In Search of Optimal Cognitive Diagnostic Model(s) for ESL Grammar Test Data

Autor/in	Yi, Yeon-Sook
Titel	In Search of Optimal Cognitive Diagnostic Model(s) for ESL Grammar Test Data
Quelle	In: Applied Measurement in Education, 30 (2017) 2, S.82-101 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2017.1283314
Schlagwörter	English (Second Language); Grammar; Language Tests; Cognitive Measurement; Models; Comparative Analysis; Goodness of Fit + Suchen Sie Ihr Suchwort? English as second language; English; Second Language; Englisch als Zweitsprache; Grammatik; Language test; Sprachtest; Analogiemodell
Abstract	This study compares five cognitive diagnostic models in search of optimal one(s) for English as a Second Language grammar test data. Using a unified modeling framework that can represent specific models with proper constraints, the article first fit the full model (the log-linear cognitive diagnostic model, LCDM) and investigated which model emerged as the dominant model. It then fit the dominant model and the other models to confirm that the model provides the best fit to the data. The model found to represent the most number of items in the test was the Compensatory Reparameterized Unified Model (C-RUM) and other models compared were the Deterministic-Input, Noisy-And (DINA), Deterministic Input, Noisy-Or-gate (DINO), and Noisy Input, Deterministic-Or-gate (NIDO). The absolute (item-association root mean square error values) and relative (information criteria) model fit indices also indicated that the LCDM and the C-RUM were the best fit to the data. More detailed analyses on the functioning of the C-RUM were conducted and the interpretation of the results was included in the discussion section. The article ends with some suggestions for future research based on the limitations of the study. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01