A Monte Carlo Approach for Adaptive Testing With Content Constraints

This article presents a new algorithm for computerized adaptive testing (CAT) when content constraints are present. The algorithm is based on shadow CAT methodology to meet content constraints but applies Monte Carlo methods and provides the following advantages over shadow CAT: (a) lower maximum it...

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Bibliographic Details
Published inApplied psychological measurement Vol. 32; no. 6; pp. 431 - 446
Main Authors Belov, Dmitry I., Armstrong, Ronald D., Weissman, Alexander
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publications 01.09.2008
Subjects
Adaptive Testing
College Admission
College Entrance Examinations
Computation
Computer Assisted Testing
Computer Simulation
Evaluation Methods
Law Schools
Monte Carlo Methods
Test Items
test assembly
Monte Carlo methods
item response theory
automated test assembly
shadow test assembly
computerized adaptive testing
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Summary:This article presents a new algorithm for computerized adaptive testing (CAT) when content constraints are present. The algorithm is based on shadow CAT methodology to meet content constraints but applies Monte Carlo methods and provides the following advantages over shadow CAT: (a) lower maximum item exposure rates, (b) higher utilization of the item pool, and (c) more robust ability estimates. Computer simulations with Law School Admission Test items demonstrated that the new algorithm (a) produces similar ability estimates as shadow CAT but with half the maximum item exposure rate and 100% pool utilization and (b) produces more robust estimates when a high- (or low-) ability examinee performs poorly (or well) at the beginning of the test.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0146-6216
1552-3497
DOI:10.1177/0146621607309081