# Construction and Management Method of University Information Platform Based on Big Data Technology

Authors: Yamin Si; Bin Wu
Journal: Mobile Information Systems
 (2022)

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## Abstract

In order to solve the problems faced in the current management of colleges and universities, this topic integrates data mining technology and traditional statistical analysis technology in the process of College data management, and integrates multisource campus raw data. Take corresponding research models for different research objectives, implement the fine, meticulous, and accurate management concept of colleges and universities for students, build a student information management platform and help the construction of smart campus. The research integrates the school’s multi-business system data, opens up the information channel between information systems, establishes a multisystem data center and realizes the whole life cycle management of College Students’ data, including data collection, cleaning, storage and file recycling, and records the data of important nodes during students’ enrollment, graduation, and departure. Through the analysis of the school’s business management needs and the study of the school’s data characteristics, the data collected from the system information has been effectively distributed, and the data linking process is only used to delete data features based on data features. Under the action of big data technology, the information platform for college students can be constructed, which can provide more efficient, safe, and comprehensive decision support for college students’ management, making all work decisions more scientific. It can be said that the integration of big data will support the management of college students to a new level, which is very important for the modern management of students.


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## Body

## 1. Introduction

With the popularity of cloud computing and the Internet of Things, big data has become an important topic. Big data uses techniques for making very large files created in the data age. At the same time, its technological development and innovation are also named after big data. Through careful analysis of the big data generated in the data age, many enterprises can gain commercial and social connections, thereby promoting the development of various industries [1]. In the development of college information systems, big data technology is used to record a large amount of school data, summarize information, and conduct in-depth investigations, as shown in Figure 1, to provide decision-making basis for college informatization construction, educational resources, and career development. Transparent materials can better influence students’ learning strategies, help plan lesson plans ahead, resolve teacher-student conflicts, and simplify community service prompts. Big data is not a short term for a large amount of data, because it not only means a large amount of information, but also includes the data technology means behind the data that are rarely paid attention to. If you want to find a definition for big data, it is impossible to define it in a unified way [2]. After entering the twenty-first century, all countries in the world have carried out reflection and reform on Chinese education, and the direction of reform has gradually transformed society into a learning community. Judging from the situation of colleges and universities in recent years, the enrollment scale of colleges and universities has gradually expanded, and the school area has also increased year by year. The increase in the number of students will inevitably bring new challenges to the student management. The school will change the traditional teaching mode according to the specific learning situation. Since the standardized student management system in the past could not solve the current student management problem, we should constantly try to use information technology, that is, large-scale data model, to solve the student management problem [3]. Many colleges and universities now have very large records. Many colleges and universities are researching models that utilize big data to analyze and perform educational professional tasks, which poses new challenges to the academic management.Figure 1
University information platform.


## 2. Literature Review

Hermes and others believe that all information generated by college students in the whole process from entering the campus to entering the society will be incorporated into the university information management system, so a large number of information records are retained in the university information management system, including enrollment records, student management, graduation design, equipment use, attendance, examination results, employment, and other aspects [4]. Jia and others said that these data can simplify student management, help some relevant managers liberate from heavy work, and promote the continuous improvement of work efficiency [5]. Cui and others believe that in the traditional information management mode, managers collect all kinds of information with the help of traditional means, which makes it difficult to obtain effective statistics, and there are problems of missing and error [6]. Ma et al. It is believed that with the help of big data technology, administrators can dig deep into the key points in the data, and explore the relationship between student behavior and assistance through analysis, so as to improve awareness and control decision-making. For colleges and universities, the use of big data to improve the level of student management is a top priority [7]. Kalinin said that although the development of colleges and universities has achieved success at this level, many colleges and universities have provided big data in management, but due to the large number of students, there are problems in data. Technology and technology management, and increasing the use of big data in colleges and universities have become increasingly difficult to manage students. Since the popularity of big data, colleges and universities have been actively learning and using big data, which makes it easier for students to manage. However, overcrowding and the problem of informatization development have not been solved in time, leading to big data in colleges and universities, and managing students in colleges and universities is also a challenge [8]. Ch En et al. It is believed that the development of high school student data management should turn to the characteristics of student data management. Not only to solve the problems in student supervision, but also to pay close attention to the problems of faculty and staff in management, that is, to meet the needs of student management and provide information services for teachers and students, and provide information and support materials to faculty and staff [9]. Aiyebelehin and others said that big data information technology for college student management should not only have various intelligent service functions, but also ensure that people in every position can get convenience and benefits from it, so as to effectively promote decision-making. A pyramid model can be established. Under the stratification of the pyramid, a hierarchical decision-making organization can be realized through several specific objectives to help the decision-making science of student management [10]. Qiao and others believe that at present, most colleges and universities have completed information construction. University information system, student management system, office automation system, black board platform, e-learning system, teaching resource database, library management system, distance education system, teaching and scientific research system, campus one card system, video monitoring system, financial management system, enrollment, and employment system have been put into use. These systems are independent. When using them, teachers and students have to log in to different systems for information input and query according to their needs, which leads to problems such as repeated input of information and inability to merge query results [11]. Mao and others said that integrating the information data of all departments of the university to build a big data exchange and sharing platform for learning has obvious characteristics for big data information. First of all, the educational administration management system and campus kilometer system in the campus can be managed uniformly. By building a data sharing platform for various services, students can more easily participate in campus life, the isolated island of information can be truly eliminated, and various information exchanges can be carried out more closely between different campus departments. At the same time, it plays a supporting role in the development of big data [12]. Yang also said that with the support of big data, student management can be improved, many activities are more obvious, the department’s participation wisdom has increased, and different types of information can be shared. It has been continuously updated and the use of data has been improved continuously. The performance of the big data platform will support improving the management performance of college students, provide detailed and in-depth data analysis for each specific task, and support early improvement of intervention management [13].


