【摘要】：在現(xiàn)代化的高等教育管理中,信息化水平逐年提高,隨著校園一卡通的廣泛使用以及歷年各大業(yè)務系統(tǒng)數(shù)據(jù)的積累,形成了校園大數(shù)據(jù)環(huán)境。主要體現(xiàn)在學生的數(shù)據(jù)大規(guī)模、多類型、高速度、低密度價值幾個特點,如何有效挖掘?qū)W生一卡通數(shù)據(jù)成為提升學生工作信息化管理水平的重要內(nèi)容。本課題主要研究本科學生一卡通數(shù)據(jù)及相應的業(yè)務系統(tǒng)中保存的在校學生的各種行為(學習行為、生活行為、心里行為)數(shù)據(jù),數(shù)據(jù)包括學生的消費數(shù)據(jù)、校醫(yī)院看診數(shù)據(jù)、進出門禁數(shù)據(jù)、圖書館借閱數(shù)據(jù)、考試成績、上網(wǎng)時長等海量數(shù)據(jù)。分析數(shù)據(jù)探究學生學習、生活和心理等方面的相關(guān)關(guān)系,挖掘?qū)W生異常數(shù)據(jù)、反饋異常數(shù)據(jù),充分利用學生在校行為數(shù)據(jù)建設數(shù)字校園、智慧校園,使得校園信息化水平得以提升。本文通過搭建學生行為大數(shù)據(jù)分析系統(tǒng),以中共北京市委教育工作委員會首都大學生思想政治教育研究課題《大數(shù)據(jù)視角下高校學生工作一卡通數(shù)據(jù)分析與應用》為依托,對學生在校行為數(shù)據(jù)進行挖掘研究,主要完成以下內(nèi)容:(1)整合學校各大業(yè)務系統(tǒng)歷史數(shù)據(jù),結(jié)合學生在校一卡通中的各類數(shù)據(jù)進行分析,并對異常數(shù)據(jù)進行相關(guān)處理。(2)研究大數(shù)據(jù)框架Hadoop的HDFS文件系統(tǒng)和MapReduce計算模型,搭建基于Hadoop技術(shù)的高校學生行為大數(shù)據(jù)分析系統(tǒng)的總體技術(shù)架構(gòu),并利用計算模型MapReduce對高校學生行為數(shù)據(jù)進行挖掘處理。(3)將學生行為數(shù)據(jù)測點進行歸約,梳理不同行為之間的關(guān)聯(lián)關(guān)系,繪制學生在校的“學生畫像”,清晰的描繪學生在校情況,關(guān)聯(lián)分析學生的學習情況、生活狀態(tài)以及心理動態(tài)之間的關(guān)系。(4)構(gòu)建高校家庭經(jīng)濟困難學生認定模型,利用模糊評價方法隸屬度的概念結(jié)合大數(shù)據(jù)分析系統(tǒng)中的學生一卡通消費數(shù)據(jù)和家庭情況調(diào)查表中的數(shù)據(jù),確定學生隸屬等級,通過隸屬度的相對大小來確定其貧困等級。(5)實現(xiàn)學生行為大數(shù)據(jù)分析系統(tǒng),分析總結(jié)學生行為規(guī)律與特性,提出具有建設性的參考意見供相關(guān)部門分析,以便于分析學生行為特點,及時的指導學生行為全面健康的發(fā)展。(6)完成大數(shù)據(jù)分析系統(tǒng)在學校家庭經(jīng)濟困難學生認定工作中的實際應用,利用模型認定的方式代替輔導員以往憑借經(jīng)驗認定的定性分析,將認定工作定性化向定量化轉(zhuǎn)變,提高學生工作的效率和認定結(jié)果的科學性及可靠性。
[Abstract]:In the modern management of higher education, the level of information has been improved year by year. With the extensive use of campus card and the accumulation of data of each major business system over the years, the environment of campus big data has been formed. It is mainly reflected in the characteristics of large scale, multi-type, high speed and low density value of students' data. How to effectively mine students' one-card data has become an important content in improving the level of information management of students' work. This subject mainly studies the data of undergraduate students' one-card and the data of students' behavior (study behavior, life behavior, psychological behavior) stored in the corresponding business system. The data include the data of students' consumption, the data of hospital consultation, the data of students' consumption, and the data of hospital consultation. Access to access data, library borrowing data, test scores, Internet access and other massive data. Analyze the data to explore the relationship between students' study, life and psychology, dig out the abnormal data of students, feedback the abnormal data, make full use of the data of students' behavior in school to build the digital campus and intelligent campus. So that the level of information on campus can be improved. Based on the subject of ideological and political education of college students of the Beijing Municipal Committee of the Communist Party of China (CPC), this paper builds up an analysis system of student behavior by big data. < Analysis and Application of the data of one Card in Student work in Colleges and Universities from the Perspective of big data. The main contents of this paper are as follows: (1) integrating the historical data of the major business systems of the school, and analyzing the various data in the student's school card, the main contents of the research are as follows: (1) integrating the historical data of the major business systems of the school, And related to the abnormal data processing. (2) study big data framework Hadoop HDFS file system and MapReduce computing model, and build the overall technical framework of college student behavior analysis system based on Hadoop technology. And using the computational model MapReduce to mine the data of college students' behavior. (3) reducing the measuring points of students' behavior data, combing the relationship between different behaviors, and drawing the student's "student portrait" in the school. Clearly describe the situation of students in school, and analyze the relationship among students' learning, living conditions and psychological dynamics. (4) construct the identification model of students with financial difficulties in colleges and universities. By using the concept of membership degree of fuzzy evaluation method combined with the data of student card consumption in big data analysis system and the data of family situation questionnaire, the grade of student membership is determined. The relative size of membership degree is used to determine the poverty grade. (5) to realize the analysis system of student behavior big data, analyze and summarize the law and characteristics of students' behavior, and put forward constructive suggestions for relevant departments to analyze. In order to analyze the characteristics of students' behavior and guide the students' behavior to develop healthily in time. (6) to complete the practical application of big data analysis system in the work of identifying the students with financial difficulties in school families, In order to improve the efficiency of student work and the scientificity and reliability of the result, the way of model identification is used to replace the qualitative analysis of the counselor's experience in the past, and the qualitative analysis of the identification work is changed from the qualitative work to the quantitative one.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13

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