Construct Knowledge Structure of Linear Algebra

Abstract:

Apply interpretive structural modeling to construct knowledge structure of linear algebra. New fuzzy clustering algorithms improved fuzzy c-means algorithm based on Mahalanobis distance has better performance than fuzzy c-means algorithm. Each cluster of data can easily describe features of knowledge structures individually. The results show that there are six clusters and each cluster has its own cognitive characteristics. The methodology can improve knowledge management in classroom more feasible.

• Periodical: Advanced Materials Research (Volumes 211-212)
• Pages: 793-797
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.211-212.793
• Online since: February 2011
• Authors: Chin Chun Chen, Yuan Horng Lin, Jeng Ming Yih, Sue Fen Huang
• Keywords: Fuzzy C-Means Algorithm, Interpretive Structural Modeling (ISM), Knowledge Structure, Mahalanobis Distance

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