Using Bayesian Networks to Characterize Student Performance across Multiple Assessments of Individual Standards

Autor/inn/en	Xu, Jiajun; Dadey, Nathan
Titel	Using Bayesian Networks to Characterize Student Performance across Multiple Assessments of Individual Standards
Quelle	In: Applied Measurement in Education, 35 (2022) 3, S.179-196 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Xu, Jiajun)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2022.2103134
Schlagwörter	Bayesian Statistics; Learning Analytics; Scores; Academic Achievement; Standards; Correlation; Holistic Approach; Mastery Learning; Mathematics Tests; Mathematics Education; Grade 4; Arithmetic; Elementary School Students; Learning Trajectories + Suchen Sie Ihr Suchwort? Schulleistung; Standard; Korrelation; Holistischer Ansatz; Mathematische Bildung; School year 04; 4. Schuljahr; Schuljahr 04; Addition; Arithmetik; Arithmetikunterricht; Rechnen
Abstract	This paper explores how student performance across the full set of multiple modular assessments of individual standards, which we refer to as mini-assessments, from a large scale, operational program of interim assessment can be summarized using Bayesian networks. We follow a completely data-driven approach in which no constraints are imposed to best reflect the empirical relationships between these assessments, and a learning trajectory approach in which constraints are imposed to mirror the stages of a mathematic learning trajectory to provide insight into student learning. Under both approaches, we aim to draw a holistic picture of performance across all of the mini-assessments that provides additional information for students, educators, and administrators. In particular, the graphical structure of the network and the conditional probabilities of mastery provide information above and beyond an overall score on a single mini-assessment. Uses and implications of our work are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01