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Bao, Yu; Bradshaw, Laine – Measurement: Interdisciplinary Research and Perspectives, 2018
Diagnostic classification models (DCMs) can provide multidimensional diagnostic feedback about students' mastery levels of knowledge components or attributes. One advantage of using DCMs is the ability to accurately and reliably classify students into mastery levels with a relatively small number of items per attribute. Combining DCMs with…
Descriptors: Test Items, Selection, Adaptive Testing, Computer Assisted Testing
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Wang, Chun – Journal of Educational and Behavioral Statistics, 2014
Many latent traits in social sciences display a hierarchical structure, such as intelligence, cognitive ability, or personality. Usually a second-order factor is linearly related to a group of first-order factors (also called domain abilities in cognitive ability measures), and the first-order factors directly govern the actual item responses.…
Descriptors: Measurement, Accuracy, Item Response Theory, Adaptive Testing
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Wang, Chun – Educational and Psychological Measurement, 2013
Cognitive diagnostic computerized adaptive testing (CD-CAT) purports to combine the strengths of both CAT and cognitive diagnosis. Cognitive diagnosis models aim at classifying examinees into the correct mastery profile group so as to pinpoint the strengths and weakness of each examinee whereas CAT algorithms choose items to determine those…
Descriptors: Computer Assisted Testing, Adaptive Testing, Cognitive Tests, Diagnostic Tests
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Yao, Lihua – Applied Psychological Measurement, 2013
Through simulated data, five multidimensional computerized adaptive testing (MCAT) selection procedures with varying test lengths are examined and compared using different stopping rules. Fixed item exposure rates are used for all the items, and the Priority Index (PI) method is used for the content constraints. Two stopping rules, standard error…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Selection
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Han, Kyung T. – Journal of Educational Measurement, 2012
Successful administration of computerized adaptive testing (CAT) programs in educational settings requires that test security and item exposure control issues be taken seriously. Developing an item selection algorithm that strikes the right balance between test precision and level of item pool utilization is the key to successful implementation…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Selection
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Wang, Wen-Chung; Liu, Chen-Wei – Educational and Psychological Measurement, 2011
The generalized graded unfolding model (GGUM) has been recently developed to describe item responses to Likert items (agree-disagree) in attitude measurement. In this study, the authors (a) developed two item selection methods in computerized classification testing under the GGUM, the current estimate/ability confidence interval method and the cut…
Descriptors: Computer Assisted Testing, Adaptive Testing, Classification, Item Response Theory
Spray, Judith A.; Reckase, Mark D. – 1994
The issue of test-item selection in support of decision making in adaptive testing is considered. The number of items needed to make a decision is compared for two approaches: selecting items from an item pool that are most informative at the decision point or selecting items that are most informative at the examinee's ability level. The first…
Descriptors: Ability, Adaptive Testing, Bayesian Statistics, Computer Assisted Testing