DocumentCode :
3265008
Title :
Research on Courses Relationship Model Based on Bayesian Networks
Author :
Huang, Jianming ; Fang, Jiaoli
Author_Institution :
Comput. Center, Kunming Univ. of Sci. & Technol., Kunming, China
Volume :
2
fYear :
2009
fDate :
6-7 June 2009
Firstpage :
15
Lastpage :
18
Abstract :
University courses are not isolated settings. There is a certain link among them. This paper presents a construction method for Bayesian networks of university courses relationship, which use the examination results of students´ courses as data sample. The 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. Such the Bayesian network of courses dependence relationship was obtained, and its condition probability table was learned by mathematical statistics method. This model had represented the dependence relationship of courses intuitively, and the condition probability table quantified tightness of relationship. It plays guidance role to the setting and arrangement of university courses, and it has the prediction ability to the studentspsila achievement.
Keywords :
belief networks; directed graphs; educational courses; learning (artificial intelligence); statistical analysis; Bayesian networks; condition probability table; courses relationship model; information theory; mathematical statistics method; structure learning algorithm; undirected graph; university courses; Bayesian methods; Computational intelligence; Computer networks; Data mining; Information theory; Isolation technology; Medical diagnosis; Probability; Random variables; Statistics; Bayesian networks; condition probability table; information theory; structure learning; undirected graph;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3645-3
Type :
conf
DOI :
10.1109/CINC.2009.222
Filename :
5231044
Link To Document :
بازگشت