talents, we mainly focus on highly educated, high professional titles and young doctors to improve the level of the entire teaching staff.


## 3.3. Application of Association Rules in Student Score Data Analysis

Student achievement is the basis for evaluating the quality of teaching, and it is also an important sign to test whether students have mastered the knowledge they have learned in school, by applying association rule mining technology to a large amount of student achievement data; interesting connections between these data can be found. For example, in the curriculum system, a certain precourse course is excellent, and its follow-up courses are excellent, and the proportion of excellent is also high. For example, students with excellent performance in discrete mathematics have a higher proportion of students with excellent performance in data structure; under the credit system, students can choose courses according to these rules and provide guidance for the revision of the existing curriculum system.Association analysis is a mining method for discovering hidden relationships between data [21]. In a large database, many similar rules can be analyzed without screening, in order to remove useless association rules, two thresholds are generally set: support and confidence. Support is an important measure. If the support is very low, it means that this rule only appears by chance, which is basically meaningless [22]. Therefore, support is often used to remove those meaningless rules. Confidence is the reliability of reasoning through rules. The user can define two thresholds, requiring that the support and confidence of the rules mined by the mining system are not less than the given thresholds.


## 4. Results Analysis

### 4.1. Data Acquisition and Preprocessing

Now, based on the student achievement data of the shared data center, we select the professional course achievement database of 250 software engineering students from the School of Computer Science, some data are shown in Table3, each record in the table represents a transaction, the student number attribute can be regarded as an ID number, and the content of the following fields can represent the item set of the transaction, that is, the grades of a certain professional course. The table actually contains a large number of “attribute-value” pairs, such as “C language-85.” Curriculum correlation analysis is to study the relationship between multiple “attribute-value” pairs that frequently appear together.Table 3
Original student grade transaction database.
Student IDC languageData structureDatabase applicationSoftware engineeringMicrocomputer principleOperating systemIntroduction to computersNetwork principle092101410192907981728874890921014102867366888575809709210141038085869078798185092101410484807987748169850921014105858574688387807709210141067775808569858768………………………As shown in Table3, there are very few “attribute-values” in the table that are exactly the same, and if you mine it directly as an item, you will not get the desired result. After the discretization of grades and course coding, the obtained student grade transaction database is shown in Table 4.Table 4
Transaction database of student grades after processing.
Student IDC languageData structureDatabase applicationSoftware engineeringMicrocomputer principleOperating systemIntroduction to computersNetwork principle0921014101K1AK2AK3BK4AK5BK6AK7BK8A0921014102K1AK2BK3BK4AK5AK6BK7BK8A0921014103K1AK2AK3AK4AK5BK6BK7AK8A0921014104K1AK2AK3BK4AK5BK6AK7BK8A0921014105K1AK2AK3BK4BK5AK6AK7AK8B0921014106K1BK2BK3AK4AK5BK6AK7AK8B………………………The course names are coded with K1, K2, ..., for example, the coding of “C language” is K1, the code of the “data structure” is K2; at the same time, the grade data is transformed into three grade data, namely, A: corresponding score 80-100; B: corresponding score 60-79; C: corresponding score 0-59.Figure3 shows the accuracy of the dataset at different sampling times. With the increase of sampling times, the correct rate of the algorithm increases, and the correct rate under all sampling times is above 80%, when the sampling times increase to a certain value, the performance of the algorithm also tends to be stable [23, 24]. It can be seen from the figure that when the sampling times are 12 and 15, the difference in algorithm performance is very small, so 12 is selected as the final sampling times.Figure 3
Algorithm performance under different sampling times.


### 4.2. Correlation Analysis of Students’ Course Grades

After data preprocessing, the data can be considered to be pure; on this dataset, the classical Apriori algorithm is used to perform association rule analysis on the data, the support degree is set to 0.2, the confidence degree is 0.6, and the maximum number of itemsets is 3, and some association rules are mined [25].Discrete mathematics by rules, “excellent” → data structure, and“excellent”confidence=61%, we can find that students who score above 80 in Discrete Mathematics are 61% more likely to score above 80 in “Data Structure.” We can strengthen the teaching of “discrete mathematics” to improve the teaching effect of “data structure”; designed by regular assembly language programming, “excellent” → microcomputer principle and interface technology, and “excellent”confidence=61%, we can find that students with a score of 80 or more in “Assembly Language Programming,” “microcomputer principle and interface technology” is also 61% likely to score above 80 points; similarly, we can also strengthen the teaching of “Assembly Language Programming” class, in order to improve the teaching effect of “Microcomputer Principle and Interface Technology” course.


