Restrictive Stochastic Item Selection Methods in Cognitive Diagnostic Computerized Adaptive Testing

Autor/inn/en	Wang, Chun; Chang, Hua-Hua; Huebner, Alan
Titel	Restrictive Stochastic Item Selection Methods in Cognitive Diagnostic Computerized Adaptive Testing
Quelle	In: Journal of Educational Measurement, 48 (2011) 3, S.255-273 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/j.1745-3984.2011.00145.x
Schlagwörter	Test Items; Adaptive Testing; Computer Assisted Testing; Cognitive Tests; Diagnostic Tests; Statistical Analysis; Comparative Analysis + Suchen Sie Ihr Suchwort? Test content; Testaufgabe; Kognitiver Fähigkeitstest; Diagnostic test; Diagnostischer Test; Statistische Analyse
Abstract	This paper proposes two new item selection methods for cognitive diagnostic computerized adaptive testing: the restrictive progressive method and the restrictive threshold method. They are built upon the posterior weighted Kullback-Leibler (KL) information index but include additional stochastic components either in the item selection index or in the item selection procedure. Simulation studies show that both methods are successful at simultaneously suppressing overexposed items and increasing the usage of underexposed items. Compared to item selection based upon (1) pure KL information and (2) the Sympson-Hetter method, the two new methods strike a better balance between item exposure control and measurement accuracy. The two new methods are also compared with Barrada et al.'s (2008) progressive method and proportional method. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10