Clustering approach to polytomous IRS with application in statistics learning for university students

  • Authors:

  • Yuan-Horng Lin

  • Affiliations:

  • Department of Mathematics Education, National Taichung University, Taichung City, Taiwan, Taiwan

  • Venue:
  • ACACOS'10 Proceedings of the 9th WSEAS international conference on Applied computer and applied computational science
  • Year:
  • 2010

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Abstract

The purpose of this study is to integrate fuzzy clustering and polytomous item relational structure (PIRS) so that concept diagnosis could adaptively present features of concept structures for students. PIRS is beyond the limitations of dichotomous item relational structure (IRS) and PIRS is well efficient for polytomous response data. In addition, fuzzy clustering provide well algorithm for population partition so that homogeneity within the same cluster. Therefore, it will be well-designed that population clustering is analyzed in advance and then PIRS is to display concept structures. In this study, test data from statistics learning of university students will be an empirical data. This study adopts PIRS based on response data matrix to display graphic concept structures. The results show that the integration of PIRS and fuzzy clustering are useful for concept diagnosis and adaptive instruction. Finally, some suggestions and recommendations based on findings are discussed.