# Application of Teaching Administrating Informatization in Universities Based on the Era of Big Data

Authors: Difei Xi; Dick Sonbull
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The arrival of the era of big data has brought a great impact on China’s education industry and prompted the education industry to launch a series of educational reforms. Further use of information tools is an inevitable requirement of higher education reform, and many colleges and universities regard information construction as the foundation of higher education reform. In the process of informatization construction, there are bound to be various problems. By introducing the big data analysis system, this paper analyzes and summarizes the data collected by information systems such as the teaching resource database and the square educational administration system.K-MEANS clustering algorithm and association rule mining algorithm are designed. Aiming at the low time efficiency of Apriori algorithm of association rules, an improved I++ algorithm is proposed, and the association rules are applied to students’ behavior analysis, and experimental analysis is carried out. Experiments show that the running time of the improved algorithm is obviously shortened, and the time complexity is small. Because the algorithm uses Boolean matrix and stores Boolean values “0” or “1,” the space complexity is also small. Therefore, the improved Apriori algorithm I++ is faster and more efficient.


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## Body

## 1. Introduction

In the process of talent training and education in contemporary colleges and universities, education management is one of the key links, and the reform of information-based education can promote the long-term development of college education management. In recent years, with the continuous development of society, the education systems of various countries are also constantly developing and changing, and the education systems of various colleges and universities have also developed and innovated. Among them, the construction of education management informatization has become a hot topic of discussion.. In the context of the era of big data, higher education management needs to gradually transition to the direction of information management and use the advantages of big data technology and information tools to improve the overall quality and effect of education management [1]. However, there are still deficiencies in the current higher education informatization construction process. Schools need to conduct a comprehensive analysis of its causes and explore the path of informatization construction to improve the comprehensive level of higher education management and provide students with more comprehensive and efficient education management services.In this paper, aiming at the low time efficiency of Apriori algorithm of association rules, an improvedI++ algorithm is proposed, and the association rules are applied to the analysis of students’ behavior in teaching management. The above I++ algorithm is used to analyze the student behavior data, and the data samples are from the data generated by our independently developed teaching environment: Mac OS platform, hardware environment: Intel Core I10, 16g memory, 8t hard disk. SQL Server 2018 database and JavaEE development tool are used in the background. Through experiments, it is found that the running time of the improved algorithm is significantly shortened and the time complexity is small. Because the algorithm uses Boolean matrix and the storage is Boolean value “0” or “1,” the space complexity is also small. Therefore, the improved Apriori algorithm I++ is faster and more efficient. Research on preference data based on big data is of great significance to teaching and management. On the basis of mining, a personalized learning scheme is designed for everyone, which can teach students according to their aptitude and improve learning efficiency [2].To sum up, big data technology is a brand-new technical framework or mode in the field of data science. For all kinds of data with large data volume and complex types, which need immediate processing and value purification, new technologies such as data perception, collection, storage, processing, analysis, and visualization are comprehensively used to extract data value, so as to obtain profound and comprehensive knowledge and insight on the laws of nature and human society from the data. Based on the concept of big data, aiming at the information management of teaching activities in colleges and universities, and taking the research and mining of students’ learning preferences as the breakthrough point, this paper applies big data algorithm to the information management of education to optimize the management efficiency, which is of great guiding significance to the teaching reform [3]. The innovation lies in:
(1)
Carrying out personalized teaching activities. With the popularization of big data, more and more open courses are entering the education system, and online learning has become the second classroom for many students to acquire knowledge. Online courses relying on big data affect the traditional teaching activities. Compared with traditional teaching methods, online courses have more high-quality course resources, more flexible learning time, various multimedia videos, and interactive learning methods. The system records it in detail. When the data accumulates to a certain level, through the analysis and mining of these big data to find out the internal rules, the course resources can be further adjusted and optimized, and at the same time, the personalized learning that suits them can be pushed to the students.(2)
In the relevant teaching process, teachers in colleges and universities can take more flexible teaching forms such as flipping the classroom by using various online courses and network resources, which is conducive to integrating teaching practice and educational ideas into classroom teaching and realizing the trinity of theoretical knowledge, jobs, and teaching management(3)
In order to adapt to the changes of the Internet era, some excellent enterprises actively cooperate with universities, such as building big data laboratories in universities, bringing employment services, rigorous management system, and excellent certified lecturers and excellent learning environment into universities, and jointly cultivating highly skilled talents [4].This paper is divided into seven chapters from the organizational structure.The first chapter is the introduction. This part analyzes the current situation of higher education informatization, summarizes the causes of the problems, and designs effective big data technologies and algorithms for higher education informatization management, aiming to provide reference for the informatization construction of higher education. The purpose, method, and innovation of this paper are put forward. The second chapter mainly summarizes the relevant literature, summarizes its advantages and disadvantages, and puts forward the research ideas of this paper. The third chapter introduces the characteristics of big data. In Chapter 4, the practical application of big data technology in the construction of talent training system in colleges and universities is elaborated in detail, which is explained from theory and examples. In the fifth chapter, the simulation experiment ofI++ algorithm is carried out and the results are obtained. In Chapter 6, the specific development strategies of the informatization of teaching management in colleges and universities are prospected. Chapter 7 is the conclusion. This part mainly reviews the main contents and results of this study.


