Title :
Discovering the Most Adaptive Students of One Course by Data Mining
Author :
Mao Keji ; Yu MingYuan ; Chen Qingzhang
Author_Institution :
Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
In the paper, we discover the most adaptive students of one course by association rule and classification analysis. First, we improve the performance of the previous Apriori to reduce some unnecessary steps, as a result, we can get more efficient association rules to mine the course, and then discover the most adaptive student according to the characteristic displayed by the association rule; secondly, we discover the students´ attribute characteristic of one course through classification analysis, the students with the attribute characteristic are the most adaptive students of the course and should be recommended. We can create the behavior mode of course-select through above methods and take it as useful reference to students and schools.
Keywords :
computer aided instruction; data mining; educational courses; adaptive student; association rule; attribute characteristic; classification analysis; data mining; Association rules; Data mining; Databases; Educational institutions; Electronic mail; Information analysis; Information systems; Itemsets; Paper technology; Performance analysis; Association Rules; Classification; Data Mining;
Conference_Titel :
Web Information Systems and Mining, 2009. WISM 2009. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3817-4
DOI :
10.1109/WISM.2009.57