## 3. Method

### 3.1. University Student Data Analysis Algorithm

The data sources of colleges and universities are diversified, covering most of the learning and life processes of students in school. Data analysis of college students requires a standardized analysis process according to the characteristics of data collection. Data is divided into static data and dynamic data according to data exchange, design data, and non-data data according to data type, and school data content and daily life information according to business information [14]. The classification of data can more accurately establish the corresponding analysis model. Data mining mainly analyzes the text in the students’ description of family status and follow-up visit materials, uses the relevant text information to segment words through word segmentation tools, then uses Word2vec to expand the relevant vocabulary features, looks for the key words with high correlation with each difficulty level, and finally identifies the level by using machine learning algorithms such as support vector machine.


#### 3.1.1. Word Segmentation Algorithm and Weight Calculation

For words, words are the smallest and most useful words that can stand on their own. The current Chinese word segmentation algorithms include word segmentation based on word frequency statistics, code-based word segmentation, and word segmentation tools such as SCWS and J1EBA [15].After segmenting a large amount of text, use TF-IDF (Term Frequency Inverse Document Request) to measure the importance of the message, usually based on how many times they appear in the data and how often they appear based on usage, mainly to evaluate whether some words have a good ability to distinguish between documents, as shown in :(1)w=ni,j∑nk,j×logD1+j:tj∈dj,where ni,j represents the number of times ti appears in document dj; ∑nk,j represents the sum of the number of occurrences of all words in the document; D represents the total number of documents in the corpus; 1+j:ti∈dj indicates the number of files containing the word ti.TF-IDF algorithms usually filter out similar words and store more important words. The text is then represented as a vector space.


#### 3.1.2. Extracting Eigenvalues by Chi-Square Test

Chi-square test is a method in mathematical statistics to measure the independence of two variables. In the special option of segmenting text, in order to ensure that the wordW is independent of the T group, a chi-square test is performed on the word, and the formula in formula (2) is:(2)x2=Nad−bc2a+ca+bb+dc+d,where: N is all data; a is material with w and in class t; b is data with w but not in class t; c is data without w but in class t; d is non-ba data, not in class t number.Then, the larger the chi-square value, it indicates thatt is an obvious feature to distinguish this class.


