Mining Process Data to Detect Aberrant Test Takers

Autor/inn/en	Liao, Manqian; Patton, Jeffrey; Yan, Ray; Jiao, Hong
Titel	Mining Process Data to Detect Aberrant Test Takers
Quelle	In: Measurement: Interdisciplinary Research and Perspectives, 19 (2021) 2, S.93-105 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Liao, Manqian)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1536-6367
DOI	10.1080/15366367.2020.1827203
Schlagwörter	Data Analysis; Cheating; Identification; Behavior Patterns; Multivariate Analysis + Suchen Sie Ihr Suchwort? Auswertung; Prellen; Identifikation; Identifizierung; Multivariate Analyse
Abstract	Item harvesters who memorize, record and share test items can jeopardize the validity and fairness of credentialing tests. Item harvesting behaviors are difficult to detect by the existing statistical modeling approaches due to the absence of operational definitions and the idiosyncratic nature of human behaviors. Motivated to detect the hard-to-define aberrant test-taking behaviors like item harvesting, we proposed a data-mining approach that utilized the process data and identified the examinees whose test-taking processes deviate from the majority of examinees. Specifically, two steps were implemented in the proposed approach: First, archetypes of test-taking processes are learned with the "k"-means clustering algorithm; second, examinees whose behavioral patterns deviate from the archetypes are flagged for further investigation. Given that the process data makes it possible to capture more subtle differences between the aberrant test-takers and normal examinees, the proposed approach is expected to be complementary of the statistical modeling methods, capture additional types of aberrant test-takers and increase the probability of discovering the elusive item harvesters. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01