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Detecting Preknowledge Cheating via Innovative Measures: A Mixture Hierarchical Model for Jointly Modeling Item

Kaiwen Man1, Jeffrey R Harring2

  • 1University of Alabama, Tuscaloosa, USA.

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Summary
This summary is machine-generated.

This study introduces a new model to detect preknowledge cheating in tests by analyzing item responses, response times, and eye-tracking data (gaze fixations). The model accurately identifies aberrant test-takers with prior knowledge.

Keywords:
eye-trackinggaze-fixation countsitem response theoryjoint modelingresponse timestechnology enhanced assessment

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Cognitive Psychology

Background:

  • Prenewledge cheating compromises test validity.
  • Existing methods analyze item responses and response times.
  • Eye-tracking data, specifically gaze fixations, offers potential for enhanced detection of aberrant testing behavior.

Purpose of the Study:

  • To propose a novel mixture hierarchical model integrating multiple data sources for preknowledge cheating detection.
  • To identify aberrant test takers with varying levels of preknowledge.
  • To differentiate behavioral patterns between normal and aberrant examinees.

Main Methods:

  • Development of a mixture hierarchical model.
  • Integration of item responses, response times, and visual fixation counts from eye-tracking.
  • Application of a Bayesian approach with Markov Chain Monte Carlo (MCMC) for parameter estimation.

Main Results:

  • The proposed model effectively detects aberrant test takers.
  • The model accounts for differences in preknowledge levels.
  • Behavioral patterns of aberrant examinees are distinguished from those of normal examinees.

Conclusions:

  • The integrated model enhances the accuracy of detecting preknowledge cheating.
  • Eye-tracking data provides valuable insights into test-taking behavior.
  • This approach offers a robust method for ensuring test integrity.