Incorporating Learning into the Cognitive Assessment Framework

Autor/inn/en	Studer, Cassandra; Junker, Brian; Chan, Helen
Institution	Society for Research on Educational Effectiveness (SREE)
Titel	Incorporating Learning into the Cognitive Assessment Framework
Quelle	(2012), (7 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Foreign Countries; Data Analysis; Cognitive Measurement; Measurement Techniques; Educational Assessment; Student Evaluation; Models; Federal Aid; Educational Research; Evaluation Utilization; Simulation; Second Language Learning; English (Second Language); Pretests Posttests; Evaluation Methods; College Students; Web Based Instruction; Cloze Procedure; Language Tests; China + Suchen Sie Ihr Suchwort? Ausland; Auswertung; Messtechnik; Education; assessment; Bewertungssystem; Schulnote; Studentische Bewertung; Analogiemodell; Bildungsforschung; Pädagogische Forschung; Simulation program; Simulationsprogramm; Zweitsprachenerwerb; English as second language; English; Second Language; Englisch als Zweitsprache; Collegestudent; Web Based Training; Lückentext; Language test; Sprachtest
Abstract	The authors aimed to incorporate learning into the cognitive assessment framework that exists for static assessment data. In order to accomplish this, they derive a common likelihood function for dynamic models and introduce Parameter Driven Process for Change + Cognitive Diagnosis Model (PDPC + CDM), a dynamic model which tracks learning indirectly through student membership in latent states which drive the distributions of the student parameter in the static portion of the model. They described this model both theoretically and empirically through application to the article data set (Chan, 2012). One limitation of this data set is that the items are single skill. In order to truly test PDPC + CDM, the authors need to find data that have items with multiple skills. In general, by adding a dynamic component to the cognitive assessment framework, they provide education researchers with a method to track individual student learning while taking item and skill features into consideration. In addition, one could use a model such as this to define learning trajectories which could lead to better instructional methods and sequences (Haertel, 2012). Teachers could also use this information to better focus their lessons. One goal for the future would be to make these models accessible to researchers and teachers who can use the results to further student learning and the field of education research. (Contains 2 figures.) [This work was supported in part by the Program for Interdisciplinary Education Research, Carnegie Mellon University, under Institute for Education Sciences, Department of Education.] (ERIC).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10