Bayesian Change-Point Analysis Approach to Detecting Aberrant Test-Taking Behavior Using Response Times

Autor/inn/en	Zhu, Hongyue; Jiao, Hong; Gao, Wei; Meng, Xiangbin
Titel	Bayesian Change-Point Analysis Approach to Detecting Aberrant Test-Taking Behavior Using Response Times
Quelle	In: Journal of Educational and Behavioral Statistics, 48 (2023) 4, S.490-520 (31 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Zhu, Hongyue)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986231151961
Schlagwörter	Bayesian Statistics; Test Wiseness; Behavior Problems; Reaction Time; Responses; Student Behavior; Change; Computer Assisted Testing; Simulation; High Stakes Tests + Suchen Sie Ihr Suchwort? Reaktionsvermögen; Student behaviour; Schülerverhalten; Wandel; Simulation program; Simulationsprogramm
Abstract	Change-point analysis (CPA) is a method for detecting abrupt changes in parameter(s) underlying a sequence of random variables. It has been applied to detect examinees' aberrant test-taking behavior by identifying abrupt test performance change. Previous studies utilized maximum likelihood estimations of ability parameters, focusing on detecting one change point for each examinee. This article proposes a Bayesian CPA procedure using response times (RTs) to detect abrupt changes in examinee speed, which may be related to aberrant responding behaviors. The lognormal RT model is used to derive a procedure for detecting aberrant RT patterns. The method takes the numbers and locations of the change points as parameters in the model to detect multiple change points or multiple aberrant behaviors. Given the change points, the corresponding speed of each segment in the test can be estimated, which enables more accurate inferences about aberrant behaviors. Simulation study results indicate that the proposed procedure can effectively detect simulated aberrant behaviors and estimate change points accurately. The method is applied to data from a high-stakes computerized adaptive test, where its applicability is demonstrated. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01