#### 3.1.3. Word2vec Keyword Feature Extension

In the process of text mining, in order to avoid the problem of synonyms, content features need to be expanded, which are based on neural network language structures and are easier to obtain than word vectors. It uses cbow and cross Gram modes to express homosyllabic words and is a useful tool for obtaining the mathematical vectors [16]. Word2vec maps the up-down relationship of words in the article to the unified coordinate system to build a sparse matrix. The matrix reflects the association between each word. Huffman’s compression algorithm is adopted to reduce the weight of some popular words. The trained word vector can be used for clustering analysis, searching for synonyms, part of speech analysis, and many other natural language processing-related works.When defining the behavior of a student’s behavior trajectory in a certain geographical location in a certain time period, in addition to considering whether the student conducts relevant behavior in that location in that time period, the student’s behavior is obviously reflected in the number of times the student appears in this location. The more times the student eats in the same location, such as the same canteen, indicates that the location has a high degree of importance to the student’s behavior trajectory [17]. At the same time, the temporal and spatial attributes of behavior trajectory are also changing. The closer the selection time is to the current time, the more it can influence the students’ recent behavior. In order to reflect the differences of students’ behavior times in different places and the evolution trend of students’ behavior trajectory, the differences of boundary values and occurrence times over the research span should be taken into account. Therefore, this paper uses the cosine similarity method to calculate the similarity of student behavior, in which the definition of student s signing in at a certain time point t and a certain position P is e, then the student’s behavior at a certain point can be expressed by formula:(3)es,t,p=∑n=1Ns,t,pes,t,pfs,t,pt,where fs,t,pt is the marginal effect function of student s in time period t and position P, which is expressed by formula:(4)fs,t,pt=e1Hd−d0,where: d is the current date; d0 is the latest occurrence time; H represents the maximum value of the absolute value of the difference between p and the current time and date; Ns,t,p represents the total number of times that student s appears at point P in time period l; es,t,p indicates whether student s appears at point P in the time interval. If it occurs, es,t,p=1; Otherwise, es,t,p=0.Based on this, we can calculate the similarity of student behavior in the next step, and set the similarity calculation formula (5) of student s1 and student s2 as:(5)sims1,s2=∑t∈T∑p∈Pes1,t,p′es2,t,p′∑t∈T∑p∈Pes1,t,p′∑p∈p∑p∈Pes2,t,p′,where: T is the set of time intervals; P is the collection of student behavior places.Then, the value range ofsims1,s2 is [0,1]. If the behavior trajectories of two students are completely different, the value of sims1,s2 is 0. If the behavior trajectories of two students are completely similar, the value of sims1,s2 is 1. It can be found that the more similar the behavior of students s1 and s2, the greater the value of sims1,s2.The platform collects students’ school data from multiple sources and obtains 13 types of information including family income, labor force, educated children and family emergencies, so as to comprehensively evaluate students’ family economic status at multiple levels and dimensions, and provide more effective and reliable reference information for accurate funding [18]. The questionnaire mainly provides the factors of static family economic situation, which are divided into three types: value, degree, and right and wrong. Dynamic consumption can more effectively and practically evaluate the students’ family economic situation and consumption habits. Through the average effective consumption days and average consumption quota per month, these information can be added to the model as dynamic factors. The specific evaluation factors are shown in Figure 2.Figure 2
Comprehensive fuzzy evaluation factors.At present, the level of family economic difficulties in Colleges and universities is divided into four categories: general family economic difficulties, family economic difficulties, very difficult family economy, and non-difficult family economy [19]. According to different data types, different membership functions are used to evaluate them. For degree data such as family accidents and natural disasters, the membership degree is assigned according to its severity, and the membership function is shown in formula:(6)Severe degree:10.50,More serious:0.510,No such situation:010.For the numerical data such as labor force population ratio and monthly average consumption data, the membership function is used to calculate them. The membership function is shown in equations (7)–(9). The numerical interval is divided into three segments S1,S2,S3. The position of X is described by the membership function:(7)f1X1=1X1<S11−S2−X1S2−X1S1<X1≤S20X1>S2,(8)f2X1=0X1≤S1,X1≥S2X−SS−SS1<X1<S21−X−SS−SS2<X1<S21X1=S2,(9)f3X1=0X1<S2X1−S2S3−S2S2<X1≤S31X1>S3.Based on this, the membership degree of numerical data is calculated, combined with the degree assignment membership degree, that is, the membership degree of the evaluation object to different fuzzy subsets is determined, and the fuzzy relationship matrix is obtained, as shown in equation:(10)R=r11r12r13r21r22r23.........rn1rn2rn3,where rn represents the membership degree of the evaluation object to the fuzzy subset vj from the perspective of factor u, that is, it represents the reasonable relationship between each family difficulty factor and students. Multiply the relationship matrix by the weight w1,w2,…,wn of N poverty causing factors. The weight is calculated according to the expert scoring matrix. The scoring matrix is shown in Table 1.Table 1
Expert scoring matrix of poverty factors.
Labor population ratioAverage monthly consumptionSudden family changesNatural disasterThe family is seriously illLabor population ratio11/31/31/51/7Average monthly consumption311/51/31/5Sudden family changes35133Natural disaster531/311/3The family is seriously ill751/331


