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A Comparative Study of Item Response Theory Models for Mixed Discrete-Continuous Responses.

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  • 1College of Education, University of Oregon, Eugene, OR 97403, USA.

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|March 27, 2024
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Summary

New measurement models for AI-driven language proficiency tests show promise. The Beta item response model offers superior predictive accuracy for dictation tasks, though benchmarks for model fit are needed.

Keywords:
bounded continuous datacontinuous response modeldictation taskitem response theorylanguage assessmentnatural language processingzero-and-one inflated data

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Area of Science:

  • Educational Measurement
  • Artificial Intelligence in Education
  • Psychometrics

Background:

  • Language proficiency assessments are crucial for education and careers.
  • AI integration enables complex item types like dictation tasks with mixed response distributions.
  • Existing measurement models may not adequately capture these unique response features.

Purpose of the Study:

  • To evaluate novel measurement models for AI-driven language assessments with mixed discrete-continuous response features.
  • To assess the performance of zero-and-one-inflated extensions of Beta, Simplex, and Samejima's Continuous item response models.
  • To incorporate collateral information using latent regression for improved parameter estimation.

Main Methods:

  • Evaluation of extended Beta, Simplex, and Samejima's Continuous item response models.
  • Application of latent regression to incorporate collateral information.
  • Comparison of model performance using item and person parameters and out-of-sample predictive accuracy.

Main Results:

  • All evaluated models yielded highly correlated item and person parameters.
  • The Beta item response model demonstrated superior out-of-sample predictive accuracy.
  • A significant challenge identified is the lack of established benchmarks for model and item fit for these novel models.

Conclusions:

  • Novel measurement models, particularly the Beta item response model, are effective for AI-driven language assessments with mixed response distributions.
  • Further research is essential to develop benchmarks for evaluating the fit of these innovative models.
  • Establishing reliability and validity benchmarks is critical for real-world application of these advanced assessment tools.