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Article: New Item Selection Methods for Cognitive Diagnosis Computerized Adaptive Testing

TitleNew Item Selection Methods for Cognitive Diagnosis Computerized Adaptive Testing
Authors
Keywordscognitive diagnosis model
Issue Date2015
Citation
Applied Psychological Measurement, 2015, v. 39, n. 3, p. 167-188 How to Cite?
Abstract© The Author(s) 2014.This article introduces two new item selection methods, the modified posterior-weighted Kullback–Leibler index (MPWKL) and the generalized deterministic inputs, noisy “and” gate (G-DINA) model discrimination index (GDI), that can be used in cognitive diagnosis computerized adaptive testing. The efficiency of the new methods is compared with the posterior-weighted Kullback–Leibler (PWKL) item selection index using a simulation study in the context of the G-DINA model. The impact of item quality, generating models, and test termination rules on attribute classification accuracy or test length is also investigated. The results of the study show that the MPWKL and GDI perform very similarly, and have higher correct attribute classification rates or shorter mean test lengths compared with the PWKL. In addition, the GDI has the shortest implementation time among the three indices. The proportion of item usage with respect to the required attributes across the different conditions is also tracked and discussed.
Persistent Identifierhttp://hdl.handle.net/10722/228216
ISSN
2015 Impact Factor: 1.0
2015 SCImago Journal Rankings: 1.721

 

DC FieldValueLanguage
dc.contributor.authorKaplan, Mehmet-
dc.contributor.authorde la Torre, Jimmy-
dc.contributor.authorBarrada, Juan Ramón-
dc.date.accessioned2016-08-01T06:45:29Z-
dc.date.available2016-08-01T06:45:29Z-
dc.date.issued2015-
dc.identifier.citationApplied Psychological Measurement, 2015, v. 39, n. 3, p. 167-188-
dc.identifier.issn0146-6216-
dc.identifier.urihttp://hdl.handle.net/10722/228216-
dc.description.abstract© The Author(s) 2014.This article introduces two new item selection methods, the modified posterior-weighted Kullback–Leibler index (MPWKL) and the generalized deterministic inputs, noisy “and” gate (G-DINA) model discrimination index (GDI), that can be used in cognitive diagnosis computerized adaptive testing. The efficiency of the new methods is compared with the posterior-weighted Kullback–Leibler (PWKL) item selection index using a simulation study in the context of the G-DINA model. The impact of item quality, generating models, and test termination rules on attribute classification accuracy or test length is also investigated. The results of the study show that the MPWKL and GDI perform very similarly, and have higher correct attribute classification rates or shorter mean test lengths compared with the PWKL. In addition, the GDI has the shortest implementation time among the three indices. The proportion of item usage with respect to the required attributes across the different conditions is also tracked and discussed.-
dc.languageeng-
dc.relation.ispartofApplied Psychological Measurement-
dc.subjectcognitive diagnosis model-
dc.titleNew Item Selection Methods for Cognitive Diagnosis Computerized Adaptive Testing-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1177/0146621614554650-
dc.identifier.scopuseid_2-s2.0-84926454809-
dc.identifier.volume39-
dc.identifier.issue3-
dc.identifier.spage167-
dc.identifier.epage188-
dc.identifier.eissn1552-3497-

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