### 3.2. Overall Design of Platform

As shown in Figure3, the student information management platform is based on multidimensional student campus data such as campus card consumption records, dormitory access control records, library access control records, achievement records, and historical subsidy records. After a series of data processing steps such as data cleaning, desensitization, index calculation, missing value filling and Feature Engineering, according to different research objectives, combined with data mining technologies such as classification, clustering and outlier detection, the application pages of family economic difficulty judgment, student portrait, and refined supervision are generated to help the school understand students’ personality behavior and assist the school in daily management [20]. According to the above analysis, the process of building the platform is mainly divided into the following four parts:The first part (corresponding data source layer): Integrate heterogeneous data on campus to avoid the phenomenon of “information island” on each data platform. The establishment of university database lays a research foundation for the follow-up research objectives such as student behavior analysis and portrait construction.The second part (corresponding data processing layer): Format conversion and preprocessing of multidimensional data in the database, and then do corresponding feature engineering for different research contents, such as feature construction and feature extraction. In addition, in the process of student portrait research, build a four-tier portrait label system and calculate the indicators required in the system.The third part (corresponding model algorithm layer): Clarify the objectives of each module of the student information management platform, integrate the traditional statistical analysis method and data mining technology, and study the judgment of family economic difficulties, the construction of student portraits, and the early warning supervision of accurate management.The fourth part (corresponding application layer): Visualize the analysis results of the three modules, develop the student information management platform, and display the results of each module on the platform page, which can be applied to the actual campus management.Figure 3
Construction scheme of student information platform.As shown in Figure4, the student information management platform mainly designs and develops six functional modules: user management module, department management module, teacher management module, careful management module, accurate funding module, and fine supervision module.Figure 4
Function module of student information management platform.Among them, the user management module is mainly responsible for the management of platform user roles, including the addition, deletion, empowerment, and query functions of user accounts; The department management module and teacher management module are responsible for adding, deleting, querying and displaying the information of colleges, majors, classes, grades, and teachers; The careful management module mainly constructs student portraits in terms of students’ behavior, study, consumption habits and social interaction, and compares and displays the personal portraits of excellent students and students who need attention; The precise funding module combines the multisource campus data and data mining technology of students to determine the funding level of students with financial difficulties, as well as the management of work study application and student loan list of poor students; Fine supervision includes outlier students’ early warning, social closure early warning, and psychological abnormality early warning. It integrates questionnaire analysis and statistical analysis methods to give timely early warning to students with abnormal data, which is conducive to managers’ offline positioning and attention [21].


## 3.1. University Student Data Analysis Algorithm

The data sources of colleges and universities are diversified, covering most of the learning and life processes of students in school. Data analysis of college students requires a standardized analysis process according to the characteristics of data collection. Data is divided into static data and dynamic data according to data exchange, design data, and non-data data according to data type, and school data content and daily life information according to business information [14]. The classification of data can more accurately establish the corresponding analysis model. Data mining mainly analyzes the text in the students’ description of family status and follow-up visit materials, uses the relevant text information to segment words through word segmentation tools, then uses Word2vec to expand the relevant vocabulary features, looks for the key words with high correlation with each difficulty level, and finally identifies the level by using machine learning algorithms such as support vector machine.


### 3.1.1. Word Segmentation Algorithm and Weight Calculation

For words, words are the smallest and most useful words that can stand on their own. The current Chinese word segmentation algorithms include word segmentation based on word frequency statistics, code-based word segmentation, and word segmentation tools such as SCWS and J1EBA [15].After segmenting a large amount of text, use TF-IDF (Term Frequency Inverse Document Request) to measure the importance of the message, usually based on how many times they appear in the data and how often they appear based on usage, mainly to evaluate whether some words have a good ability to distinguish between documents, as shown in :(1)w=ni,j∑nk,j×logD1+j:tj∈dj,where ni,j represents the number of times ti appears in document dj; ∑nk,j represents the sum of the number of occurrences of all words in the document; D represents the total number of documents in the corpus; 1+j:ti∈dj indicates the number of files containing the word ti.TF-IDF algorithms usually filter out similar words and store more important words. The text is then represented as a vector space.


### 3.1.2. Extracting Eigenvalues by Chi-Square Test

Chi-square test is a method in mathematical statistics to measure the independence of two variables. In the special option of segmenting text, in order to ensure that the wordW is independent of the T group, a chi-square test is performed on the word, and the formula in formula (2) is:(2)x2=Nad−bc2a+ca+bb+dc+d,where: N is all data; a is material with w and in class t; b is data with w but not in class t; c is data without w but in class t; d is non-ba data, not in class t number.Then, the larger the chi-square value, it indicates thatt is an obvious feature to distinguish this class.


