Journal article
Using Biclustering to Detect Cheating in Real Time on Mixed-Format Tests
Educational and psychological measurement, Vol.85(5), pp.956-982
10/2025
DOI: 10.1177/00131644251333143
PMCID: PMC12104213
PMID: 40433225
Abstract
We evaluated a real-time biclustering method for detecting cheating on mixed-format assessments that included dichotomous, polytomous, and multi-part items. Biclustering jointly groups examinees and items by identifying subgroups of test takers who exhibit similar response patterns on specific subsets of items. This method’s flexibility and minimal assumptions about examinee behavior make it computationally efficient and highly adaptable. To further finetune accuracy and reduce false positives in real-time detection, enhanced statistical significance tests were incorporated into the illustrated algorithms. Two simulation studies were conducted to assess detection across varying testing conditions. In the first study, the method effectively detected cheating on tests composed entirely of either dichotomous or non-dichotomous items. In the second study, we examined tests with varying mixed item formats and again observed strong detection performance. In both studies, detection performance was examined at each timestamp in real time and evaluated under three varying conditions: proportion of cheaters, cheating group size, and proportion of compromised items. Across conditions, the method demonstrated strong computational efficiency, underscoring its suitability for real-time applications. Overall, these results highlight the adaptability, versatility, and effectiveness of biclustering in detecting cheating in real time while maintaining low false-positive rates.
Details
- Title: Subtitle
- Using Biclustering to Detect Cheating in Real Time on Mixed-Format Tests
- Creators
- Hyeryung Lee - University of IowaWalter P. Vispoel - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Educational and psychological measurement, Vol.85(5), pp.956-982
- DOI
- 10.1177/00131644251333143
- PMID
- 40433225
- PMCID
- PMC12104213
- NLM abbreviation
- Educ Psychol Meas
- ISSN
- 0013-1644
- eISSN
- 1552-3888
- Publisher
- Sage; THOUSAND OAKS
- Language
- English
- Electronic publication date
- 05/24/2025
- Date published
- 10/2025
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984824183702771
Metrics
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