数据挖掘在教学评价中的应用研究
The research of the teaching management system based on the data mining technology
Abstract:In recent years, with the rapid development of information technology. We have achieve tremendous progress in many fields .At the same time, There have created a lot of data and in the university there also have a large number of data about student’s score and teacher’s rating results. There are many important information behind the fast development of the data. But most of the information system in the university is online transaction processing system.It does not have comprehensive analysis and decision support capabilities. Which makes the teacher can not get the information about the class teach. And the college is also lack of the data for decision-making basis.
Teaching management data business analysis is an important means to improve the teaching quality of management, In this paper we use data mining technology to find the helpful information hidden in the data about student’s score and teacher’s rating results.To improvement the teaching quality .Supporting to find the way to improve the teacher and student’s quality.
This paper discuss the application of data mining techniques in teaching management. focusing on it’s score and teacher’s rating results , as well as the hidden relationship between the teachers college type of mining association factor. It will bring as many helpful information about the in the professional building, course construction. teacher training. It’s also an important guiding significance
Keywords :Data mining Association rules Teaching Evaluation
目 录
一、绪论1
（一）研究背景及意义1
（二）数据挖掘的研究现状1
（三）课题研究的意义和目的2
二、数据挖掘技术及应用3
（一）数据挖掘的概念3
（二）数据挖掘过程3
（三）数据挖掘的方法,5
（四）关联算法的分析6
1、 关联规则挖掘概述6
2、 关联规则的基本概念6
3、 Apriori算法9
三、在教学评价方面的结果分析10
（一）问题的定义10
（二）数据挖掘的实施步骤10
（三）挖掘关联规则的步骤14
（四）数据分析结论15
四、结束语24
致谢25
参考文献26
一、绪论
（一） 研究背景及意义
以数据库和信息技术的发展为技术保障,以网络技术的迅速普及为发展通道,以计算机硬件数据收集设备和存储介质的大量供应为物质基础,人们的数据收集能力得到了大幅的提高,社会各行业都存储了大量的有关自己的生产管理和科研的各种信息,全球范围内数据存储量正急剧增加。然而与此形成鲜明对比的是,人们对数据的处理能力和理解能力并没有得到十分明显的提高,仅仅依靠传统的数据检索和统计分析等方法己远远不能满足现在的需要,导致现在出现数据丰富,知识贫乏的局面，为从海量的数据中找出数据变化的规律和数据之间的相互关系,充分发掘数据的潜力和效用。
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