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New Item Selection Methods for Cognitive Diagnosis Computerized Adaptive Testing.

Mehmet Kaplan1, Jimmy de la Torre1, Juan Ramón Barrada2

  • 1Rutgers, The State University of New Jersey, New Brunswick, USA.

Applied Psychological Measurement
|June 9, 2018
PubMed
Summary

Two new methods for computerized adaptive testing, the modified posterior-weighted Kullback-Leibler index (MPWKL) and generalized deterministic inputs, noisy "and" gate (G-DINA) model discrimination index (GDI), improve attribute classification accuracy and reduce test length compared to the existing posterior-weighted Kullback-Leibler (PWKL) index.

Keywords:
cognitive diagnosis modelcomputerized adaptive testingitem selection method

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Cognitive Science

Background:

  • Computerized adaptive testing (CAT) requires efficient item selection methods.
  • Cognitive diagnosis models (CDMs) aim to classify examinees on specific attributes.
  • Existing methods like PWKL may not be optimal for complex CDMs such as G-DINA.

Purpose of the Study:

  • Introduce and evaluate two novel item selection indices: MPWKL and GDI.
  • Compare their performance against the PWKL index within the G-DINA model framework.
  • Investigate the influence of factors like item quality and test termination rules.

Main Methods:

  • Simulation study comparing MPWKL, GDI, and PWKL item selection indices.
  • Utilized the generalized deterministic inputs, noisy

Main Results:

  • MPWKL and GDI demonstrated superior performance over PWKL.
  • Both new indices achieved higher attribute classification accuracy.
  • Shorter test lengths were observed with MPWKL and GDI compared to PWKL.
  • GDI exhibited the fastest implementation time.

Conclusions:

  • MPWKL and GDI are effective and efficient item selection methods for cognitive diagnosis CAT under the G-DINA model.
  • These methods offer advantages in terms of accuracy and test length.
  • The choice of item selection index impacts diagnostic accuracy and test efficiency.