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Multidimensional Computerized Adaptive Testing Using Non-Compensatory Item Response Theory Models.

Chia-Ling Hsu1, Wen-Chung Wang1

  • 1The Education University of Hong Kong, Tai Po, Hong Kong.

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|August 28, 2019
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Summary
This summary is machine-generated.

This study developed multidimensional computerized adaptive testing (MCAT) algorithms using non-compensatory models. Fisher and mutual information methods showed superior performance for accurate measurement and test reliability.

Keywords:
computerized adaptive testingitem response theoryitem selection methodsnon-compensatory models

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Computerized Adaptive Testing

Background:

  • Current multidimensional computerized adaptive testing (MCAT) primarily uses compensatory multidimensional item response theory (MIRT) models.
  • Challenges exist in applying MCAT with non-compensatory MIRT models due to data complexities.

Purpose of the Study:

  • To develop and evaluate MCAT algorithms specifically designed for non-compensatory MIRT models.
  • To compare the performance of different item selection methods within this non-compensatory framework.

Main Methods:

  • Adapted and compared three item selection methods: Fisher information, mutual information, and Kullback-Leibler information.
  • Conducted simulations to assess algorithm performance under varying conditions.
  • Investigated the impact of termination criteria and latent trait correlations on measurement precision.

Main Results:

  • Fisher information and mutual information methods demonstrated comparable and superior performance over the Kullback-Leibler method.
  • Increased measurement precision and test reliability were observed with stricter termination criteria and higher latent trait correlations.
  • Test reliability remained consistent across dimensions irrespective of latent trait correlations or termination criteria.

Conclusions:

  • MCAT algorithms utilizing non-compensatory MIRT models can be effectively developed and implemented.
  • Fisher and mutual information methods are recommended for item selection in non-compensatory MCAT.
  • Findings provide insights into optimizing item bank construction for non-compensatory assessments.