A Method of Cross-level Frequent Pattern Mining for Web-based Instruction
Yueh Min Huang, Juei-Nan Chen, Shu Cheng · 2008
Due to the rise of e-Learning, more and more useful learning materials are open to public access. Therefore, an appropriate learning suggestion mechanism is an important tool to enable learners to work more efficiently. A smoother learning process increases the learning effect, avoiding unnecessarily difficult concepts and disorientation during learning. However, many suggestion demands come from different abstraction levels, and traditional single level frequent pattern mining is not sufficient. This paper proposes a methodology for mining frequent patterns of learners ’ behavior which connote a hierarchical scheme to provide cross-level learning suggestions instead of merely single level frequent pattern mining results. Our study shows that the algorithms can mine considerable quantities of frequent patterns from real life learning data. The experimental data are collected from a Web learning system originating from National Cheng Kung University in Taiwan. The proposed methodology gives learners many suggestions to help them learn more effectively and efficiently. Finally, we collect some representative cases to realize different requirements which are extracted from a learners ’ access database. These cases are classified into three types; notably, type three generalized four meaningful external factors which are inferred by our observations from these cross-level frequent patterns.