Application of Fuzzy Structural Modeling in Knowledge Structure with Feature Clustering
The purpose of this study is to provide an integrated methodology of cognitive representation for knowledge structures based on fuzzy structural modeling (FSM) and fuzzy clustering.In the form of graphic representation and clustering results, it is easy to describe features of knowledge structure. Calculation based on item response theory (IRT) and fuzzy logical model of perception (FLMP) generate subordinate matrix for fuzzy structural modeling (FSM) to exhibit individualized knowledge structure. Analysis from student-problem chart (S-P chart) and fuzzy similarity compared with expert provided resource for fuzzy clustering. Therefore, results from the integrated methodology are important and useful for adaptive and remedial instruction. A test data set on equality axiom concepts for sixth graders is analyzed so that results exhibited features of knowledge structure of each group. Individualized knowledge structure displays hierarchies,linkage and relationship among concepts. The integrated methodology should be feasible for intelligent technology on remedial instruction and cognition diagnosis. Finally, some recommendation and suggestions are discussed and provided for future research.
cognition diagnosis fuzzy clustering fuzzy structural modeling knowledge structure
Yuan-Homg Lin He-Kai Chert
Department of Mathematics Education National Taichung University Taichung City, Taiwan Magong Elementary School Magong, Taiwan
国际会议
The 10th International Conference on Intelligent Technologies(第十届智慧科技国际会议 InTech09)
桂林
英文
636-642
2009-12-12（万方平台首次上网日期，不代表论文的发表时间）