## 2. Related Work

There is no authoritative and precise definition of big data in the academic world. Xiaomei KE proposed three major characteristics of big data: data volume (Volume), data generation speed (Velocity), and data diversity (Variety), based on a large number of detailed statistical results. The connotation, extension, status quo, and technical trend of big data technology are analyzed [5]. On this basis, some people in the industry have summarized other big data characteristics, such as accuracy (Veracity), low value density (Value), and survivability (Viability). From the perspective of BI&A (Business Intelligence and Analysis Technology), Xin G M regards big data technology as the development direction of the next generation of BI&A, points out its technical connotation, and has a good understanding of the application of big data technology in data analysis, text analysis, web analysis, network analysis, etc. Technology and application modes in the fields of e-commerce and market intelligence, e-government and politics, science and technology, smart health and medical care, and public safety are analyzed [6]. Chen Y believes that big data technology has been extended from four Vs to three dimensions: real-time, interpretability, and data accuracy/stability [7]. Wang S believes that in the era of data engineering, big data needs to be operated, shared, and then mined and used to generate social value and solve future problems, and believes that in big data, data quality is very important [8]. Gao F introduced the emerging core concept of data science: Data Nature [9]. Sun J analyzes the characteristics of O2O user data through big data technology, proposes a data mining model, and discusses the data mining process and the main methods of the data mining process [10]. Gao FC, China Software Evaluation Center, studied the key technologies for utilizing big data, and expounded the opportunities and challenges faced, and finally summarized the US government’s “Big Data Research and Development Program” [11]. Lin X discussed the characteristics, concepts, and technologies of big data, especially in data mining and the challenges we face in the era of big data [12]. Lu W first discusses the principles, basic concepts, and applications of big data, then studies the big data processing framework, and finally introduces the new challenges brought by “big data” [13]. Qiu X Y of China Information Technology Education analyzed educational materials through data mining technology and introduced the Moodle function [14].


