Tuesday Jun 22, 2021
Tuesday Jun 22, 2021
MPI research shows that one third of children ages 5 and under in the United States are Dual Language Learners (DLLs) who live with at least one parent who speaks a language other than English at home; over 80 percent are racial or ethnic minorities and 95 percent are U.S. citizens. These DLLs have the potential to become bilingual and biliterate, given appropriate home language and other supports. They also disproportionately face challenges including lower levels of family income, parental educational attainment, and access to the internet and digital devices.
With extensive research in recent decades demonstrating the disparities and language learning challenges and opportunities DLLs face, calls for adoption of early childhood policies and programs that are equitable and responsive to these children’s needs are longstanding. Yet, nearly all state early childhood systems currently lack standardized definitions and policies to identify DLL children, which means that these systems lack information critical to understanding whether DLLs are being effectively and equitably served. However, as new investments and substantial relief funds for early childhood services begin to flow to states, leaders and stakeholders both inside and outside government have a rare opportunity to develop processes to identify DLLs across early childhood systems—an essential step in promoting equitable services and outcomes for this large and growing population.
In this webinar, MPI experts Margie McHugh, Delia Pompa, and Maki Park discuss a framework describing the most critical elements that should be included in standardized, comprehensive DLL identification and tracking processes for early childhood systems, based on program and policy needs. They also explore promising approaches from across the United States as identified in an accompanying report and provide an analysis of state and national DLL data. The Executive Director of Early Edge spoke about the legislative efforts to effectively define and identify DLLs across the state of California through a strengths-based approach.