# Language difference or difficulty learning? Assessing early language skills to identify risk for reading difficulty among young Spanish-English dual language learners

> **NIH NIH R21** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2022 · $222,581

## Abstract

Project Summary/Abstract
A major challenge in educational health research is that there are no well-established approaches for reliably
identifying early signs of reading difficulty among dual language learners (DLLs). Given the value of literacy to
individual well-being and the importance of early identification to maximize educational growth, there is a critical
need to optimize assessment to identify Spanish-English DLLs at risk for reading difficulty early in the elementary
years. Consequently, the long-term goal of the proposed research is to establish practical, reliable methods for
early identification of reading disability among DLLs. Representing one step toward this goal, the overall objective
of this project is to identify assessments of kindergarten language and preliteracy that: (1) reliably predict
Spanish-English DLLs’ reading development one year later and (2) are highly sensitive for identifying DLLs
classified as being at risk for reading difficulties in grade 1. We hypothesize that measures that tap multiple
underlying domains of linguistic knowledge will most strongly predict future reading development, but that the
relations between early language and later reading will be inconsistent across DLLs, depending on dual language
exposure, use, and early code-based literacy skills. We further hypothesize that measures of transferable
language skills will provide optimal single-measure discrimination for predicting reading difficulty in grade 1, but
that assessment in both languages will be necessary to achieve good classification accuracy. To test these
hypotheses, two specific aims will be addressed. First, this project will identify measures of DLLs’ English and
Spanish language skills in kindergarten language that reliably predict reading achievement in grade 1. Second,
we will determine the classification function of standardized measures of dual language abilities for
identifying Spanish-English DLLs at risk for reading difficulties in grade 1. To achieve these aims, children will
complete assessments of Spanish and English vocabulary knowledge, morphosyntax, early code-based literacy
skills, narrative macrostructure, and narrative microstructure during kindergarten. Their parents and teachers will
also be asked to describe the children's dual language exposure and use across the home and school
environment. One year later, during grade 1, children will complete measures of decoding and reading
comprehension in English and Spanish. This work is significant because early, reliable prediction of literacy
difficulties is critical to prevent students from falling behind their peers during the early elementary years. Early
identification of DLLs likely to have difficulty learning to read allows educators to allocate specialized resources
and individualized instruction to maximize gains for those students. This work is innovative because it directly
attends to the heterogeneity of the DLL population, examining how dual language exp...

## Key facts

- **NIH application ID:** 10470904
- **Project number:** 5R21HD106072-02
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Lisa Fitton
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $222,581
- **Award type:** 5
- **Project period:** 2021-08-17 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10470904

## Citation

> US National Institutes of Health, RePORTER application 10470904, Language difference or difficulty learning? Assessing early language skills to identify risk for reading difficulty among young Spanish-English dual language learners (5R21HD106072-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10470904. Licensed CC0.

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