Reference : Adaptive estimators of trait level in adaptive testing: Some proposals
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Physical, chemical, mathematical & earth Sciences : Mathematics
Adaptive estimators of trait level in adaptive testing: Some proposals
[fr] Estimateurs adaptatifs du niveau d'habileté en testing adaptatif: quelques propositions
Raîche, Gilles mailto [ > > ]
Blais, Jean-Guy [ > > ]
Magis, David mailto [Université de Liège - ULiège > Département de mathématique > Statistique mathématique >]
Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing
[fr] Actes du congrès GMAC 2007
Weiss, David
Graduate Management Admission Council Conference on Computerized Adaptive Testing (GMAC)
juin 2007
Minneapolis (MN)
[en] Computerized adaptive testing ; Adaptive estimation ; Ability
[en] In a computerized adaptive test (CAT), we seek an acceptably accurate trait (θ) level estimate using an optimal number of items. Bayesian estimation methods like MAP and EAP are often used to compute that estimate. Unfortunately, with such methods, decreasing the number of items generates bias whenever the true θ level differs significantly from the a priori estimate. Adaptive versions of the maximum a posteriori and expected a posteriori estimation methods are proposed to reduce this bias. These AMAP and AEAP methods adapt the a priori values used in the estimation function according to the previously computed θ estimate obtained from the previous administered item. The performance of AMAP and AEAP is evaluated in a CAT context.

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