## 3. Analysis of the Characteristics of Big Data

Big data technology has been gradually applied to various fields, and its scope is becoming wider and wider. The information management of higher education is no exception. Its emergence not only improves the overall mechanism of education but also optimizes the innovation awareness of education. These are attributed to the characteristics of big data, as shown in Figure1.Figure 1
Features of big data.First, the huge data capacity. Under the background of big data, cloud computing, Internet of Things, and Internet plus technologies are in an optimized state of development, realizing the huge development of big data capacity systems [15].Second, the diversification of data types. With the analysis of sensors, smart devices, and social collaboration, it can provide rich structural support for data types. By optimizing the structural system of traditional relational data, text, audio, and video, it can realize the reasonable adjustment of unstructured data.Third, the speed of data processing. Under the background of the optimized development of network technology, it is necessary to effectively process data resources through the optimized analysis of software performance. At the same time, in the process of data processing and analysis, the management data can be systematically analyzed to ensure the front-end of educational management data mining and effectively improves the core requirements of higher education management [16].Fourth, the authenticity analysis of data value. In data system analysis, the determination of data volume needs authentic data system as the support. Through scientific processing of big data resources, the basic connotation of management data can be found in time, and the optimization of work project design in big data environment can be demonstrated.


## 4. Application Research of Big Data Technology in the Construction of Talent Training System in Colleges and Universities

The evaluation index system is established by means of teaching management informatization, as shown in Figure2. The established student comprehensive ability evaluation system is a dynamic file that records the growth of students, provides key information for all departments of the school, and provides dynamic digital support for evaluating teaching effects and implementation effects. In the specific implementation, the information platform is adopted. Use big data technology for management, use quantitative indicators in the implementation process, collect data in the action process, analyze each indicator, conduct specific chart analysis on each dimension of data, directly move from goal to summary, and conduct normalized process analysis [17].Figure 2
Modules of students’ comprehensive ability analysis system.


### 4.1. The Processing Flow of Big Data

First, extract and integrate data sources; second, use aggregation and association to obtain entities and relationships; finally, use effective methods to store these data. In order to ensure the feasibility and quality of the data, it is necessary to clean the data first when extracting and integrating the data, and analyzing the data in the process of big data processing is the top priority [18]. The information extracted from the source data is the value of big data. Visualization techniques can be used to allow users to participate in and understand the analysis process to some extent, as shown in Figure 3.Figure 3
The process of mining knowledge from data.


### 4.2. Preference Data Analysis and Modeling

The key to personalized recommendation is to build a user preference model, which aims to help users select interesting information from a large amount of information. User preference mining can adopt explicit acquisition and implicit mining methods. Explicit access, that is, direct access to user preference information. This method requires the direct participation of users, and it is done by registering information or filling in a preference information form. Implicit mining, that is, the information of students’ learning preferences is obtained by mining users’ history of browsing the web [19].Taking the student preference information data generated by a teaching system as an example, this paper discusses the acquisition of long-term learning preferences. When students study, whether browsing learning resources or doing problems in various subjects, they need to register their accounts first, in order to store the basic information of students. Information that students need to provide to register an account includes name, age, gender, student number, class, subject preference, admission results, and student self-evaluation. Among them, basic variables (subject preference and students’ self-evaluation) play a decisive role in students’ long-term learning preference [20–22].Establish the student user registration information vector. The student registration information includes name, age, gender, student number, class, subject preference, admission score, and student self-evaluation. At this point, a vectorY= (name, age, gender, student ID, class, subject preference, entrance examination results, and students’ self-evaluation) can be used to represent the basic information of students and then converted into the form of vector component values, namely:
(1)Y=y1,y2,y3,⋯y8.For example, for gender, 1 is female and 0 is male.According to the basic information vector of students, this paper usesK-MEANS clustering algorithm to cluster students into K-class stable user sets. Let the set of data points be:
(2)P=Y1,Y2,⋯,Ym.Among them,(3)Yi=yi1,yi2,⋯yi8,i=1,2,⋯,m.Calculate the new center point of the cluster,Q1M+1,Q2M+1,⋯,QkM+1, the calculation formula is:
(4)QiM+1=1mi∑Yj∈DiYji,i=1,2,⋯,k,wheremi is the number of points in cluster Di, and let the mean error criterion function:
(5)FM+1=∑Yj−QiM2/1mi.If(6)FM+1−FM∂,then the algorithm ends; otherwise,M=M+1; return to continue.User learning preference is affected by both short-term learning interest and long-term learning interest, so the user’s learning interest preference document can be expressed as:(7)D=M,N,whereM represents short-term learning interest, and N represents long-term learning interest. Due to the variety of learning interests, M and N are, respectively, represented as:
(8)M=S1,S2,⋯,Sn,(9)N=L1,L2,⋯,Ln.For users’ various short-term and long-term learning interests, the learning interest vector should contain a lot of resource information in order to distinguish the degree of users’ interest and their categories in more detail. For eachSi, class attribute variable Ei, Ei, and weight attribute variable Fi are introduced, so Si,Li is expressed as:
(10)Si=Si,Fi,Ei,i=1,2,⋯,m,Lj=Lj,Fj,Ej,j=1,2,⋯,n.With the decline of students’ interest, students’ short-term learning interest preference gradually tends to be 0. Therefore, the user preference matrix becomes very sparse. To solve this problem, new learning resources are provided for short-term learning interests through long-term learning interest association recommendation.The following is a comparison chart of the number of students in the decision-making time of the two models, as shown in Figure4. In order to analyze the effect of the comprehensive model of students’ long-term and short-term learning preference we constructed, we compared the model with the old model of short-term learning preference based on web log data and analyzed their pros and cons about “decision time.”Figure 4
Comparison of decision-making time between old and new models.From the figure, we can clearly see that the number of people who complete the same decision time after applying the new model is much more than the old model. Therefore, it can be proved that the comprehensive model of students’ long-term and short-term learning preference is reasonable and effective, and the model can significantly improve the students’ learning efficiency.


