E.g., 06/14/2021
E.g., 06/14/2021
Ending the Invisibility of Dual Language Learners in Early Childhood Systems: A Framework for DLL Identification
Policy Briefs
May 2021

Ending the Invisibility of Dual Language Learners in Early Childhood Systems: A Framework for DLL Identification

More than 7.4 million U.S. children ages 5 and under live in a household where a language other than English is spoken by a parent or caregiver. But despite the size and growth of this Dual Language Learner (DLL) population, and these children’s distinct linguistic assets and learning needs, standardized policies for identifying them and understanding how best to support them are rare.

Related Resources

Other resources produced by MPI's National Center on Immigrant Integration Policy as part of this project include:

  • A report exploring federal, state, and local approaches to DLL identification and opportunities for improvement
  • Sociodemographic data profiles with the number and characteristics of DLLs and other young children ages 0 to 5 and ages 0 to 8, as well as their families, nationwide and in all 50 states and the District of Columbia

Gathering better information about DLLs—at a minimum, the number in a given program or system—is critical in order to determine whether these preschoolers are being effectively and equitably served. More robust identification procedures would also collect information on their language environments and experiences, skills in English and their home languages, and more.

This issue brief sets out a framework of the most critical elements that should ideally be included in standardized, comprehensive DLL identification and tracking processes for early childhood systems. It also identifies foundational system components that would need to be in place in order to support the development and implementation of the framework, including comprehensive data systems, professional development, and culturally relevant and age-appropriate early childhood assessments and tools.

Table of Contents 

1  Introduction

2  Key Elements of a DLL Identification Process

3  Opportunities to Advance This Framework

4  Conclusion