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Related Experiment Video

Updated: Apr 20, 2026

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Developing a hierarchical cognitive second language (L2) reading model using multi-dimensional fuzzy Delphi method

Muhamad Firdaus Mohd Noh1, Mohd Effendi Ewan Mohd Matore2, Nur Ainil Sulaiman3

  • 1Faculty of Education, Universiti Kebangsaan Malaysia (UKM) Bangi, 43600, Selangor, Malaysia.

Acta Psychologica
|April 18, 2026
PubMed
Summary

This study identified core cognitive attributes for second language (L2) reading comprehension in secondary students, creating a hierarchical model. This model guides instruction and assessment design for better L2 reading skills development.

Keywords:
Cognitive diagnostic assessmentFuzzy Delphi methodInterpretive structural modelingQ matrixSecond language reading

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Area of Science:

  • Educational Psychology
  • Applied Linguistics
  • Cognitive Science

Background:

  • Secondary students often struggle with academic reading in a non-native language.
  • Current reading assessments lack a hierarchical understanding of cognitive skills.
  • Existing skills lists overlook prerequisite relationships crucial for L2 reading comprehension.

Purpose of the Study:

  • To identify core cognitive attributes essential for lower-secondary second language (L2) reading comprehension.
  • To develop a hierarchical model of these L2 reading attributes.
  • To inform the design of instructional strategies and diagnostic tools for L2 reading.

Main Methods:

  • A two-stage expert-elicitation process involving 12 experts.
  • Multidimensional Fuzzy Delphi Method (FDM) to screen candidate attributes for importance and relevance.
  • Interpretive structural modeling (ISM) to construct a hierarchical model of cognitive attributes.

Main Results:

  • A four-level hierarchy of L2 reading comprehension attributes was established.
  • Level IV: Recognizing word meaning & Retrieving explicit information; Level III: Identifying main ideas & Distinguishing facts/opinions; Level II: Making inferences; Level I: Drawing generalizations.
  • MICMAC analysis classified attributes into low-dependence, linkage, and dependent categories.

Conclusions:

  • The proposed hierarchical model provides a theory-aligned framework for L2 reading.
  • Actionable structures can inform Q-matrix design, item generation, and instructional sequencing.
  • Future research should validate the model with learner data and expand the expert panel.