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A Shadow-Test Approach to Adaptive Item Calibration.

Wim J van der Linden1, Bingnan Jiang2

  • 1University of Twente, Enschede, The Netherlands. wjvdlinden@outlook.com.

Psychometrika
|June 20, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a shadow-test approach for calibrating field-test items in adaptive testing. This method efficiently calibrates items using smaller sample sizes and comparable runtimes to traditional adaptive testing.

Keywords:
Bayesian -optimalityGibbs samplingMCMC algorithmadaptive testingitem calibrationitem response modelsshadow-test approach

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Area of Science:

  • Psychometrics
  • Computerized Adaptive Testing
  • Statistical Modeling

Background:

  • Traditional adaptive testing methods face challenges in calibrating new field-test items efficiently.
  • Accurate calibration of field-test items is crucial for maintaining the integrity and precision of adaptive testing systems.

Purpose of the Study:

  • To present a novel shadow-test approach for the calibration of field-test items within adaptive testing frameworks.
  • To develop an objective function that adaptively selects both operational and field-test items using a Bayesian criterion.
  • To incorporate practical constraints for hiding field-test items and controlling their exposure rates.

Main Methods:

  • Utilized a shadow-test model with a Bayesian criterion for adaptive item selection (e.g., -optimality).
  • Implemented efficient Gibbs sampling for real-time updating of ability and field-test parameters.
  • Optimized algorithm settings to demonstrate the approach's efficacy.

Main Results:

  • The shadow-test approach enables adaptive selection of both operational and field-test items.
  • Field-test items can be effectively hidden within the test content and their exposure rates controlled.
  • Item calibration was demonstrated with smaller sample sizes than traditional methods.
  • The approach achieved runtimes comparable to conventional adaptive testing.

Conclusions:

  • The proposed shadow-test approach offers an efficient and practical solution for calibrating field-test items in adaptive testing.
  • This method allows for the seamless integration and calibration of new items without compromising test security or efficiency.
  • The findings suggest a viable alternative for updating item pools in adaptive testing systems with reduced data requirements.