TY - CONF T1 - Bayesian Perspectives on Adaptive Testing T2 - IACAT 2017 Conference Y1 - 2017 A1 - Wim J. van der Linden A1 - Bingnan Jiang A1 - Hao Ren A1 - Seung W. Choi A1 - Qi Diao KW - Bayesian Perspective KW - CAT AB -

Although adaptive testing is usually treated from the perspective of maximum-likelihood parameter estimation and maximum-informaton item selection, a Bayesian pespective is more natural, statistically efficient, and computationally tractable. This observation not only holds for the core process of ability estimation but includes such processes as item calibration, and real-time monitoring of item security as well. Key elements of the approach are parametric modeling of each relevant process, updating of the parameter estimates after the arrival of each new response, and optimal design of the next step.

The purpose of the symposium is to illustrates the role of Bayesian statistics in this approach. The first presentation discusses a basic Bayesian algorithm for the sequential update of any parameter in adaptive testing and illustrates the idea of Bayesian optimal design for the two processes of ability estimation and online item calibration. The second presentation generalizes the ideas to the case of 62 IACAT 2017 ABSTRACTS BOOKLET adaptive testing with polytomous items. The third presentation uses the fundamental Bayesian idea of sampling from updated posterior predictive distributions (“multiple imputations”) to deal with the problem of scoring incomplete adaptive tests.

Session Video 1

Session Video 2

 

JF - IACAT 2017 Conference PB - Niigata Seiryo University CY - Niigata, Japan ER -