Compromised Item Detection for Computerized Adaptive Testing - Liu, Cheng; Han, Kyung T.; Li, Jun (Frontiers in Psychology, 2019)
Item leakage has been a serious issue in continuous, computer-based testing, especially computerized adaptive testing (CAT), as compromised items jeopardize the fairness and validity of the test. Strategies to detect and address the problem of compromised items have been proposed and investigated, but many solutions are computationally intensive and thus difficult to apply in real-time monitoring. Recently, researchers have proposed several sequential methods aimed at fast detection of compromised items, but applications of these methods have not considered various scenarios of item leakage. In this paper, we introduce a model with a leakage parameter to better characterize the item leaking process and develop a more generalized detection method on its basis. The new model achieves a high level of detection accuracy while maintaining the type-I error at the nominal level, for both fast and slow leakage scenarios. The proposed model also estimates the time point at which an item becomes compromised, thus providing additional useful information for testing practitioners.