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Testing the actual equivalence of automatically generated items.

Debora de Chiusole1, Luca Stefanutti2, Pasquale Anselmi2

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Summary

Ensuring test item equivalence is crucial for accurate assessments. This study uses knowledge space theory to propose and validate methods for testing item equivalence at deterministic and probabilistic levels.

Keywords:
Automatic item generationBasic local independence modelItem templateKnowledge assessmentKnowledge space theory

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Cognitive Science

Background:

  • Automatic item generation (AIG) requires methods to ensure test item equivalence for valid assessments.
  • Existing frameworks lack robust approaches for verifying equivalence across different item instances.

Purpose of the Study:

  • To propose and validate a framework for testing the equivalence of automatically generated test items.
  • To introduce deterministic and probabilistic levels of item equivalence within the knowledge space theory.

Main Methods:

  • Utilized the knowledge space theory framework to model item equivalence.
  • Developed two models: BLIM for deterministic equivalence and constrained BLIM for probabilistic equivalence.
  • Employed model comparison for testing equivalence, validated through simulation and empirical application.

Main Results:

  • Proposed a viable approach for testing item equivalence based on knowledge space theory.
  • Demonstrated the effectiveness of deterministic and probabilistic equivalence models.
  • Simulation and empirical data confirmed the proposed methods' viability.

Conclusions:

  • The knowledge space theory provides a robust framework for assessing automatic item generation quality.
  • The proposed methods offer a rigorous way to ensure the accuracy and fairness of assessments using generated items.