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Cluster analysis for cognitive diagn...
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Chiu, Chia-Yi.
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Cluster analysis for cognitive diagnosis: Theory and applications.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Cluster analysis for cognitive diagnosis: Theory and applications./
作者:
Chiu, Chia-Yi.
面頁冊數:
109 p.
附註:
Source: Dissertation Abstracts International, Volume: 69-11, Section: A, page: 4240.
Contained By:
Dissertation Abstracts International69-11A.
標題:
Educational psychology. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3337778
ISBN:
9780549912057
Cluster analysis for cognitive diagnosis: Theory and applications.
Chiu, Chia-Yi.
Cluster analysis for cognitive diagnosis: Theory and applications.
- 109 p.
Source: Dissertation Abstracts International, Volume: 69-11, Section: A, page: 4240.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.
This item is not available from ProQuest Dissertations & Theses.
Latent class models for cognitive diagnosis often begin with specification of a matrix that indicates which attributes or skills are needed for each item. Then by imposing restrictions that take this into account, along with a theory governing how subjects interact with items, parametric formulations of item response functions are derived and fitted. Cluster analysis provides an alternative approach that does not require specifying an item response model, but does require an item-by-attribute matrix. After summarizing the data with a particular vector of sum-scores, K-means cluster analysis or hierarchical agglomerative cluster analysis can be applied with the purpose of clustering subjects who possess the same skills. Asymptotic classification accuracy results are given, along with simulations comparing effects of test length and method of clustering. An application to a language examination is provided to illustrate how the methods can be implemented in practice.
ISBN: 9780549912057Subjects--Topical Terms:
517650
Educational psychology.
Cluster analysis for cognitive diagnosis: Theory and applications.
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Latent class models for cognitive diagnosis often begin with specification of a matrix that indicates which attributes or skills are needed for each item. Then by imposing restrictions that take this into account, along with a theory governing how subjects interact with items, parametric formulations of item response functions are derived and fitted. Cluster analysis provides an alternative approach that does not require specifying an item response model, but does require an item-by-attribute matrix. After summarizing the data with a particular vector of sum-scores, K-means cluster analysis or hierarchical agglomerative cluster analysis can be applied with the purpose of clustering subjects who possess the same skills. Asymptotic classification accuracy results are given, along with simulations comparing effects of test length and method of clustering. An application to a language examination is provided to illustrate how the methods can be implemented in practice.
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