Work

Mapping First Grader’s Numerical Development at Scale: Leveraging Cognitive Models in a Large-Scale Educational Assessment

Year: 2026

Type: article

Abstract: Large-scale assessments (LSAs) primarily support system-level monitoring, but their instructional diagnostic potential remains underused. Cognitive Diagnostic Models (CDMs) offer a promising avenue, though their application in LSAs poses theoretical and practical challenges. This study explores whether cognitive models used in item generation can directly inform Q-matrix construction for CDM analyses, enhancing diagnostic value in early numeracy assessment. Using data from Luxembourg’s school monitoring program (N = 35,058), we analyzed four cognitive attributes (counting, addition < 10, decomposition, addition > 10) using developmental frameworks. We compared a Single-Attribute Hierarchical Model, assuming linear progression, with a Multiple-Attribute Hierarchical Model, allowing skill interactions. Both hierarchical models reproduced expected developmental progressions, with decomposition emerging as a key threshold skill and socio-economic status showing the largest subgroup differences. Subgroup analyses revealed a smaller-than-expected impact of migration background, while math anxiety peaked at intermediate skill levels. Embedding cognitive models in LSAs can bridge system-level monitoring and instructional support.

Source: Educational Assessment

DOI: 10.1080/10627197.2026.2633512

Authors: Philipp Sonnleitner, Pamela Inostroza Fernández, Caroline Hornung

Institutions: University of Luxembourg, Universidad de Los Andes

Language: English

Topic: Psychometric Methodologies and Testing

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Quality education

Open Access status: hybrid

Funder: Fonds National de la Recherche Luxembourg