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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
van der Linden, Wim J.; Xiong, Xinhui – Journal of Educational and Behavioral Statistics, 2013
Two simple constraints on the item parameters in a response--time model are proposed to control the speededness of an adaptive test. As the constraints are additive, they can easily be included in the constraint set for a shadow-test approach (STA) to adaptive testing. Alternatively, a simple heuristic is presented to control speededness in plain…
Descriptors: Adaptive Testing, Heuristics, Test Length, Reaction Time
Moyer, Eric L.; Galindo, Jennifer L.; Dodd, Barbara G. – Educational and Psychological Measurement, 2012
Managing test specifications--both multiple nonstatistical constraints and flexibly defined constraints--has become an important part of designing item selection procedures for computerized adaptive tests (CATs) in achievement testing. This study compared the effectiveness of three procedures: constrained CAT, flexible modified constrained CAT,…
Descriptors: Adaptive Testing, Computer Assisted Testing, Test Items, Item Analysis
Samejima, Fumiko; Changas, Paul S. – 1981
The methods and approaches for estimating the operating characteristics of the discrete item responses without assuming any mathematical form have been developed and expanded. It has been made possible that, even if the test information function of a given test is not constant for the interval of ability of interest, it is used as the Old Test.…
Descriptors: Adaptive Testing, Latent Trait Theory, Mathematical Models, Methods