Simultaneous Detection of Cheaters and Compromised Items Using a Biclustering Approach

Traditional methods for detecting cheating in assessments tend to focus on either identifying cheaters or compromised items in isolation, overlooking their interconnection. In this study, we present a novel biclustering approach that simultaneously detects both cheaters and compromised items by identifying coherent subgroups of examinees and items exhibiting suspicious response patterns. To identify these patterns, our method leverages response accuracy, response time, and distractor choice data. We evaluated the approach on real datasets and compared its performance with existing detection approaches. Additionally, a comprehensive simulation study was conducted, modeling a variety of realistic cheating scenarios such as answer copying, pre-knowledge of test items, and distinct forms of rapid guessing. Our findings revealed that the biclustering method outperformed previous methods in simultaneously distinguishing cheating and non-cheating behaviors within the empirical study. The simulation analyses further revealed the conditions under which the biclustering approach was most effective in both regards. Overall, the findings underscore the flexibility of biclustering and its adaptability in enhancing test security within diverse testing environments.