Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data

Autor/inn/en	de la Torre, Jimmy; Douglas, Jeffrey A.
Titel	Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data
Quelle	In: Psychometrika, 73 (2008) 4, S.595-624 (30 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-008-9063-2
Schlagwörter	Markov Processes; Identification; Goodness of Fit; Subtraction; Item Response Theory; Psychometrics; Models; Mathematics; Problem Solving; Comparative Analysis + Suchen Sie Ihr Suchwort? Markowscher Prozess; Identifikation; Identifizierung; Subtraktion; Item-Response-Theorie; Psychometry; Psychometrie; Analogiemodell; Mathematik; Problemlösen
Abstract	This paper studies three models for cognitive diagnosis, each illustrated with an application to fraction subtraction data. The objective of each of these models is to classify examinees according to their mastery of skills assumed to be required for fraction subtraction. We consider the DINA model, the NIDA model, and a new model that extends the DINA model to allow for multiple strategies of problem solving. For each of these models the joint distribution of the indicators of skill mastery is modeled using a single continuous higher-order latent trait, to explain the dependence in the mastery of distinct skills. This approach stems from viewing the skills as the specific states of knowledge required for exam performance, and viewing these skills as arising from a broadly defined latent trait resembling the theta of item response models. We discuss several techniques for comparing models and assessing goodness of fit. We then implement these methods using the fraction subtraction data with the aim of selecting the best of the three models for this application. We employ Markov chain Monte Carlo algorithms to fit the models, and we present simulation results to examine the performance of these algorithms. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10