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Using Deterministic, Gated Item Response Theory Model to Detect Test Cheating due to Item Compromise

Author

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  • Zhan Shu
  • Robert Henson
  • Richard Luecht

Abstract

The Deterministic, Gated Item Response Theory Model (DGM, Shu, Unpublished Dissertation. The University of North Carolina at Greensboro, 2010 ) is proposed to identify cheaters who obtain significant score gain on tests due to item exposure/compromise by conditioning on the item status (exposed or unexposed items). A “gated” function is introduced to decompose the observed examinees’ performance into two distributions (the true ability distribution determined by examinees’ true ability and the cheating distribution determined by examinees’ cheating ability). Test cheaters who have score gain due to item exposure are identified through the comparison of the two distributions. Hierarchical Markov Chain Monte Carlo is used as the model’s estimation framework. Finally, the model is applied in a real data set to illustrate how the model can be used to identify examinees having pre-knowledge on the exposed items. Copyright The Psychometric Society 2013

Suggested Citation

  • Zhan Shu & Robert Henson & Richard Luecht, 2013. "Using Deterministic, Gated Item Response Theory Model to Detect Test Cheating due to Item Compromise," Psychometrika, Springer;The Psychometric Society, vol. 78(3), pages 481-497, July.
    • Handle: RePEc:spr:psycho:v:78:y:2013:i:3:p:481-497
    DOI: 10.1007/s11336-012-9311-3
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    Citations

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    Cited by:

    1. Chen, Yunxiao & Lu, Yan & Moustaki, Irini, 2022. "Detection of two-way outliers in multivariate data and application to cheating detection in educational tests," LSE Research Online Documents on Economics 112499, London School of Economics and Political Science, LSE Library.
    2. Hyeon-Ah Kang, 2023. "Sequential Generalized Likelihood Ratio Tests for Online Item Monitoring," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 672-696, June.
    3. Xi Wang & Yang Liu, 2020. "Detecting Compromised Items Using Information From Secure Items," Journal of Educational and Behavioral Statistics, , vol. 45(6), pages 667-689, December.
    4. Sandip Sinharay, 2017. "Detection of Item Preknowledge Using Likelihood Ratio Test and Score Test," Journal of Educational and Behavioral Statistics, , vol. 42(1), pages 46-68, February.
    5. Chun Wang & Gongjun Xu & Zhuoran Shang, 2018. "A Two-Stage Approach to Differentiating Normal and Aberrant Behavior in Computer Based Testing," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 223-254, March.
    6. Stefan Zimmermann & Dietrich Klusmann & Wolfgang Hampe, 2016. "Are Exam Questions Known in Advance? Using Local Dependence to Detect Cheating," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-13, December.

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    Keywords

    cheating; model estimation;

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