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Ant colony optimization for parallel test assembly.

Luc Zimny1, Ulrich Schroeders2, Oliver Wilhelm3

  • 1Institute of Psychology and Education, Ulm University, Albert-Einstein-Allee 47, 89081, Ulm, Germany. luc.zimny@uni-ulm.de.

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Summary
This summary is machine-generated.

Ant colony optimization (ACO) algorithms can now create multiple parallel short tests. These tests ensure reliable, precise, and gender-fair measurement of factual knowledge simultaneously.

Keywords:
Ant colony optimizationAutomatic test assemblyDeclarative knowledgeParallel tests

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Area of Science:

  • Psychometrics
  • Artificial Intelligence
  • Educational Measurement

Background:

  • Ant colony optimization (ACO) algorithms have been used for single short scale construction.
  • Existing methods face challenges in creating multiple parallel scales with competing criteria.

Purpose of the Study:

  • To demonstrate the versatility of ACO for constructing multiple parallel short scales.
  • To ensure these scales meet criteria for construct coverage, unidimensionality, reliability, precision, and gender fairness.
  • To establish test equivalence through aligned characteristic and information functions.

Main Methods:

  • Utilized an initial pool of 120 knowledge items.
  • Applied ACO algorithms to assemble three parallel 12-item tests.
  • Aligned test characteristic and information functions to ensure equivalence.
  • Cross-validated scales and examined associations with full scales and external covariates.

Main Results:

  • Successfully assembled three parallel 12-item knowledge tests.
  • Achieved adequate construct coverage, unidimensionality, reliability, precision, and gender fairness.
  • Demonstrated test equivalence through functional alignment.
  • Confirmed associations with full scales and covariates.

Conclusions:

  • ACO algorithms are versatile for advanced test assembly, including parallel forms.
  • The developed method ensures high-quality, equivalent, and psychometrically sound short scales.
  • Highlights potential for metaheuristic approaches in test development and equivalence studies.