## 4.1. Data Acquisition and Preprocessing

Now, based on the student achievement data of the shared data center, we select the professional course achievement database of 250 software engineering students from the School of Computer Science, some data are shown in Table3, each record in the table represents a transaction, the student number attribute can be regarded as an ID number, and the content of the following fields can represent the item set of the transaction, that is, the grades of a certain professional course. The table actually contains a large number of “attribute-value” pairs, such as “C language-85.” Curriculum correlation analysis is to study the relationship between multiple “attribute-value” pairs that frequently appear together.Table 3
Original student grade transaction database.
Student IDC languageData structureDatabase applicationSoftware engineeringMicrocomputer principleOperating systemIntroduction to computersNetwork principle092101410192907981728874890921014102867366888575809709210141038085869078798185092101410484807987748169850921014105858574688387807709210141067775808569858768………………………As shown in Table3, there are very few “attribute-values” in the table that are exactly the same, and if you mine it directly as an item, you will not get the desired result. After the discretization of grades and course coding, the obtained student grade transaction database is shown in Table 4.Table 4
Transaction database of student grades after processing.
Student IDC languageData structureDatabase applicationSoftware engineeringMicrocomputer principleOperating systemIntroduction to computersNetwork principle0921014101K1AK2AK3BK4AK5BK6AK7BK8A0921014102K1AK2BK3BK4AK5AK6BK7BK8A0921014103K1AK2AK3AK4AK5BK6BK7AK8A0921014104K1AK2AK3BK4AK5BK6AK7BK8A0921014105K1AK2AK3BK4BK5AK6AK7AK8B0921014106K1BK2BK3AK4AK5BK6AK7AK8B………………………The course names are coded with K1, K2, ..., for example, the coding of “C language” is K1, the code of the “data structure” is K2; at the same time, the grade data is transformed into three grade data, namely, A: corresponding score 80-100; B: corresponding score 60-79; C: corresponding score 0-59.Figure3 shows the accuracy of the dataset at different sampling times. With the increase of sampling times, the correct rate of the algorithm increases, and the correct rate under all sampling times is above 80%, when the sampling times increase to a certain value, the performance of the algorithm also tends to be stable [23, 24]. It can be seen from the figure that when the sampling times are 12 and 15, the difference in algorithm performance is very small, so 12 is selected as the final sampling times.Figure 3
Algorithm performance under different sampling times.


## 4.2. Correlation Analysis of Students’ Course Grades

After data preprocessing, the data can be considered to be pure; on this dataset, the classical Apriori algorithm is used to perform association rule analysis on the data, the support degree is set to 0.2, the confidence degree is 0.6, and the maximum number of itemsets is 3, and some association rules are mined [25].Discrete mathematics by rules, “excellent” → data structure, and“excellent”confidence=61%, we can find that students who score above 80 in Discrete Mathematics are 61% more likely to score above 80 in “Data Structure.” We can strengthen the teaching of “discrete mathematics” to improve the teaching effect of “data structure”; designed by regular assembly language programming, “excellent” → microcomputer principle and interface technology, and “excellent”confidence=61%, we can find that students with a score of 80 or more in “Assembly Language Programming,” “microcomputer principle and interface technology” is also 61% likely to score above 80 points; similarly, we can also strengthen the teaching of “Assembly Language Programming” class, in order to improve the teaching effect of “Microcomputer Principle and Interface Technology” course.


## 5. Conclusion

Based on the shared data center, the application of data mining technology in educational informatization is discussed. Two topics of teaching quality evaluation analysis and students’ course performance correlation analysis are selected, and decision tree algorithm and association rule mining algorithm are, respectively, applied, which are implemented on two datasets, and some reasonable and effective rules are obtained. According to the analysis results of teaching quality evaluation, it can provide a certain reference for schools in the training, assessment, and talent introduction of teachers. Of course, teachers’ teaching methods, instruments, behaviors, students’ own qualities, and other factors will also affect students’ evaluation of teachers. Through the correlation analysis of students’ performance, it is found that there are correlations between courses of the same major, such as the sequence of courses, the connection of content, and the professional weight of courses. It can provide in-depth analysis of curriculum settings for teaching departments, provide reference for the overall curriculum settings of schools, and provide guidance for students to choose courses under the full credit system. Of course, the applications in the above two directions are just some simple applications of data mining technology in the digital campus system. How to make full use of college resources, it is a practical problem facing colleges and universities to better combine data mining technology and digital campus. The follow-up work will continue to select some topics, such as classification labels to measure the scientific research ability level of teaching and research personnel, cluster analysis, objective and effective description of the current situation of teachers, association rules technical description of teaching, scientific research and social work, and other aspects. Relationship, etc., discusses the in-depth application of data mining technology in digital campus.



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*Source: 4814727-2022-07-04.xml*