### 4.3. Personalized Teaching Activities Based on Big Data

Big data technology provides the possibility for the development of personalized teaching, which can realize the transformation of education from groups to individuals and make personalized learning and differentiated teaching possible. Through the analysis of various data of teaching activities, such as the number of classroom speeches, the number of questions, and the completion of homework, teachers can discover the strengths and weaknesses of each student, so as to achieve hierarchical teaching, personalized assignments, and accurate guidance and compensation, which can provide students with a better learning environment, thereby contributing to the overall development of students. The comparison between precise teaching based on big data and traditional teaching mode is shown in Table1.Table 1
Comparison of precise teaching based on big data and traditional teaching mode.
Traditional modeBig data acquisition, analysis, and compensationOn-campusTeachingTraditional teachingLayered teachingEvaluationFollow the scorePay attention to the lack of knowledge points and abilitiesReviewAnalyze wrong questionsFind the root causeOff-campusOperationUnified workPersonalized homeworkReviewIndependent reviewPrecise compensationTeaching management involves many aspects. It is based on big data and constantly generates new data. Big data technology plays a key role in teaching management. Using big data technology, it focuses on teaching management activities, processes, and various decisions, and comprehensively collects big data for teaching management. For example, in various fields such as scientific research, teaching evaluation, and teaching quality control, systematic planning and sorting are carried out, and data collection standards are standardized to form big data for teaching management of the whole university. At the same time, it can also record the data of important management objects from different directions and sources, so as to confirm each other and establish the data of multisource management objects. In a word, big data technology can promote the scientific and refined teaching management, which is worthy of wide promotion.


### 4.4. Learning Situation Analysis Based on Big Data

Teachers in colleges and universities can generally remember students’ positions in classes and grades. However, teachers cannot know every student’s situation because they do not have a good grasp of specific knowledge points in the learning process. However, many students often know that there are problems in their studies, but they do not know where there are problems. After the introduction of big data analysis system, these problems have been solved. For example, when the data analysis system summarizes and exports student A’s mastery of each subject, you can get Table2.Table 2
Summary table of students’ mastery of subject A.
Whole schoolWhole classStudent AMath82.30%79.30%90.60%Accounting84.20%91.80%94.20%Programming83.60%79.30%84.20%Management84.30%81.20%77.20%Computer application87.20%86.10%87.30%Legal basis86.90%84.90%88.20%Economic law87.30%84.70%90.70%Practical writing90.20%86.50%86.40%SQL application86.40%87.10%87.00%The table is digitized and displayed as the following Figure5. In this way, teachers and students can further intuitively analyze students’ mastery of various subjects in the whole academic year and can also guide teachers’ teaching and students’ learning according to students’ performance trends. Big data analysis allows teachers not only to know the overall situation of their own disciplines but also to be specific to every student and every knowledge point.Figure 5
Intuitive map of students’ mastery of subject A.Observing Table2 and Figure 5, it is not difficult to find that student A’s mastery of accounting is better than the average level of the whole class and the whole school, but the mastery of management is obviously weaker than that of the whole class and the whole school.


