Dynamic Bayesian Networks in Educational Measurement: Reviewing and Advancing the State of the Field

Autor/in	Reichenberg, Ray
Titel	Dynamic Bayesian Networks in Educational Measurement: Reviewing and Advancing the State of the Field
Quelle	In: Applied Measurement in Education, 31 (2018) 4, S.335-350 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2018.1495217
Schlagwörter	Bayesian Statistics; Measurement; Student Evaluation; Psychometrics; Evaluation Research; Research Needs + Suchen Sie Ihr Suchwort? Messverfahren; Schulnote; Studentische Bewertung; Psychometry; Psychometrie; Evaluationsforschung; Forschungsbedarf
Abstract	As the popularity of rich assessment scenarios increases so must the availability of psychometric models capable of handling the resulting data. Dynamic Bayesian networks (DBNs) offer a fast, flexible option for characterizing student ability across time under psychometrically complex conditions. In this article, a brief introduction to DBNs is offered, followed by a review of the existing literature on the use of DBNs in educational and psychological measurement with a focus on methodological investigations and novel applications that may provide guidance for practitioners wishing to deploy these models. The article concludes with a discussion of future directions for research in the field. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01