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Computerized Adaptive Testing for Cognitively Based Multiple-Choice Data.

Hulya D Yigit1, Miguel A Sorrel2, Jimmy de la Torre3

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This study introduces the Jensen-Shannon divergence (JSD) index for cognitive diagnosis computerized adaptive testing (CD-CAT). JSD significantly enhances attribute classification accuracy by analyzing student responses to multiple-choice items, reducing testing time.

Keywords:
G-DINAGDIJSDMC-DINAcognitive diagnosis modelscomputerized adaptive testingitem selection methods

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Learning Analytics

Background:

  • Cognitive Diagnosis Models (CDMs) offer insights into student knowledge profiles.
  • Cognitive Diagnosis Computerized Adaptive Testing (CD-CAT) leverages technology for efficient diagnostics.
  • Polytomous scoring in CD-CAT enhances diagnostic accuracy over dichotomous scoring for multiple-choice items.

Purpose of the Study:

  • To propose and evaluate the Jensen-Shannon divergence (JSD) index as an item selection method for the multiple-choice deterministic inputs, noisy "and" gate (MC-DINA) model.
  • To assess the impact of JSD on attribute classification accuracy and item usage.
  • To compare JSD with random selection and dichotomized response methods.

Main Methods:

  • The study employed the Jensen-Shannon divergence (JSD) index within the multiple-choice deterministic inputs, noisy "and" gate (MC-DINA) framework.
  • Attribute classification accuracy and item usage were evaluated under varying item quality and test termination rules.
  • Performance was benchmarked against random item selection and a dichotomized response approximation.

Main Results:

  • The Jensen-Shannon divergence (JSD) index significantly improved attribute classification accuracy under the MC-DINA model.
  • JSD effectively utilized information from distractors in multiple-choice items.
  • Accuracy improvements were observed even with very short test lengths.

Conclusions:

  • The JSD index offers a superior method for item selection in MC-DINA based CD-CAT.
  • Considering distractor information via JSD enhances diagnostic precision.
  • Reduced testing times are possible, leading to more focused learning opportunities in educational settings.