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Sequential Detection of Compromised Items Using Response Times in Computerized Adaptive Testing

Published online by Cambridge University Press:  01 January 2025

Edison M. Choe ,
Jinming Zhang  and
Hua-Hua Chang
Edison M. Choe*
Affiliation:
Graduate Management Admission Council® (GMAC®)
Jinming Zhang
Affiliation:
University of Illinois at Urbana-Champaign
Hua-Hua Chang
Affiliation:
University of Illinois at Urbana-Champaign
*
Correspondence should be made to Edison M. Choe, Graduate Management Admission Council® (GMAC®), 11921 Freedom Drive, Suite 300, Reston, VA 20190, USA. Email: echoe@gmac.com
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Abstract

Item compromise persists in undermining the integrity of testing, even secure administrations of computerized adaptive testing (CAT) with sophisticated item exposure controls. In ongoing efforts to tackle this perennial security issue in CAT, a couple of recent studies investigated sequential procedures for detecting compromised items, in which a significant increase in the proportion of correct responses for each item in the pool is monitored in real time using moving averages. In addition to actual responses, response times are valuable information with tremendous potential to reveal items that may have been leaked. Specifically, examinees that have preknowledge of an item would likely respond more quickly to it than those who do not. Therefore, the current study proposes several augmented methods for the detection of compromised items, all involving simultaneous monitoring of changes in both the proportion correct and average response time for every item using various moving average strategies. Simulation results with an operational item pool indicate that, compared to the analysis of responses alone, utilizing response times can afford marked improvements in detection power with fewer false positives.

Keywords

test securityresponse timecomputerized adaptive testingsequential analysischange-point detectionrepeated significance tests

Information

Type
Original Paper
Information
Psychometrika , Volume 83 , Issue 3 , September 2018 , pp. 650 - 673
Copyright
Copyright © The Psychometric Society 2017

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