A Fast and Simple Algorithm for Bayesian Adaptive Testing. (February 2020)

• Record Type: Journal Article
• Authors: van der Linden, Wim J.; Ren, Hao
• Is Part Of: Journal of educational and behavioral statistics. Volume 45:Number 1(2020)
• Page Start: 58
• Page End: 85
• Publication Date: 2020-02
• Subjects: ability estimation -- adaptive testing -- Bayesian optimality -- Gibbs sampler -- item calibration -- item response models -- MCMC algorithm; Educational statistics -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 370.2
• DOI: 10.3102/1076998619858970
• Languages: English
• ISSNs: 1076-9986

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.