摘要
提出一种大学课程关系的贝叶斯网络构造方法,以学生课程考试成绩作为数据样本,以基于信息论的结构学习算法构造无向图,最后以课程开设的先后顺序给边定向,得到课程依赖关系的贝叶斯网络,并以数理统计的方法学习其条件概率表。该模型直观的反映了课程间的依赖联系,而条件概率表则量化了联系的紧密程度,对大学课程的设置和编排具有指导作用,对学生成绩具有预测能力。
University courses are not isolated settings; there is a certain link between them. This paper presents a construction method for university courses relationship Bayesian networks, which use the examination results of students＇ courses as data sample, undirected graph was constructed with structure learning algorithm based on information theory, and its edges were oriented according to the time order of courses opening, the Bayesian network of courses dependency relationship was obtained, and its condition probability table was learned by mathematical statistics method. This model had represented the dependence relation of courses intuitively, and the condition probability table quantified tightness of relationship, it plays guidance role to the setting and arrangement of university courses, it has the prediction ability to the student achievement.
出处
《贵州大学学报（自然科学版）》
2009年第2期81-84,共4页
Journal of Guizhou University:Natural Sciences
基金
昆明理工大学校基金资助项目(2007-55)
关键词
贝叶斯网络
无向图
结构学习
条件概率表
Bayesian network
undirected graph
structure learning
condition probability table
