Online Calibration of a Joint Model of Item Responses and Response Times in Computerized Adaptive Testing

Autor/inn/en	Kang, Hyeon-Ah; Zheng, Yi; Chang, Hua-Hua
Titel	Online Calibration of a Joint Model of Item Responses and Response Times in Computerized Adaptive Testing
Quelle	In: Journal of Educational and Behavioral Statistics, 45 (2020) 2, S.175-208 (34 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kang, Hyeon-Ah)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998619879040
Schlagwörter	Quantitative Daten; Adaptive Testing; Computer Assisted Testing; Item Response Theory; Reaction Time; Item Banks; Statistical Inference; Sampling + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Reaktionsvermögen; Inferential statistics; Schließende Statistik
Abstract	With the widespread use of computers in modern assessment, online calibration has become increasingly popular as a way of replenishing an item pool. The present study discusses online calibration strategies for a joint model of responses and response times. The study proposes likelihood inference methods for item paramter estimation and evaluates their performance along with optimal sampling procedures. An extensive simulation study indicates that the proposed online calibration strategies perform well with relatively small samples (e.g., 500~800 examinees). The analysis of estimated parameters suggests that response time information can be used to improve the recovery of the response model parameters. Among a number of sampling methods investigated, "A"-optimal sampling was found most advantageous when the item parameters were weakly correlated. When the parameters were strongly correlated, "D"-optimal sampling tended to achieve the most accurate parameter recovery. The study provides guidelines for deciding sampling design under a specific goal of online calibration given the characteristics of field-testing items. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01