### 3.1.3. Word2vec Keyword Feature Extension

In the process of text mining, in order to avoid the problem of synonyms, content features need to be expanded, which are based on neural network language structures and are easier to obtain than word vectors. It uses cbow and cross Gram modes to express homosyllabic words and is a useful tool for obtaining the mathematical vectors [16]. Word2vec maps the up-down relationship of words in the article to the unified coordinate system to build a sparse matrix. The matrix reflects the association between each word. Huffman’s compression algorithm is adopted to reduce the weight of some popular words. The trained word vector can be used for clustering analysis, searching for synonyms, part of speech analysis, and many other natural language processing-related works.When defining the behavior of a student’s behavior trajectory in a certain geographical location in a certain time period, in addition to considering whether the student conducts relevant behavior in that location in that time period, the student’s behavior is obviously reflected in the number of times the student appears in this location. The more times the student eats in the same location, such as the same canteen, indicates that the location has a high degree of importance to the student’s behavior trajectory [17]. At the same time, the temporal and spatial attributes of behavior trajectory are also changing. The closer the selection time is to the current time, the more it can influence the students’ recent behavior. In order to reflect the differences of students’ behavior times in different places and the evolution trend of students’ behavior trajectory, the differences of boundary values and occurrence times over the research span should be taken into account. Therefore, this paper uses the cosine similarity method to calculate the similarity of student behavior, in which the definition of student s signing in at a certain time point t and a certain position P is e, then the student’s behavior at a certain point can be expressed by formula:(3)es,t,p=∑n=1Ns,t,pes,t,pfs,t,pt,where fs,t,pt is the marginal effect function of student s in time period t and position P, which is expressed by formula:(4)fs,t,pt=e1Hd−d0,where: d is the current date; d0 is the latest occurrence time; H represents the maximum value of the absolute value of the difference between p and the current time and date; Ns,t,p represents the total number of times that student s appears at point P in time period l; es,t,p indicates whether student s appears at point P in the time interval. If it occurs, es,t,p=1; Otherwise, es,t,p=0.Based on this, we can calculate the similarity of student behavior in the next step, and set the similarity calculation formula (5) of student s1 and student s2 as:(5)sims1,s2=∑t∈T∑p∈Pes1,t,p′es2,t,p′∑t∈T∑p∈Pes1,t,p′∑p∈p∑p∈Pes2,t,p′,where: T is the set of time intervals; P is the collection of student behavior places.Then, the value range ofsims1,s2 is [0,1]. If the behavior trajectories of two students are completely different, the value of sims1,s2 is 0. If the behavior trajectories of two students are completely similar, the value of sims1,s2 is 1. It can be found that the more similar the behavior of students s1 and s2, the greater the value of sims1,s2.The platform collects students’ school data from multiple sources and obtains 13 types of information including family income, labor force, educated children and family emergencies, so as to comprehensively evaluate students’ family economic status at multiple levels and dimensions, and provide more effective and reliable reference information for accurate funding [18]. The questionnaire mainly provides the factors of static family economic situation, which are divided into three types: value, degree, and right and wrong. Dynamic consumption can more effectively and practically evaluate the students’ family economic situation and consumption habits. Through the average effective consumption days and average consumption quota per month, these information can be added to the model as dynamic factors. The specific evaluation factors are shown in Figure 2.Figure 2
Comprehensive fuzzy evaluation factors.At present, the level of family economic difficulties in Colleges and universities is divided into four categories: general family economic difficulties, family economic difficulties, very difficult family economy, and non-difficult family economy [19]. According to different data types, different membership functions are used to evaluate them. For degree data such as family accidents and natural disasters, the membership degree is assigned according to its severity, and the membership function is shown in formula:(6)Severe degree:10.50,More serious:0.510,No such situation:010.For the numerical data such as labor force population ratio and monthly average consumption data, the membership function is used to calculate them. The membership function is shown in equations (7)–(9). The numerical interval is divided into three segments S1,S2,S3. The position of X is described by the membership function:(7)f1X1=1X1<S11−S2−X1S2−X1S1<X1≤S20X1>S2,(8)f2X1=0X1≤S1,X1≥S2X−SS−SS1<X1<S21−X−SS−SS2<X1<S21X1=S2,(9)f3X1=0X1<S2X1−S2S3−S2S2<X1≤S31X1>S3.Based on this, the membership degree of numerical data is calculated, combined with the degree assignment membership degree, that is, the membership degree of the evaluation object to different fuzzy subsets is determined, and the fuzzy relationship matrix is obtained, as shown in equation:(10)R=r11r12r13r21r22r23.........rn1rn2rn3,where rn represents the membership degree of the evaluation object to the fuzzy subset vj from the perspective of factor u, that is, it represents the reasonable relationship between each family difficulty factor and students. Multiply the relationship matrix by the weight w1,w2,…,wn of N poverty causing factors. The weight is calculated according to the expert scoring matrix. The scoring matrix is shown in Table 1.Table 1
Expert scoring matrix of poverty factors.
Labor population ratioAverage monthly consumptionSudden family changesNatural disasterThe family is seriously illLabor population ratio11/31/31/51/7Average monthly consumption311/51/31/5Sudden family changes35133Natural disaster531/311/3The family is seriously ill751/331


