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Local processors and global integrators: Identifying L2 reader profiles using mixture item response theory models.

Farshad Effatpanah1, Shadi Noroozi2, Jörg Tobias Kuhn3

  • 1Faculty of Rehabilitation Sciences, TU Dortmund University, Dortmund, Germany; English Department, Islamic Azad University, Mashhad Branch, Mashhad, Iran.

Acta Psychologica
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study identified two distinct second/foreign language (L2) reading profiles using mixture item response theory (MixIRT) models. Findings reveal differences in processing strategies and proficiency levels between "Local Processors" and "Global Integrators".

Keywords:
GenderL2 reading comprehensionLexico-grammatical knowledgeMixture item response theory modelsMultiple profilesOverall language ability

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Applied Linguistics

Background:

  • Understanding second/foreign language (L2) reading proficiency is crucial for effective pedagogy.
  • Previous research often treats L2 reading as a monolithic skill, overlooking diverse reader profiles.
  • Item response theory (IRT) and its extensions offer sophisticated methods for modeling test data and identifying latent traits.

Purpose of the Study:

  • To explore latent reader profiles within L2 reading comprehension using mixture item response theory (MixIRT) models.
  • To characterize these profiles based on examinee demographics and linguistic knowledge.
  • To determine the best-fitting MixIRT model for identifying distinct L2 reader types.

Main Methods:

  • Analysis of item responses from 2439 examinees on a high-stakes multiple-choice L2 reading test.
  • Application of various MixIRT models, including mixture Rasch, 2PL, 3PL, and 4PL models, with varying numbers of latent classes.
  • Characterization of identified classes using gender, lexico-grammatical knowledge, and Cloze test proficiency scores.

Main Results:

  • The 2-parameter logistic (2PL) IRT model with two latent classes provided the best fit to the reading comprehension data.
  • Two distinct reader profiles emerged: 'Local Processors' (lower-moderate proficiency) and 'Global Integrators' (higher proficiency).
  • Local Processors demonstrated limited proficiency and relied on bottom-up, sentence-level strategies, while Global Integrators utilized integrated top-down and bottom-up approaches.

Conclusions:

  • L2 reading proficiency is not uniform and can be effectively modeled using mixture IRT approaches.
  • The identified 'Local Processors' and 'Global Integrators' profiles offer valuable insights into L2 reading strategy variations.
  • These findings have implications for diagnostic assessment and targeted instruction in L2 reading development.