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Q-SID: Detecting collusion groups from exam question scores with error quantification
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DOI：10.1016/j.patter.2026.101642.png)
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Abstract
En 中文
<h2>Summary</h2><p>Collusion in online exams undermines the integrity of exam results. Existing detection methods identify suspicious student pairs and are often limited to multiple-choice formats. We introduce Question-Score Identity Detection (Q-SID), a statistical algorithm that efficiently detects likely collusion groups and quantifies uncertainty using only graded numeric question scores. This enables Q-SID to support a broad range of multiple-choice and non-multiple-choice exams. Q-SID reports two false-positive rates (FPRs) for each collusion group: (1) an <i>empirical FPR</i>, whose null data are from strictly proctored exam datasets, and (2) a <i>synthetic FPR</i>, whose null data are simulated from a copula-based model. Across 34 unproctored exam datasets, including two benchmark datasets with verified positives and negatives from textual analysis, Q-SID performs robustly across exam formats, class sizes, numbers of questions, and levels of complexity. It is more accurate and substantially faster than widely used pairwise statistics and group-level baselines, including answer similarity at group level (ASIg) and clique detection.</p>
Keywords:
cheating detection
exam integrity
collusion
question scores
copula model
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