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Caught in the Act: Predicting Cheating in Unproctored Knowledge Assessment.

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Detecting cheating in online ability tests is crucial. Test data and para-data, like reaction times, effectively predict cheating, unlike self-report measures.

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Area of Science:

  • Psychological Assessment
  • Educational Technology
  • Behavioral Science

Background:

  • Unproctored ability assessments face significant cheating threats, regardless of security measures or test format.
  • Existing methods for detecting cheating often rely on self-report measures, which may lack predictive power.

Purpose of the Study:

  • To evaluate the effectiveness of self-report indicators, test performance data, and para-data in predicting cheating behavior.
  • To compare the predictive validity of different data sources for identifying dishonest actions in online assessments.

Main Methods:

  • 315 participants completed a knowledge test in both unproctored online and proctored lab settings.
  • Multiple regression and latent change score models were employed to analyze cheating prediction.
  • Indicators assessed included Honesty-Humility, Overclaiming scales, performance on difficult items, reaction times, and browser tab switching.

Main Results:

  • Test data (performance on difficult items) and para-data (reaction times, browser activity) were significant predictors of cheating.
  • Traditional self-report measures (Honesty-Humility, Overclaiming) did not effectively predict cheating behavior.
  • Para-data and test data demonstrated superior predictive power compared to self-report indicators.

Conclusions:

  • Test data and para-data offer a more reliable approach to detecting cheating in unproctored online ability assessments.
  • Self-report measures are insufficient for accurately identifying cheating in these contexts.
  • Practical strategies for enhancing cheating detection in online assessments can be developed based on performance and behavioral data.