Optimizing Large‐Scale Mathematical Assessments: Leveraging Hierarchical Attribute Structures and Diagnostic Classification Models for Enhanced Student Diagnostics

Effatpanah, Farshad; Kunina‐Habenicht, Olga; BERNARD, Steve; HORNUNG, Caroline; SONNLEITNER, Philipp

doi:10.1111/emip.70016

Article (Scientific journals)

Optimizing Large‐Scale Mathematical Assessments: Leveraging Hierarchical Attribute Structures and Diagnostic Classification Models for Enhanced Student Diagnostics

Effatpanah, Farshad; Kunina‐Habenicht, Olga; BERNARD, Steve et al.

2026 • In Educational Measurement: Issues and Practice, 45 (2)

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https://hdl.handle.net/10993/68008

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10.1111/emip.70016

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Keywords :

diagnostic classification models, large-scale assessments, mathematical development, cognitive item models

Abstract :

[en] Diagnostic classification models (DCMs) assess students’ mastery of cognitive attributes to provide personalized ability profiles. Retrofitting DCMs to large‐scale mathematics assessments usually relies on inferred Q‐matrices, which can reduce accuracy and diagnostic value. This study evaluated whether constructing items from cognitive models—yielding Q‐matrices directly—and incorporating hierarchical relationships among attributes improve diagnostic outcomes. Responses from 5,336 third‐grade students to a Luxembourgish image‐based, large‐scale standardized mathematics exam were analyzed using multiple DCMs and their hierarchical extensions. Items were constructed based on a Q‐matrix, derived from the curriculum and cognitive models. The hierarchical A‐CDM outperformed other models, classifying students into 60 latent classes with acceptable attribute‐ and test‐level accuracy and more interpretable results than the G‐DINA model. Using cognitive model‐based item generation and Q‐matrices as well as specifying attribute hierarchies enhance the accuracy and interpretability of DCM‐based diagnostics in large‐scale assessments, complementing traditional psychometric approaches by discerning meaningful within‐score differences.

Disciplines :

Education & instruction

Author, co-author :

Effatpanah, Farshad ; Research Unit of Psychological Assessment, Faculty of Rehabilitation Sciences Technische Universität Dortmund Dortmund Germany

Kunina‐Habenicht, Olga ; Research Unit of Psychological Assessment, Faculty of Rehabilitation Sciences Technische Universität Dortmund Dortmund Germany

BERNARD, Steve ; University of Luxembourg

HORNUNG, Caroline ; University of Luxembourg

SONNLEITNER, Philipp ; University of Luxembourg

External co-authors :

yes

Language :

English

Title :

Optimizing Large‐Scale Mathematical Assessments: Leveraging Hierarchical Attribute Structures and Diagnostic Classification Models for Enhanced Student Diagnostics

Publication date :

17 March 2026

Journal title :

Educational Measurement: Issues and Practice

ISSN :

0731-1745

eISSN :

1745-3992

Publisher :

Wiley

Volume :

Issue :

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://onlinelibrary.wiley.com/doi/pdf/10.1111/emip.70016

FnR Project :

FNR13650128 - FAIR-ITEMS - Fairness Of Latest Innovations In Item And Test Development In Mathematics, 2019 (01/09/2020-31/08/2023) - Philipp Sonnleitner

Name of the research project :

R-AGR-3682 - C19/SC/13650128/FAIR-ITEMS - SONNLEITNER Philipp

Funders :

Fonds National de la Recherche Luxembourg
Deutsche Forschungsgemeinschaft

Funding number :

C19/SC/13650128

Available on ORBilu :

since 18 March 2026

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