可拓数据挖掘在高校教学质量评价中的应用  

The Application of Extension Data Mining in Higher Institution Teaching Quality Evaluation

作　　者：方耀楣[1] 何万篷[1] 

机构地区：[1]同济大学经济与管理学院,上海200092

出　　处：《数学的实践与认识》2009年第4期82-87,共6页Mathematics in Practice and Theory

基　　金：同济大学2006教育教学改革与研究重点项目

摘　　要：高校在教学和管理工作中积累了大量的数据,但这些数据没有得到有效利用.将可拓数据挖掘技术引入教学领域,从教学评价数据中提取出隐藏在数据之中的有用信息,为教学管理者提供决策支持.首先通过可拓分析,寻找质量达到要求、可进行有效挖掘的教学评价数据,然后对这些数据进行两方面的挖掘:影响教学质量的关键因素挖掘、教学质量与教师特征之间的关联规则挖掘.The higher institution has accumulated a large number of data in teaching and management, but these data have not got effective use. In this paper, extension data mining techniques are used in the teaching field. Hidden useful information is extracted from the teaching evaluation data to offer decision support to teaching administrator. First, look for the qualified teaching evaluation data through extension analysis. Then excavate these data from two respects：the excavating of key factors that influence teaching quality and the excavating of related rule between teaching quality and teacher＇s characteristic.

关 键 词：教学评价 可拓方法 数据挖掘 关联规则 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]