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Grochowalski, Joseph H.; Hendrickson, Amy – Journal of Educational Measurement, 2023
Test takers wishing to gain an unfair advantage often share answers with other test takers, either sharing all answers (a full key) or some (a partial key). Detecting key sharing during a tight testing window requires an efficient, easily interpretable, and rich form of analysis that is descriptive and inferential. We introduce a detection method…
Descriptors: Identification, Cooperative Learning, Cheating, Statistical Analysis
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Becker, Kirk; Meng, Huijuan – Journal of Applied Testing Technology, 2022
The rise of online proctoring potentially provides more opportunities for item harvesting and consequent brain dumping and shared "study guides" based on stolen content. This has increased the need for rapid approaches for evaluating and acting on suspicious test responses in every delivery modality. Both hiring proxy test takers and…
Descriptors: Identification, Cheating, Computer Assisted Testing, Observation
Peng, Luyao; Sinharay, Sandip – Educational and Psychological Measurement, 2022
Wollack et al. (2015) suggested the erasure detection index (EDI) for detecting fraudulent erasures for individual examinees. Wollack and Eckerly (2017) and Sinharay (2018) extended the index of Wollack et al. (2015) to suggest three EDIs for detecting fraudulent erasures at the aggregate or group level. This article follows up on the research of…
Descriptors: Cheating, Identification, Statistical Analysis, Testing
Sinharay, Sandip – Grantee Submission, 2021
Drasgow, Levine, and Zickar (1996) suggested a statistic based on the Neyman-Pearson lemma (e.g., Lehmann & Romano, 2005, p. 60) for detecting preknowledge on a known set of items. The statistic is a special case of the optimal appropriateness indices of Levine and Drasgow (1988) and is the most powerful statistic for detecting item…
Descriptors: Robustness (Statistics), Hypothesis Testing, Statistics, Test Items
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Wang, Xi; Liu, Yang; Robin, Frederic; Guo, Hongwen – International Journal of Testing, 2019
In an on-demand testing program, some items are repeatedly used across test administrations. This poses a risk to test security. In this study, we considered a scenario wherein a test was divided into two subsets: one consisting of secure items and the other consisting of possibly compromised items. In a simulation study of multistage adaptive…
Descriptors: Identification, Methods, Test Items, Cheating
Sinharay, Sandip; Johnson, Matthew S. – Grantee Submission, 2019
According to Wollack and Schoenig (2018), score differencing is one of six types of statistical methods used to detect test fraud. In this paper, we suggested the use of Bayes factors (e.g., Kass & Raftery, 1995) for score differencing. A simulation study shows that the suggested approach performs slightly better than an existing frequentist…
Descriptors: Cheating, Deception, Statistical Analysis, Bayesian Statistics
Sinharay, Sandip – Grantee Submission, 2019
Benefiting from item preknowledge (e.g., McLeod, Lewis, & Thissen, 2003) is a major type of fraudulent behavior during educational assessments. This paper suggests a new statistic that can be used for detecting the examinees who may have benefitted from item preknowledge using their response times. The statistic quantifies the difference in…
Descriptors: Test Items, Cheating, Reaction Time, Identification
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Daffin, Lee William, Jr.; Jones, Ashley A. – Online Learning, 2018
As online education becomes a more popular and permanent option for obtaining an education after high school, it also raises questions as to the academic rigor of such classes and the academic integrity of the students taking the classes. The purpose of the current study is to explore the integrity issue and to investigate student performance on…
Descriptors: College Students, Online Courses, Psychology, Computer Assisted Testing
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Haberman, Shelby J.; Lee, Yi-Hsuan – ETS Research Report Series, 2017
In investigations of unusual testing behavior, a common question is whether a specific pattern of responses occurs unusually often within a group of examinees. In many current tests, modern communication techniques can permit quite large numbers of examinees to share keys, or common response patterns, to the entire test. To address this issue,…
Descriptors: Student Evaluation, Testing, Item Response Theory, Maximum Likelihood Statistics
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Sunbul, Onder; Yormaz, Seha – Eurasian Journal of Educational Research, 2018
Purpose: Several studies can be found in the literature that investigate the performance of ? under various conditions. However no study for the effects of item difficulty, item discrimination, and ability restrictions on the performance of ? could be found. The current study aims to investigate the performance of ? for the conditions given below.…
Descriptors: Test Items, Difficulty Level, Ability, Cheating
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Chuang, Chia Yuan; Craig, Scotty D.; Femiani, John – Higher Education Research and Development, 2017
This study investigated the ability of test takers' behaviors during online assessments to detect probable cheating incidents. Specifically, this study focused on the role of time delay and head pose for detection of cheating incidences in a lab-based online testing session. The analysis of a test taker's behavior indicated that not only time…
Descriptors: Cheating, Ethics, Computer Assisted Testing, Time
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Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2017
An increasing concern of producers of educational assessments is fraudulent behavior during the assessment (van der Linden, 2009). Benefiting from item preknowledge (e.g., Eckerly, 2017; McLeod, Lewis, & Thissen, 2003) is one type of fraudulent behavior. This article suggests two new test statistics for detecting individuals who may have…
Descriptors: Test Items, Cheating, Testing Problems, Identification
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Belov, Dmitry I. – Journal of Educational Measurement, 2013
The development of statistical methods for detecting test collusion is a new research direction in the area of test security. Test collusion may be described as large-scale sharing of test materials, including answers to test items. Current methods of detecting test collusion are based on statistics also used in answer-copying detection.…
Descriptors: Cheating, Computer Assisted Testing, Adaptive Testing, Statistical Analysis
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Roberts, Foster; Thomas, Christopher H.; Novicevic, Milorad M.; Ammeter, Anthony; Garner, Bart; Johnson, Paul; Popoola, Ifeoluwa – Journal of Management Education, 2018
In this article, we develop an "integrated moral conviction theory of student cheating" by integrating moral conviction with (a) the dual-process model of Hunt-Vitell's theory that gives primacy to individual ethical philosophies when moral judgments are made and (b) the social cognitive conceptualization that gives primacy to moral…
Descriptors: Undergraduate Students, Business Administration Education, Cheating, Learner Engagement
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Blincoe, Sarai; Garris, Christopher P. – Journal of Experimental Education, 2017
Academic entitlement (AE) is increasingly associated with problematic behaviors and attitudes, including student incivility and endorsement of cheating. As research on this context-specific form of entitlement increases, no one has yet explored the rates of occurrence outside of North America. To investigate whether students at North American…
Descriptors: Foreign Countries, Student Attitudes, Expectation, Behavior Problems
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