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Pan, Yiqin; Wollack, James A. – Journal of Educational Measurement, 2021
As technologies have been improved, item preknowledge has become a common concern in the test security area. The present study proposes an unsupervised-learning-based approach to detect compromised items. The unsupervised-learning-based compromised item detection approach contains three steps: (1) classify responses of each examinee as either…
Descriptors: Test Items, Cheating, Artificial Intelligence, Identification
Liu, Jinghua; Becker, Kirk – Journal of Educational Measurement, 2022
For any testing programs that administer multiple forms across multiple years, maintaining score comparability via equating is essential. With continuous testing and high-stakes results, especially with less secure online administrations, testing programs must consider the potential for cheating on their exams. This study used empirical and…
Descriptors: Cheating, Item Response Theory, Scores, High Stakes Tests

Stocking, Martha L.; Ward, William C.; Potenza, Maria T. – Journal of Educational Measurement, 1998
Explored, using simulations, the use of disclosed items on continuous testing conditions under a worse-case scenario that assumes that disclosed items are always answered correctly. Some item pool and test designs were identified in which the use of disclosed items produces effects on test scores that may be viewed as negligible. (Author/MAK)
Descriptors: Adaptive Testing, Cheating, Computer Assisted Testing, Item Banks
van der Linden, Wim J.; Sotaridona, Leonardo – Journal of Educational Measurement, 2004
A statistical test for the detection of answer copying on multiple-choice tests is presented. The test is based on the idea that the answers of examinees to test items may be the result of three possible processes: (1) knowing, (2) guessing, and (3) copying, but that examinees who do not have access to the answers of other examinees can arrive at…
Descriptors: Multiple Choice Tests, Test Items, Hypothesis Testing, Statistical Distributions