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
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.
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.
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