An Association Rule Mining Approach for Intelligent Tutoring System
Intelligent tutoring system (ITS) creates a new teaching mode,but most ITS are merely e-learning platforms that provide course study,without considering learning processes of learners,which cant effectively help learners to consolidate and review the unmastered knowledge points. Data mining techniques can extract the potential,valuable pattern or regulation from a great quantity of data. An intelligent tutoring system has been designed based on data mining technology that could return the learners feedback about knowledge points. In order to quickly find all frequent patterns,i.e.,knowledge points,an improved algorithm for mining association rules based on FP-growth is presented. Experimental results show that the improved algorithm can provide effective decision support,and help learners to improve their learning efficiency.
data mining intelligent tutoring system association rule frequent itemset
Yuemin Li Shenghui Zhao
Department of Chemistry and Life Science Chuzhou University Chuzhou,China Department of Computer Science and Technology Chuzhou University Chuzhou,China
国际会议
成都
英文
3932-3936
2010-04-16（万方平台首次上网日期，不代表论文的发表时间）