01188nas a2200133 4500008003900000245006600039210006500105300001200170490000700182520079400189100001300983700001300996856004501009 2018 d00aConstructing Shadow Tests in Variable-Length Adaptive Testing0 aConstructing Shadow Tests in VariableLength Adaptive Testing a538-5520 v423 aImposing content constraints is very important in most operational computerized adaptive testing (CAT) programs in educational measurement. Shadow test approach to CAT (Shadow CAT) offers an elegant solution to imposing statistical and nonstatistical constraints by projecting future consequences of item selection. The original form of Shadow CAT presumes fixed test lengths. The goal of the current study was to extend Shadow CAT to tests under variable-length termination conditions and evaluate its performance relative to other content balancing approaches. The study demonstrated the feasibility of constructing Shadow CAT with variable test lengths and in operational CAT programs. The results indicated the superiority of the approach compared with other content balancing methods.1 aDiao, Qi1 aRen, Hao uhttps://doi.org/10.1177/014662161775373602100nas a2200181 4500008004100000245004600041210004600087260005500133520153800188653002501726653000801751100002801759700001901787700001301806700002001819700001301839856006601852 2017 eng d00aBayesian Perspectives on Adaptive Testing0 aBayesian Perspectives on Adaptive Testing aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

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

 

10aBayesian Perspective10aCAT1 avan der Linden, Wim, J.1 aJiang, Bingnan1 aRen, Hao1 aChoi, Seung, W.1 aDiao, Qi uhttp://www.iacat.org/bayesian-perspectives-adaptive-testing-0