A Fast and Simple Algorithm for Bayesian Adaptive Testing

Wim J. van der Linden and Hao Ren
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Wim J. van der Linden: University of Twente
Hao Ren: Pearson

Journal of Educational and Behavioral Statistics, 2020, vol. 45, issue 1, 58-85

Abstract: The Bayesian way of accounting for the effects of error in the ability and item parameters in adaptive testing is through the joint posterior distribution of all parameters. An optimized Markov chain Monte Carlo algorithm for adaptive testing is presented, which samples this distribution in real time to score the examinee’s ability and optimally select the items. Thanks to extremely rapid convergence of the Markov chain and simple posterior calculations, the algorithm is ready for use in real-world adaptive testing with running times fully comparable with algorithms that fix all parameters at point estimates during testing.

Keywords: ability estimation; adaptive testing; Bayesian optimality; Gibbs sampler; item calibration; item response models; MCMC algorithm (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:45:y:2020:i:1:p:58-85

DOI: 10.3102/1076998619858970

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