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Two New Models for Item Preknowledge.

Kylie Gorney1, James A Wollack1

  • 1University of Wisconsin-Madison, Madison, WI, USA.

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|August 22, 2022
PubMed
Summary
This summary is machine-generated.

Two new models for detecting item preknowledge were developed, allowing for person- and item-specific impacts. These models better represent real-world testing scenarios and fit data better than existing methods.

Keywords:
aberrant behaviorcheatingitem compromiseitem preknowledgetest security

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Researchers often use simulation studies with data-generating models to evaluate preknowledge detection methods.
  • Existing models may not fully capture the complexity of preknowledge in practical testing situations.

Purpose of the Study:

  • To propose and evaluate two novel models for representing item preknowledge.
  • To allow for person- and item-specific variations in the impact of preknowledge, enhancing representational accuracy.

Main Methods:

  • Development of two new statistical models for item preknowledge.
  • Evaluation of model fit using three real-world data sets.
  • Comparison of new models against existing preknowledge models.

Main Results:

  • The proposed models demonstrated superior fit compared to several existing preknowledge models.
  • Model parameter estimates varied significantly based on the type of preknowledge considered (items only vs. items and answer key).

Conclusions:

  • The new models offer a more realistic representation of preknowledge in testing.
  • Answer key disclosure significantly influences testing behavior, as indicated by substantial variations in model parameters.