### 4.5. Analysis of Elective Subjects Based on Big Data

Figure6: Taking the analysis of middle school student A’s subject selection as an example, the teaching management system takes this student as a sample, divides the number of students into 100 equal parts, and introduces the academic ability index so that the top 1% of the school’s results will be counted as 1.00, and the top 2% will be recorded as 0.99. By analogy, the academic ability of students in each subject can be intuitively shown. As students should fully consider whether they accept and like the subject in addition to their grades, we have introduced another coefficient, namely, the subject acceptance index, into the data analysis system. The index is given by the students themselves, and they like to be downgraded from 1.00 to 0.00 for dislike. Multiply the academic ability index by the subject acceptance index to obtain the subject recommendation index.Figure 6
Analysis of the elective subjects of student A.Based on big data analysis, we can intuitively set student A’s elective subject as tourism management.


### 4.6. Changes in Academic Ranking Based on Big Data

Take student A as a research example, and import the scores of his previous examinations into the database of the teaching information system, so as to visually compare the changes of his academic ranking throughout the year, as shown in Figure7.Figure 7
The change curve of student A’s ranking throughout the year.This is included in the student management system to form an individual situation analysis chart, which is conducive to the school management and students’ own comparative understanding and improvement of learning.


## 4.1. The Processing Flow of Big Data

First, extract and integrate data sources; second, use aggregation and association to obtain entities and relationships; finally, use effective methods to store these data. In order to ensure the feasibility and quality of the data, it is necessary to clean the data first when extracting and integrating the data, and analyzing the data in the process of big data processing is the top priority [18]. The information extracted from the source data is the value of big data. Visualization techniques can be used to allow users to participate in and understand the analysis process to some extent, as shown in Figure 3.Figure 3
The process of mining knowledge from data.


