Simultaneous Detection of Compromised Items and Examinees with Item Preknowledge in Online Assessments Using Response Time Data

Abstract The rapid transition from traditional paper‐and‐pencil tests to computer‐based testing systems has significantly altered the educational landscape, particularly during the COVID‐19 pandemic. While online assessments offer numerous advantages, they also present unique challenges, with test security being paramount. This article addresses the critical issue of test fraud in digital assessments, specifically focusing on item preknowledge, where examinees have prior access to test items. Using response‐time data, we propose a statistical framework for simultaneously identifying compromised items and examinees with item preknowledge in a single‐step analysis. Unlike existing methods, our model does not require prior knowledge about the compromised status of items. Using a large‐scale online certification exam dataset, we demonstrate the model's application in detecting significant signals in response times, identifying potentially compromised items, and examinees with potential item preknowledge.