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数据挖掘技术在高校学生成绩分析中应用的研究

Research of Data Mining When Analyzing the Marks of College Students

【作者】 张莉

【导师】 朱连章;

【作者基本信息】 中国石油大学 , 计算机技术, 2010, 硕士

【摘要】 努力提高学生的成绩是每一所高校的目标,学生成绩是评估教学质量的重要依据。现在高校规模不断扩大,学生数量越来越多,随着社会的发展,影响学生学习成绩的因素也越来越多,学生成绩分析就更加重要。对学生成绩进行分析,从大量数据存在的关系、规则中研究学生成绩,预测成绩发展趋势,从而对教师的教学环节提出有针对性的建议,对学生管理工作有的放矢,提高授课和学习效果已显得非常重要。故本文引进了近年来兴起的数据挖掘技术用于高校学生成绩分析,以找到影响学生成绩的根本原因及所映射到的相关问题原因,从而可以制定相应的措施,提高教学质量。本文在数据仓库和数据挖掘知识透彻理解的基础上,首先系统研究数据挖掘各项技术工具、方法、模式以及如何在高校学生成绩分析中应用数据挖掘理论,并详细的分析现有的每一种应用及其模型优缺点和各自适应环境;其次以研究决策树、聚类等方法原理为基础,引入数据仓库技术;最后针对目前学生成绩分析的各种模型,根据上述理论进行研究,设计出学生成绩分析的方案,建立新的挖掘模式,研究时间、课程、年度、年龄、生源地等与学生成绩的关系,得出新的更有价值的规律。

【Abstract】 It is the aim that each university makes great efforts to improve student’s achievement, because the quality of teaching in a university is based on students’achievement. Enrolment expansion results in the increasing quantities of students in campus. With the development of society, the factors that affect students’academic records are also becoming more and more. In such situation, the analysis of students’achievement is even more important. Analyzing their achievements from large amount of data and existing regulations, educators can predict the possible results. Thus, teachers can get some pointed advice on how to teaching effectively. So this paper has introduced Data Mining being on the rise in recent years to do achievement analysis, and expected to find the basic reasons affecting achievement and causes related. Thereby, educators can work out the corresponding measure and improve the quality of education.With the help of full awareness of data mining and data warehouse, the author first systematically studies various kinds of technical implements, methods, patterns and how to use data mining; and analyzes in detail each of the existing models and their environment when operated. Secondly, the author introduces the technology of data mining on the basis of researching decision tree and clustering. Finally, with the help of the above-mentioned theory, the author has designed a program and built new mining mode by aiming at the various models that analyze student achievement and at last has gotten new and more valuable discipline by studing the relationship between students’achievement and time, course , year , age , descent and so on.

【关键词】 数据挖掘数据仓库决策树聚类
【Key words】 Data MiningData Warehousedecision treeclustering
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