Assessing Ability Recovery of the Sequential IRT Model with Unstructured Multiple-Attempt Data

Autor/inn/en	Li, Ziying; Huggins-Manley, A. Corinne; Leite, Walter L.; Miller, M. David; Wright, Eric A.
Titel	Assessing Ability Recovery of the Sequential IRT Model with Unstructured Multiple-Attempt Data
Quelle	In: Educational and Psychological Measurement, 82 (2022) 6, S.1203-1224 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Li, Ziying) ORCID (Leite, Walter L.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644211058386
Schlagwörter	Sequential Approach; Item Response Theory; Data; Simulation; Research Problems; Electronic Learning; Sample Size; Test Length; Statistical Bias; Ability; Models; Data Analysis; Evaluation Criteria; Error of Measurement + Suchen Sie Ihr Suchwort? Schrittfolge; Item-Response-Theorie; Daten; Simulation program; Simulationsprogramm; Forschungskritik; Fähigkeit; Fertigkeit; Analogiemodell; Auswertung; Messfehler
Abstract	The unstructured multiple-attempt (MA) item response data in virtual learning environments (VLEs) are often from student-selected assessment data sets, which include missing data, single-attempt responses, multiple-attempt responses, and unknown growth ability across attempts, leading to a complex and complicated scenario for using this kind of data set as a whole in the practice of educational measurement. It is critical that methods be available for measuring ability from VLE data to improve VLE systems, monitor student progress in instructional settings, and conduct educational research. The purpose of this study is to explore the ability recovery of the multidimensional sequential 2-PL IRT model in unstructured MA data from VLEs. We conduct a simulation study to evaluate the effects of the magnitude of ability growth and the proportion of students who make two attempts, as well as the moderated effects of sample size, test length, and missingness, on the bias and root mean square error of ability estimates. Results show that the model poses promise for evaluating ability in unstructured VLE data, but that some data conditions can result in biased ability estimates. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01