A Fast and Simple Algorithm for Bayesian Adaptive Testing

Autor/inn/en	van der Linden, Wim J.; Ren, Hao
Titel	A Fast and Simple Algorithm for Bayesian Adaptive Testing
Quelle	In: Journal of Educational and Behavioral Statistics, 45 (2020) 1, S.58-85 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998619858970
Schlagwörter	Bayesian Statistics; Adaptive Testing; Error of Measurement; Markov Processes; Monte Carlo Methods; Item Response Theory; Computation; Sampling + Suchen Sie Ihr Suchwort? Messfehler; Markowscher Prozess; Monte-Carlo-Methode; Item-Response-Theorie
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01