## 3.1.1. Word Segmentation Algorithm and Weight Calculation

For words, words are the smallest and most useful words that can stand on their own. The current Chinese word segmentation algorithms include word segmentation based on word frequency statistics, code-based word segmentation, and word segmentation tools such as SCWS and J1EBA [15].After segmenting a large amount of text, use TF-IDF (Term Frequency Inverse Document Request) to measure the importance of the message, usually based on how many times they appear in the data and how often they appear based on usage, mainly to evaluate whether some words have a good ability to distinguish between documents, as shown in :(1)w=ni,j∑nk,j×logD1+j:tj∈dj,where ni,j represents the number of times ti appears in document dj; ∑nk,j represents the sum of the number of occurrences of all words in the document; D represents the total number of documents in the corpus; 1+j:ti∈dj indicates the number of files containing the word ti.TF-IDF algorithms usually filter out similar words and store more important words. The text is then represented as a vector space.


## 3.1.2. Extracting Eigenvalues by Chi-Square Test

Chi-square test is a method in mathematical statistics to measure the independence of two variables. In the special option of segmenting text, in order to ensure that the wordW is independent of the T group, a chi-square test is performed on the word, and the formula in formula (2) is:(2)x2=Nad−bc2a+ca+bb+dc+d,where: N is all data; a is material with w and in class t; b is data with w but not in class t; c is data without w but in class t; d is non-ba data, not in class t number.Then, the larger the chi-square value, it indicates thatt is an obvious feature to distinguish this class.


## 3.1.3. Word2vec Keyword Feature Extension

In the process of text mining, in order to avoid the problem of synonyms, content features need to be expanded, which are based on neural network language structures and are easier to obtain than word vectors. It uses cbow and cross Gram modes to express homosyllabic words and is a useful tool for obtaining the mathematical vectors [16]. Word2vec maps the up-down relationship of words in the article to the unified coordinate system to build a sparse matrix. The matrix reflects the association between each word. Huffman’s compression algorithm is adopted to reduce the weight of some popular words. The trained word vector can be used for clustering analysis, searching for synonyms, part of speech analysis, and many other natural language processing-related works.When defining the behavior of a student’s behavior trajectory in a certain geographical location in a certain time period, in addition to considering whether the student conducts relevant behavior in that location in that time period, the student’s behavior is obviously reflected in the number of times the student appears in this location. The more times the student eats in the same location, such as the same canteen, indicates that the location has a high degree of importance to the student’s behavior trajectory [17]. At the same time, the temporal and spatial attributes of behavior trajectory are also changing. The closer the selection time is to the current time, the more it can influence the students’ recent behavior. In order to reflect the differences of students’ behavior times in different places and the evolution trend of students’ behavior trajectory, the differences of boundary values and occurrence times over the research span should be taken into account. Therefore, this paper uses the cosine similarity method to calculate the similarity of student behavior, in which the definition of student s signing in at a certain time point t and a certain position P is e, then the student’s behavior at a certain point can be expressed by formula:(3)es,t,p=∑n=1Ns,t,pes,t,pfs,t,pt,where fs,t,pt is the marginal effect function of student s in time period t and position P, which is expressed by formula:(4)fs,t,pt=e1Hd−d0,where: d is the current date; d0 is the latest occurrence time; H represents the maximum value of the absolute value of the difference between p and the current time and date; Ns,t,p represents the total number of times that student s appears at point P in time period l; es,t,p indicates whether student s appears at point P in the time interval. If it occurs, es,t,p=1; Otherwise, es,t,p=0.Based on this, we can calculate the similarity of student behavior in the next step, and set the similarity calculation formula (5) of student s1 and student s2 as:(5)sims1,s2=∑t∈T∑p∈Pes1,t,p′es2,t,p′∑t∈T∑p∈Pes1,t,p′∑p∈p∑p∈Pes2,t,p′,where: T is the set of time intervals; P is the collection of student behavior places.Then, the value range ofsims1,s2 is [0,1]. If the behavior trajectories of two students are completely different, the value of sims1,s2 is 0. If the behavior trajectories of two students are completely similar, the value of sims1,s2 is 1. It can be found that the more similar the behavior of students s1 and s2, the greater the value of sims1,s2.The platform collects students’ school data from multiple sources and obtains 13 types of information including family income, labor force, educated children and family emergencies, so as to comprehensively evaluate students’ family economic status at multiple levels and dimensions, and provide more effective and reliable reference information for accurate funding [18]. The questionnaire mainly provides the factors of static family economic situation, which are divided into three types: value, degree, and right and wrong. Dynamic consumption can more effectively and practically evaluate the students’ family economic situation and consumption habits. Through the average effective consumption days and average consumption quota per month, these information can be added to the model as dynamic factors. The specific evaluation factors are shown in Figure 2.Figure 2
Comprehensive fuzzy evaluation factors.At present, the level of family economic difficulties in Colleges and universities is divided into four categories: general family economic difficulties, family economic difficulties, very difficult family economy, and non-difficult family economy [19]. According to different data types, different membership functions are used to evaluate them. For degree data such as family accidents and natural disasters, the membership degree is assigned according to its severity, and the membership function is shown in formula:(6)Severe degree:10.50,More serious:0.510,No such situation:010.For the numerical data such as labor force population ratio and monthly average consumption data, the membership function is used to calculate them. The membership function is shown in equations (7)–(9). The numerical interval is divided into three segments S1,S2,S3. The position of X is described by the membership function:(7)f1X1=1X1<S11−S2−X1S2−X1S1<X1≤S20X1>S2,(8)f2X1=0X1≤S1,X1≥S2X−SS−SS1<X1<S21−X−SS−SS2<X1<S21X1=S2,(9)f3X1=0X1<S2X1−S2S3−S2S2<X1≤S31X1>S3.Based on this, the membership degree of numerical data is calculated, combined with the degree assignment membership degree, that is, the membership degree of the evaluation object to different fuzzy subsets is determined, and the fuzzy relationship matrix is obtained, as shown in equation:(10)R=r11r12r13r21r22r23.........rn1rn2rn3,where rn represents the membership degree of the evaluation object to the fuzzy subset vj from the perspective of factor u, that is, it represents the reasonable relationship between each family difficulty factor and students. Multiply the relationship matrix by the weight w1,w2,…,wn of N poverty causing factors. The weight is calculated according to the expert scoring matrix. The scoring matrix is shown in Table 1.Table 1
Expert scoring matrix of poverty factors.
Labor population ratioAverage monthly consumptionSudden family changesNatural disasterThe family is seriously illLabor population ratio11/31/31/51/7Average monthly consumption311/51/31/5Sudden family changes35133Natural disaster531/311/3The family is seriously ill751/331


