Few and Different: Detecting Examinees With Preknowledge Using Extended Isolation Forests

Abstract

Item preknowledge refers to the case where examinees have advanced knowledge of test material prior to taking the examination. When examinees have item preknowledge, the scores that result from those item responses are not true reflections of the examinee's proficiency. Further, this contamination in the data also has an impact on the item parameter estimates and therefore has an impact on scores for all examinees, regardless of whether they had prior knowledge. To ensure the validity of test scores, it is essential to identify both issues: compromised items (CIs) and examinees with preknowledge (EWPs). In some cases, the CIs are known, and the task is reduced to determining the EWPs. However, due to the potential threat to validity, it is critical for high-stakes testing programs to have a process for routinely monitoring for evidence of EWPs, often when CIs are unknown. Further, even knowing that specific items may have been compromised does not guarantee that any examinees had prior access to those items, or that those examinees that did have prior access know how to effectively use the preknowledge. Therefore, this paper attempts to use response behavior to identify item preknowledge without knowledge of which items may or may not have been compromised. While most research in this area has relied on traditional psychometric models, we investigate the utility of an unsupervised machine learning algorithm, extended isolation forest (EIF), to detect EWPs. Similar to previous research, the response behavior being analyzed is response time (RT) and response accuracy (RA).