Literaturnachweis - Detailanzeige
Autor/inn/en | Wang, Shiyu; Yang, Yan; Culpepper, Steven Andrew; Douglas, Jeffrey A. |
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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 |
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 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 |