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.
| Original language | English |
|---|---|
| Journal | Journal of educational and behavioral statistics |
| DOIs | |
| Publication status | E-pub ahead of print/First online - 10 Jul 2019 |
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Keywords
- UT-Hybrid-D
- adaptive testing
- Bayesian optimality
- Gibbs sampler
- item calibration
- item response models
- MCMC algorithm
- ability estimation
Cite this
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A Fast and Simple Algorithm for Bayesian Adaptive Testing. / van der Linden, Wim J.; Ren, Hao.
In: Journal of educational and behavioral statistics, 10.07.2019.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - A Fast and Simple Algorithm for Bayesian Adaptive Testing
AU - van der Linden, Wim J.
AU - Ren, Hao
N1 - Sage deal
PY - 2019/7/10
Y1 - 2019/7/10
N2 - 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.
AB - 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.
KW - UT-Hybrid-D
KW - adaptive testing
KW - Bayesian optimality
KW - Gibbs sampler
KW - item calibration
KW - item response models
KW - MCMC algorithm
KW - ability estimation
UR - http://www.scopus.com/inward/record.url?scp=85068831092&partnerID=8YFLogxK
U2 - 10.3102/1076998619858970
DO - 10.3102/1076998619858970
M3 - Article
JO - Journal of educational and behavioral statistics
JF - Journal of educational and behavioral statistics
SN - 1076-9986
ER -