## 4.2. Preference Data Analysis and Modeling

The key to personalized recommendation is to build a user preference model, which aims to help users select interesting information from a large amount of information. User preference mining can adopt explicit acquisition and implicit mining methods. Explicit access, that is, direct access to user preference information. This method requires the direct participation of users, and it is done by registering information or filling in a preference information form. Implicit mining, that is, the information of students’ learning preferences is obtained by mining users’ history of browsing the web [19].Taking the student preference information data generated by a teaching system as an example, this paper discusses the acquisition of long-term learning preferences. When students study, whether browsing learning resources or doing problems in various subjects, they need to register their accounts first, in order to store the basic information of students. Information that students need to provide to register an account includes name, age, gender, student number, class, subject preference, admission results, and student self-evaluation. Among them, basic variables (subject preference and students’ self-evaluation) play a decisive role in students’ long-term learning preference [20–22].Establish the student user registration information vector. The student registration information includes name, age, gender, student number, class, subject preference, admission score, and student self-evaluation. At this point, a vectorY= (name, age, gender, student ID, class, subject preference, entrance examination results, and students’ self-evaluation) can be used to represent the basic information of students and then converted into the form of vector component values, namely:
(1)Y=y1,y2,y3,⋯y8.For example, for gender, 1 is female and 0 is male.According to the basic information vector of students, this paper usesK-MEANS clustering algorithm to cluster students into K-class stable user sets. Let the set of data points be:
(2)P=Y1,Y2,⋯,Ym.Among them,(3)Yi=yi1,yi2,⋯yi8,i=1,2,⋯,m.Calculate the new center point of the cluster,Q1M+1,Q2M+1,⋯,QkM+1, the calculation formula is:
(4)QiM+1=1mi∑Yj∈DiYji,i=1,2,⋯,k,wheremi is the number of points in cluster Di, and let the mean error criterion function:
(5)FM+1=∑Yj−QiM2/1mi.If(6)FM+1−FM∂,then the algorithm ends; otherwise,M=M+1; return to continue.User learning preference is affected by both short-term learning interest and long-term learning interest, so the user’s learning interest preference document can be expressed as:(7)D=M,N,whereM represents short-term learning interest, and N represents long-term learning interest. Due to the variety of learning interests, M and N are, respectively, represented as:
(8)M=S1,S2,⋯,Sn,(9)N=L1,L2,⋯,Ln.For users’ various short-term and long-term learning interests, the learning interest vector should contain a lot of resource information in order to distinguish the degree of users’ interest and their categories in more detail. For eachSi, class attribute variable Ei, Ei, and weight attribute variable Fi are introduced, so Si,Li is expressed as:
(10)Si=Si,Fi,Ei,i=1,2,⋯,m,Lj=Lj,Fj,Ej,j=1,2,⋯,n.With the decline of students’ interest, students’ short-term learning interest preference gradually tends to be 0. Therefore, the user preference matrix becomes very sparse. To solve this problem, new learning resources are provided for short-term learning interests through long-term learning interest association recommendation.The following is a comparison chart of the number of students in the decision-making time of the two models, as shown in Figure4. In order to analyze the effect of the comprehensive model of students’ long-term and short-term learning preference we constructed, we compared the model with the old model of short-term learning preference based on web log data and analyzed their pros and cons about “decision time.”Figure 4
Comparison of decision-making time between old and new models.From the figure, we can clearly see that the number of people who complete the same decision time after applying the new model is much more than the old model. Therefore, it can be proved that the comprehensive model of students’ long-term and short-term learning preference is reasonable and effective, and the model can significantly improve the students’ learning efficiency.


## 4.3. Personalized Teaching Activities Based on Big Data

Big data technology provides the possibility for the development of personalized teaching, which can realize the transformation of education from groups to individuals and make personalized learning and differentiated teaching possible. Through the analysis of various data of teaching activities, such as the number of classroom speeches, the number of questions, and the completion of homework, teachers can discover the strengths and weaknesses of each student, so as to achieve hierarchical teaching, personalized assignments, and accurate guidance and compensation, which can provide students with a better learning environment, thereby contributing to the overall development of students. The comparison between precise teaching based on big data and traditional teaching mode is shown in Table1.Table 1
Comparison of precise teaching based on big data and traditional teaching mode.
Traditional modeBig data acquisition, analysis, and compensationOn-campusTeachingTraditional teachingLayered teachingEvaluationFollow the scorePay attention to the lack of knowledge points and abilitiesReviewAnalyze wrong questionsFind the root causeOff-campusOperationUnified workPersonalized homeworkReviewIndependent reviewPrecise compensationTeaching management involves many aspects. It is based on big data and constantly generates new data. Big data technology plays a key role in teaching management. Using big data technology, it focuses on teaching management activities, processes, and various decisions, and comprehensively collects big data for teaching management. For example, in various fields such as scientific research, teaching evaluation, and teaching quality control, systematic planning and sorting are carried out, and data collection standards are standardized to form big data for teaching management of the whole university. At the same time, it can also record the data of important management objects from different directions and sources, so as to confirm each other and establish the data of multisource management objects. In a word, big data technology can promote the scientific and refined teaching management, which is worthy of wide promotion.