## 3.2. Overall Design of Platform

As shown in Figure3, the student information management platform is based on multidimensional student campus data such as campus card consumption records, dormitory access control records, library access control records, achievement records, and historical subsidy records. After a series of data processing steps such as data cleaning, desensitization, index calculation, missing value filling and Feature Engineering, according to different research objectives, combined with data mining technologies such as classification, clustering and outlier detection, the application pages of family economic difficulty judgment, student portrait, and refined supervision are generated to help the school understand students’ personality behavior and assist the school in daily management [20]. According to the above analysis, the process of building the platform is mainly divided into the following four parts:The first part (corresponding data source layer): Integrate heterogeneous data on campus to avoid the phenomenon of “information island” on each data platform. The establishment of university database lays a research foundation for the follow-up research objectives such as student behavior analysis and portrait construction.The second part (corresponding data processing layer): Format conversion and preprocessing of multidimensional data in the database, and then do corresponding feature engineering for different research contents, such as feature construction and feature extraction. In addition, in the process of student portrait research, build a four-tier portrait label system and calculate the indicators required in the system.The third part (corresponding model algorithm layer): Clarify the objectives of each module of the student information management platform, integrate the traditional statistical analysis method and data mining technology, and study the judgment of family economic difficulties, the construction of student portraits, and the early warning supervision of accurate management.The fourth part (corresponding application layer): Visualize the analysis results of the three modules, develop the student information management platform, and display the results of each module on the platform page, which can be applied to the actual campus management.Figure 3
Construction scheme of student information platform.As shown in Figure4, the student information management platform mainly designs and develops six functional modules: user management module, department management module, teacher management module, careful management module, accurate funding module, and fine supervision module.Figure 4
Function module of student information management platform.Among them, the user management module is mainly responsible for the management of platform user roles, including the addition, deletion, empowerment, and query functions of user accounts; The department management module and teacher management module are responsible for adding, deleting, querying and displaying the information of colleges, majors, classes, grades, and teachers; The careful management module mainly constructs student portraits in terms of students’ behavior, study, consumption habits and social interaction, and compares and displays the personal portraits of excellent students and students who need attention; The precise funding module combines the multisource campus data and data mining technology of students to determine the funding level of students with financial difficulties, as well as the management of work study application and student loan list of poor students; Fine supervision includes outlier students’ early warning, social closure early warning, and psychological abnormality early warning. It integrates questionnaire analysis and statistical analysis methods to give timely early warning to students with abnormal data, which is conducive to managers’ offline positioning and attention [21].


