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Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

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Statistical Learning in Dynamic Assessment to Identify Developmental Language Disorder in Spanish-English-Speaking

Sarah Lynn Neiling^1,2, Mary Alt², Leah L Kapa²

¹School of Education, University of Cincinnati, OH.

Language, Speech, and Hearing Services in Schools

|April 21, 2026

Summary

This study developed a dynamic assessment to identify developmental language disorder (DLD) in bilingual preschoolers. Combining modifiability and teaching scores showed high accuracy, suggesting potential for DLD identification.

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Published on: December 14, 2012

Area of Science:

Child language development
Bilingualism
Speech-language pathology

Background:

Developmental Language Disorder (DLD) affects language acquisition in children.
Accurate identification of DLD in bilingual preschoolers is challenging.
Dynamic assessment offers a potential tool for identifying DLD in diverse populations.

Purpose of the Study:

To create a reliable dynamic assessment for identifying DLD in Spanish-English speaking preschoolers.
To evaluate the classification accuracy of different scoring elements within the dynamic assessment.
To assess the feasibility of using statistical learning for DLD identification.

Main Methods:

A prospective feasibility study was conducted with 18 preschoolers (4;0-5;11), including typically developing (TD) children and those with DLD.
Participants completed a novel dynamic assessment in English, followed by the Bilingual English-Spanish Oral Screener.
Bayesian logistic regression and random forests were used to analyze classification accuracy.

Main Results:

The dynamic assessment demonstrated high fidelity (98%) and child accuracy interrater reliability (99%).
Modifiability rating scale interrater reliability was moderate (ρ = .62).
Combining modifiability ratings and teaching phase scores yielded the highest classification accuracy (Bayesian AUC = .94, Random Forest AUC = .87), with 83% sensitivity and 100% specificity.

Conclusions:

The dynamic assessment procedure is feasible and shows potential for diagnostic accuracy in identifying DLD.
Further research with larger sample sizes and improved modifiability scales is warranted.
Future studies should incorporate assessments in both Spanish and English.