Estimating a Dose-Response Relationship in Quasi-Experimental Student Success Studies

Autor/inn/en	Shao, Lucy; Levine, Richard A.; Guarcello, Maureen A.; Wilke, Morten C.; Stronach, Jeanne; Frazee, James P.; Fan, Juanjuan
Titel	Estimating a Dose-Response Relationship in Quasi-Experimental Student Success Studies
Quelle	In: International Journal of Artificial Intelligence in Education, 33 (2023) 1, S.155-184 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Levine, Richard A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-021-00280-0
Schlagwörter	Statistical Bias; Probability; Scores; Weighted Scores; Evaluation Methods; Observation; Program Evaluation; Supplementary Education; Introductory Courses; Statistics Education; Academic Achievement; Learner Engagement + Suchen Sie Ihr Suchwort? Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Beobachtung; Programme evaluation; Programmevaluation; Ergänzungsunterricht; Einführungskurs; Schulleistung
Abstract	Propensity score matching and weighting methods are applied to balance covariates and reduce selection bias in the analysis of observational study data, and ultimately estimate a treatment effect. We wish to evaluate the impact of a Supplemental Instruction (SI) program on student success in an Introductory Statistics course. In such student success studies, propensity score methods have been applied successfully for evaluating a binary treatment, namely whether a student attending SI performs better or worse than a student who does not attend SI. However, in this setting, we also want to draw inferences on the dose-response relationship, namely how does the number of times a student attends SI impact performance in the course. In this paper, we introduce generalized propensity scores (GPS) for analyzing such continuous treatment. We extend recent developments in GPS analyses from the personalized learning literature for evaluating SI engagement on student success. As part of the exposition, we provide a brief review of generalized propensity scores, compare our proposed GPS approaches, present guidelines on how these methods can be applied to educational data, and present R code and illustration for practitioners to use as a template in educational data mining applications. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01