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A Changepoint Rule for Determining Bicluster Retention Cutoffs in Cheating Detection.

Hyeryung Lee1

  • 1Oklahoma State University, Stillwater, USA.

Educational and Psychological Measurement
|July 9, 2026
PubMed
Summary
This summary is machine-generated.

A new changepoint-based rule helps determine how many biclusters to retain for test cheating detection. This method uses bicluster p-values to flag suspicious response patterns, improving accuracy in identifying cheating.

Keywords:
biclusteringcheating detectionmachine learningtest security

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Area of Science:

  • Educational Measurement
  • Data Mining
  • Statistical Analysis

Background:

  • Biclustering identifies groups of examinees with unusual response patterns for test cheating detection.
  • A key challenge is determining the optimal number of biclusters to retain and examinees to flag.
  • Retaining too many biclusters risks false positives; too few may miss genuine cheating.

Purpose of the Study:

  • To propose and evaluate a changepoint-based retention rule for biclusters in test cheating detection.
  • To provide a data-driven method for setting bicluster retention cutoffs.
  • To compare the proposed rule against established benchmarks like the F1-score.

Main Methods:

  • A changepoint detection algorithm was applied to the ordered sequence of bicluster p-values.
  • The method was validated using two operational test forms with known cheating labels.
  • A simulation study varied cheating type, test length, and item compromise proportion.

Main Results:

  • The changepoint-based rule closely matched F1-score benchmarks in empirical analyses, showing similar sensitivity and slightly lower specificity.
  • In simulations, the rule approximated F1 benchmarks but favored more conservative cutoffs.
  • The method demonstrated potential for flagging relevant biclusters for review in cheating detection.

Conclusions:

  • The proposed changepoint-based retention rule offers a data-driven approach to bicluster selection in test cheating detection.
  • This method can aid in optimizing the balance between identifying cheating and minimizing false positives.
  • The rule provides a practical tool for operational settings requiring efficient review of potential cheating patterns.