## 4. Results and Analysis

The quality of the data set determines the limits of machine learning, and the efficiency of the process can only be as close as possible to those limits. The information here refers to the information to be learned after a series of feature engineering steps. The role of the architect is to provide better training to bring the design closer to the aforementioned limits. Proper design can improve the performance of the model and even easily achieve the desired effect of the design. Therefore, in the whole data mining and analysis process, it is very important to choose a suitable engineering model. It is generally believed that feature engineering includes three stages of construction, removal, and special selection. Firstly, this paper uses the outlier detection algorithm (LOF) to carry out the first step operation on the model label data set to screen and eliminate the students with abnormal data. Then, input the model label data set of 10721 students into the improved algorithm. According to the model label system, the final predicted attribute labels include academic, consumption, behavior, and social attributes. The number of cluster categories should be selected first. As shown in Figure5, the elbow method was used to observe the convergence of SSE changes in the experiment. It was concluded that the convergence speed slowed down when the number of cluster categories was 10, so the number of cluster categories was 10.Figure 5
Change trend of SSE observed by elbow method.In order to more intuitively understand the data characteristics of each class cluster, the student group label data of class cluster 0, class cluster 3, class cluster 6, and class cluster 9 with large differences are selected for comparison, as shown in Figure6.Figure 6
Comparison of some group label data.Based on the analysis of the model label construction rules introduced above, it can be concluded that class A student dormitory library has high access regularity, but the dining regularity of canteen is low, social activity is low, and most of the scores are ranked lower, with large consumption expenses and low effort index. In the table, the direct value of life behavior of class B students is relatively small, that is, the regularity of going in and out of dormitories and libraries is high, and the regularity of dining in canteens is low. If such students rank high, that is, they have excellent academic performance, the highest effort index and relatively frugal spending in peacetime, including students from poor families. Class C students’ dormitories, libraries, and canteens have low dining regularity; most of them have medium academic ranking, poor family economic status, frugal spending, high social activity, and less residence in dormitories. ClassD students have irregular access to dormitories and libraries, strong dining regularity in canteens, most of their academic achievements are lower than medium, and their economic situation is good, but their social activity is very low, and they prefer to stay in dormitories. Based on the above analysis, class B students can be marked as an excellent group. Offline, class A students, and class D students can be paid attention to from different angles, urge them to focus on their studies, and encourage social participation in after-school activities. In order to facilitate managers to more intuitively understand the personality characteristics of each student, when constructing the label system for each student, the word cloud map of each student can also be constructed through the calculation of the label. For example, when sorting out students’ library borrowing records and access control records, it can be judged whether it is liberal arts or science by the type of books borrowed, and whether it is possible to go abroad for further study by borrowing IELTS- and TOEFL-related books [22]. In contrast, the behavior data of excellent students show that they have the characteristics of learning hegemony, self-discipline, diligence, frequent library visitors, and less social interaction. The behavior data of students who need attention show that they are socially active, poor grades, generous, and may go abroad for further study. Furthermore, to compare the results of the grouping algorithms, we perform combined experiments and lof-specific tests on the labels of the old model. Here, the square of the errors in the groups is used to determine the difference between the patterns in the groups. The smaller the distance, the smaller the mass dispersion and the better the clustering effect. The algorithm used in this paper has been shown to improve the distribution results.


## 5. Conclusion

This course focuses on student management based on big data, and collects data of various types of business in colleges and universities, including student data, student portraits from enrollment to graduation, knowledge of college student data life cycle management, and behavioral data mining and design. Referring to the student portrait, this paper establishes the identification model of students with financial difficulties based on fuzzy evaluation method, puts forward the whole life cycle management model of college student data, integrates, cleans and systematically stores the data, and uses the relevant components of Hadoop ecosystem to store and analyze the student data, which provides a data basis for the next step of college management. In the development of university informatization, our goal is to use big data to collect, process, check, mine/view data, and make my data more important in big data. At the same time, big data application technology plays an important role in improving data resource sharing and providing more complete data for measurement purposes. Big data not only makes it easier for students, faculty, scientists, and administrators, it also places higher demands on them. In order for big data to function and create sufficient value in colleges and universities, it is necessary to establish the collection and analysis of long-term in-school data, and to use the evaluation results to make educational decisions. It plays a key role in the support and utilization of big data in colleges and universities across the country.



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*Source: 7674573-2022-07-07.xml*