Dual-Objective Item Selection Criteria in Cognitive Diagnostic Computerized Adaptive Testing

Autor/inn/en	Kang, Hyeon-Ah; Zhang, Susu; Chang, Hua-Hua
Titel	Dual-Objective Item Selection Criteria in Cognitive Diagnostic Computerized Adaptive Testing
Quelle	In: Journal of Educational Measurement, 54 (2017) 2, S.165-183 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/jedm.12139
Schlagwörter	Computer Assisted Testing; Adaptive Testing; Cognitive Tests; Test Items; Item Banks; Selection Criteria; Probability; Statistical Distributions + Suchen Sie Ihr Suchwort? Kognitiver Fähigkeitstest; Test content; Testaufgabe; Selection criterion; Auslesekriterium; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Wahrscheinlichkeitsverteilung
Abstract	The development of cognitive diagnostic-computerized adaptive testing (CD-CAT) has provided a new perspective for gaining information about examinees' mastery on a set of cognitive attributes. This study proposes a new item selection method within the framework of dual-objective CD-CAT that simultaneously addresses examinees' attribute mastery status and overall test performance. The new procedure is based on the Jensen-Shannon (JS) divergence, a symmetrized version of the Kullback-Leibler divergence. We show that the JS divergence resolves the noncomparability problem of the dual information index and has close relationships with Shannon entropy, mutual information, and Fisher information. The performance of the JS divergence is evaluated in simulation studies in comparison with the methods available in the literature. Results suggest that the JS divergence achieves parallel or more precise recovery of latent trait variables compared to the existing methods and maintains practical advantages in computation and item pool usage. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01