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Confidence Screening Detector: A New Method for Detecting Test Collusion.

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A new algorithm detects test collusion (TC), a form of group cheating in exams. This method, inspired by statistical analysis, effectively identifies cheating using only response data.

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Statistical Analysis

Background:

  • Test collusion (TC) is a growing problem in high-stakes exams.
  • Existing research on TC detection methods is limited.
  • TC involves examinees collaborating to manipulate item responses.

Purpose of the Study:

  • To introduce a novel algorithm for detecting test collusion.
  • To evaluate the algorithm's performance against existing methods.
  • To validate the algorithm in real-world, large-scale testing scenarios.

Main Methods:

  • Algorithm inspired by variable selection in high-dimensional statistics.
  • Utilizes only examinee item response data.
  • Supports various response similarity indices for detection.

Main Results:

  • The proposed algorithm demonstrates competitive performance compared to the clique detector approach.
  • The algorithm's efficacy was confirmed in a large-scale test setting.
  • The method proved effective in identifying group cheating patterns.

Conclusions:

  • The new algorithm offers a promising tool for detecting test collusion.
  • Its reliance on response data makes it adaptable to various testing formats.
  • Further research can explore its application in different educational and professional testing environments.