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The detection of examinees who have previously accessed proprietary test content is a primary concern in the context of test security. Researchers have proposed using item response times to detect examinee pre-knowledge, but progress in this area has been limited by a lack of real data containing credible information about pre-knowledge and by strict statistical assumptions. In this work, an innovative, but simple, method is proposed for detecting examinees with pre-knowledge. The proposed method represents a conditional scaling that assesses an examinee’s response time to a particular item, compared to a group of examinees who did not have pre-knowledge, conditioned on whether or not the item was answered correctly. The proposed method was investigated in empirical data from 93 undergraduate students, who were randomly assigned to have pre-knowledge or not. Participants took a computerized GRE Quantitative Reasoning test and were given no items, half the items, or half the items with correct answers to study before the test, depending on their condition. Exploratory analysis techniques were used to investigate the resulting values at both the item and person-level, including factor analyses and cluster analyses. The proposed method achieved impressive accuracy of separation between disclosed and undisclosed items and examinees with and without pre-knowledge (96% and 97% accuracy for cluster analyses, respectively), demonstrating detection power for item disclosure and examinee pre-knowledge. The methodology requires minimal assumptions about the data and can be used for a variety of modern test designs that preclude other types of data forensic analyses.
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Stored only when both papers are in the corpus — 27 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.