Tracking Skill Acquisition with Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model with Covariates

Autor/inn/en	Wang, Shiyu; Yang, Yan; Culpepper, Steven Andrew; Douglas, Jeffrey A.
Titel	Tracking Skill Acquisition with Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model with Covariates
Quelle	In: Journal of Educational and Behavioral Statistics, 43 (2018) 1, S.57-87 (31 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998617719727
Schlagwörter	Skill Development; Cognitive Measurement; Cognitive Processes; Markov Processes; Models; Bayesian Statistics; Computer Assisted Testing; Longitudinal Studies; Monte Carlo Methods; Spatial Ability; Equations (Mathematics); Data Analysis; Mastery Learning + Suchen Sie Ihr Suchwort? Kompetenzentwicklung; Qualifikationsentwicklung; Cognitive process; Kognitiver Prozess; Markowscher Prozess; Analogiemodell; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Monte-Carlo-Methode; Räumliches Vorstellungsvermögen; Equations; Mathematics; Gleichungslehre; Auswertung
Abstract	A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are applied to a computer-based assessment with a learning intervention. The results show the potential application of the proposed model to track the change of students' skills directly and provide immediate remediation as well as to evaluate the efficacy of different interventions by investigating how different types of learning interventions impact the transitions from nonmastery to mastery. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01