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A Fast and Simple Algorithm for Bayesian Adaptive Testing

Author

Listed:
  • Wim J. van der Linden

    (University of Twente)

  • Hao Ren

    (Pearson)

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.

Suggested Citation

  • Wim J. van der Linden & Hao Ren, 2020. "A Fast and Simple Algorithm for Bayesian Adaptive Testing," Journal of Educational and Behavioral Statistics, , vol. 45(1), pages 58-85, February.
    • Handle: RePEc:sae:jedbes:v:45:y:2020:i:1:p:58-85
    DOI: 10.3102/1076998619858970
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    References listed on IDEAS

    as
    1. Robert Tsutakawa & Jane Johnson, 1990. "The effect of uncertainty of item parameter estimation on ability estimates," Psychometrika, Springer;The Psychometric Society, vol. 55(2), pages 371-390, June.
    2. Wim Linden & Hao Ren, 2015. "Optimal Bayesian Adaptive Design for Test-Item Calibration," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 263-288, June.
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    Citations

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    Cited by:

    1. Wim J. van der Linden & Bingnan Jiang, 2020. "A Shadow-Test Approach to Adaptive Item Calibration," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 301-321, June.

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