Model-Based Anomaly Detection for Repeat Test-Takers

Flags repeat test-takers whose second-attempt performance departs from what a growth model predicts, using independent evidence sources: model-expected score gain (accounting for regression to the mean, time between attempts and remediation), differential performance on exposed versus new items (Sinharay, 2017, ), and differential response speed under a lognormal response-time model (van der Linden, 2006, ). Evidence is combined into a risk index calibrated by parametric bootstrap under the no-misconduct model, so flagging thresholds carry explicit false-positive rates.