NDLI: Polytomous IRS with Application in Concepts Diagnosis and Clustering on Fraction Subtraction for Pupils

Content Provider	IEEE Xplore Digital Library
Author	Yuan-Horng Lin Jeng-Ming Yih Min-Yen Chen
Copyright Year	2008
Description	Author affiliation: Nat. Taichung Univ., Taichung (Yuan-Horng Lin; Jeng-Ming Yih) \|\| Hui Wen Elementary Sch. (Min-Yen Chen)
Abstract	The purpose of this study is to provide the polytomous item relational structure (PIRS) analysis and utilize it in fraction subtraction concepts diagnosis with classification on pupils by fuzzy clustering. Beyond the limitations of dichotomous item relational structure (IRS), PIRS is well efficient in the analysis of polytomous data. Most IRS application aims to analyze item hierarchies, not concept hierarchies. However, hierarchies of conceptual attributes will be meaningful for pedagogy and PIRS could provide graphical concept hierarchies according to response data matrix and item-concept matrix. PIRS analysis based on results of clustering on subjects would be more effective for remedial instruction. In this study, empirical data of fraction subtraction testing for sixth graders was analyzed by fuzzy clustering and PIRS is applied to display concept hierarchies for each group. The results show that the integration of PIRS and fuzzy clustering are feasible for cognition diagnosis and remedial instruction. Finally, some suggestions and recommendations are discussed.
Starting Page	463
Ending Page	467
File Size	248951
Page Count	5
File Format	PDF
ISBN	9780769534978
DOI	10.1109/IITA.2008.544
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data analysis cognition diagnosis Psychology Displays Cognition Mathematics remedial instruction Intelligent structures Information technology Information analysis concept hierarchy fuzzy clustering item relational structure Testing
Content Type	Text
Resource Type	Article

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