An association rule mining approach for intelligent tutoring system
Yuemin Li, Shenghui Zhao · 2010
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 can't 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.