对于天津财经大学统计学成绩的数据挖掘研究

发布时间：2018-03-31 11:10

本文选题：学生成绩　切入点：数据挖掘　出处：《天津财经大学》2016年硕士论文

【摘要】：近来来信息技术的发展,使得学生信息管理系统得以在各大学中得到普遍使用,这极大地提高了学校管理的效率。然而,现在的学生成绩管理系统往往只有储存、查询等简单的功能,使得数据的背后暗藏的价值得不到有效利用。学校多年来积累了大量的考试数据,如何使用系统中存储的学生数据,得到对学校有用的信息,成为了当务之急。文章使用数据挖掘技术,通过对近两年全校的《统计学》课程成绩数据进行分析,以期挖掘出有价值的信息,为任课老师的教学和学生们的自我成绩提高提供建议。本文介绍了数据挖掘技术的概念,其中重点介绍了 K-Means算法和Apriori算法,并将这两种算法应用到成绩的分析中。本文数据源是天津财经大学全校本科生近两年的《统计学》的课程成绩,以此为基础研究与学生成绩相关的信息。对原始数据进行了预处理,并根据相关教材把两年的试题按知识点所属章节生成了统计基础、数理统计、时间序列、指数分析四个新的指标。同时由于关联规则分析的需要,对两年试题中的新生产的四个指标和学生总成绩进行了数据离散化。随后对数据进行了描述性统计,按照性别的分数差异、各任课教师的分数差异、各题型的分数差异以及各学院的分数差异进行了描述性统计和相关分析,使得对数据有了大概的了解。之后应用了 K-Means算法对数据进行聚类,把不同学生按成绩分成了五类,根据每类学生的不同情况提出了不同的指导意见。应用Apriori算法进一步分析系别,性别和总成绩,各科成绩的关联规则,得出了各个因素的相关规则,并提出了要重视统计基础部分知识的建议。
[Abstract]:Recent developments in information technology have led to the widespread use of student information management systems in universities, which has greatly improved the efficiency of school management. Simple functions such as query make the hidden value behind the data not effectively utilized. Over the years, the school has accumulated a large amount of test data, how to use the student data stored in the system to get useful information for the school. This article uses data mining technology to analyze the data of the whole school's course scores in the past two years, in order to find out the valuable information. This paper introduces the concept of data mining technology, including K-Means algorithm and Apriori algorithm. The data source of this paper is the course scores of Statistics for the undergraduate students of Tianjin University of Finance and Economics in the past two years. On this basis, we study the information related to the students' achievement. We preprocess the raw data, and according to the relevant textbooks, we generate the statistical basis, mathematical statistics, time series according to the chapters of the knowledge points. At the same time, because of the need of association rule analysis, the data of the four newly produced indexes and the total scores of students in the two years' test are discretized. Then, the data are analyzed by descriptive statistics. According to the gender score difference, the teacher's score difference, the question type score difference as well as the each college score difference has carried on the descriptive statistics and the correlation analysis. So that we have a general understanding of the data. Then we use the K-Means algorithm to cluster the data and divide different students into five categories according to their grades. According to the different situations of each kind of students, different guidance is put forward. The correlation rules of each factor are obtained by applying Apriori algorithm to further analyze the association rules of the department, gender and total score, and the results of each subject. Some suggestions are put forward to pay more attention to the basic knowledge of statistics.
【学位授予单位】：天津财经大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP311.13;G642

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