## 4.4. Learning Situation Analysis Based on Big Data

Teachers in colleges and universities can generally remember students’ positions in classes and grades. However, teachers cannot know every student’s situation because they do not have a good grasp of specific knowledge points in the learning process. However, many students often know that there are problems in their studies, but they do not know where there are problems. After the introduction of big data analysis system, these problems have been solved. For example, when the data analysis system summarizes and exports student A’s mastery of each subject, you can get Table2.Table 2
Summary table of students’ mastery of subject A.
Whole schoolWhole classStudent AMath82.30%79.30%90.60%Accounting84.20%91.80%94.20%Programming83.60%79.30%84.20%Management84.30%81.20%77.20%Computer application87.20%86.10%87.30%Legal basis86.90%84.90%88.20%Economic law87.30%84.70%90.70%Practical writing90.20%86.50%86.40%SQL application86.40%87.10%87.00%The table is digitized and displayed as the following Figure5. In this way, teachers and students can further intuitively analyze students’ mastery of various subjects in the whole academic year and can also guide teachers’ teaching and students’ learning according to students’ performance trends. Big data analysis allows teachers not only to know the overall situation of their own disciplines but also to be specific to every student and every knowledge point.Figure 5
Intuitive map of students’ mastery of subject A.Observing Table2 and Figure 5, it is not difficult to find that student A’s mastery of accounting is better than the average level of the whole class and the whole school, but the mastery of management is obviously weaker than that of the whole class and the whole school.


## 4.5. Analysis of Elective Subjects Based on Big Data

Figure6: Taking the analysis of middle school student A’s subject selection as an example, the teaching management system takes this student as a sample, divides the number of students into 100 equal parts, and introduces the academic ability index so that the top 1% of the school’s results will be counted as 1.00, and the top 2% will be recorded as 0.99. By analogy, the academic ability of students in each subject can be intuitively shown. As students should fully consider whether they accept and like the subject in addition to their grades, we have introduced another coefficient, namely, the subject acceptance index, into the data analysis system. The index is given by the students themselves, and they like to be downgraded from 1.00 to 0.00 for dislike. Multiply the academic ability index by the subject acceptance index to obtain the subject recommendation index.Figure 6
Analysis of the elective subjects of student A.Based on big data analysis, we can intuitively set student A’s elective subject as tourism management.


## 4.6. Changes in Academic Ranking Based on Big Data

Take student A as a research example, and import the scores of his previous examinations into the database of the teaching information system, so as to visually compare the changes of his academic ranking throughout the year, as shown in Figure7.Figure 7
The change curve of student A’s ranking throughout the year.This is included in the student management system to form an individual situation analysis chart, which is conducive to the school management and students’ own comparative understanding and improvement of learning.


## 5.I++ Algorithm Application

In view of the low time efficiency of the Apriori algorithm of association rules, an improvedI++ algorithm is proposed, and the association rules are applied to the analysis of students’ behavior, and experimental analysis is carried out. Now, 10 students are selected from the students. First, the quality assessment of learning enthusiasm and knowledge point mastery (in descending order) is divided into 1, 2, 3, etc., and the relationship with grades is shown in Figure 8.Figure 8
Students’ enthusiasm for learning, mastery of knowledge points, and academic performance.Experiments show that the running time of the improved algorithm is obviously shortened, and the time complexity is small. Because the algorithm uses Boolean matrix and stores Boolean values “0” or “1,” the space complexity is also small. Therefore, Apriori’s improved algorithm -—I+++ is faster and more efficient. In theI++ algorithm, the transaction set is searched only once, which is different from the Apriori algorithm in two aspects: (1) The data structure (linked list) used to calculate the support of candidate item sets is different in storing information. In the I++ algorithm, each node of the linked list is 1 table. By solving the intersection of two frequent item sets, the candidate item set support degree can be obtained. However, in Apriori algorithm, it is necessary to search the whole linked list to get the support of a candidate item set. Therefore, the time required to get frequent item sets by I++ algorithm is shorter than that by Apriori algorithm, which greatly improves the work efficiency. (2) In terms of generating candidate item sets, in Apriori algorithm, two steps are needed: combination and reduction, while in I++ algorithm, only the combination step is needed.Using the above samples and using the improvedI++ algorithm for data mining, we can see that excellent students have good learning enthusiasm, knowledge points, and learning ability. Such students study hard and spend most of their time studying. Good level students have balanced grades, strong learning enthusiasm, comprehensive knowledge, and strong learning ability. Middle level students are not very active in learning, their knowledge points are not very comprehensive, and their learning ability is relatively loose.


## 6. Specific Development Strategies for the Informatization of Education and Teaching Management in Colleges and Universities

In the era of cloud computing and big data, colleges and universities should formulate corresponding development strategies, apply cloud computing and big data technology in the process of college education and teaching management, improve the management level of colleges and universities, and enhance the informatization development level of colleges and universities.First of all, university leaders should pay full attention to the importance of informatization of education and teaching management and establish a perfect informatization organization responsibility system within the scope of universities. It is necessary to build an important institution with strong authority and decision-making ability in the informatization of education and teaching management, so as to realize the communication and exchange between different departments and give full play to the role of different institutions. Colleges and universities should formulate the responsibility system of information organizations, be responsible for the implementation of information management in colleges and universities, and then transform the education and teaching management in colleges and universities.Second, it is necessary to formulate clear educational and teaching management informatization goals and formulate scientific management plans. Educational institutions should formulate development plans suitable for the informatization of education and teaching management in local colleges and universities in light of the actual local conditions. Constructing scientific informatization facilities, formulating perfect informatization management mechanisms, and realizing informatization training of talents provide strong support and guarantee for the development of informatization in education and teaching management in colleges and universities.Third, colleges and universities should build a complete education and teaching information management facility to provide guarantee for the realization of the goal of college information management. College education and teaching management informatization software should be supported by college campus network, and the good operation of campus network is an important prerequisite for the realization of college education and teaching management informatization. In the application process of the information management system, colleges and universities should clarify the division of labor among different staff, ensure the information storage, information maintenance, and data update of the college education and teaching information management system, and promote the good operation of the college education and teaching management system.Then, strengthen the training of education and teaching information management talents, and regularly organize personnel to participate in information management training, learn the latest information management technology, improve the information technology level of information management talents, and ensure the smooth operation of education and teaching information management software.Finally, colleges and universities should formulate a scientific evaluation system for the informatization of education and teaching management. It is necessary to establish a set of teaching management evaluation system that is suitable for the informatization of education and teaching management in Colleges and universities, realize the perfect combination of information technology and teachers’ teaching quality evaluation, closely combine information technology with the overall development evaluation of colleges and universities, and combine information technology with teachers’ appointment.


## 7. Conclusions

To sum up, in the future, big data technology will surely penetrate into all aspects of teaching management in colleges and universities. At the same time, big data technology will also greatly promote the communication and interaction between colleges and universities and the society and strengthen the connection between them. Colleges and universities can adjust teaching plans and personnel training plans in a timely manner according to social needs, and the society can timely and accurately transform teaching results into production and management in various fields according to the teaching trends of schools. Big data technology organically links colleges and universities, society and students, and can effectively promote the rapid development of economy and society.In the context of the era of big data, in order to achieve educational informatization management in colleges and universities, it is necessary to propose corresponding solutions for educational management problems, in order to ensure the application value of big data technology and promote the construction of educational management informatization. Colleges and universities should strengthen their understanding of the concept of informatization, improve the professional quality of construction personnel through training or external employment, and form a professional informatization construction team. In order to ensure the smooth development of education informatization management, it is necessary to establish and improve an informatization management system, so as to fully reflect the application value of big data technology. Informatization construction can also effectively integrate campus resources, innovate teaching methods, create a more convenient campus environment for college students, and provide more learning resources.



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*Source: 4184109-2022-08-12.xml*