Closing the Gap: Addressing the Learning Needs of English Language Learners in Mainstream Classroom

How can teachers better identify English Language Learners (ELLs) at risk for learning disabilities?

It is estimated that about half of ELL students have been misdiagnosed with learning disabilities and referred for special education services, resulting in lower educational expectations and lost instructional time in mainstream classrooms. Li and her team will test a two-stage screening model for ELLs at risk for learning disabilities, consisting of a universal screening tool and a screening tool specifically intended for use with ELLs. They will examine whether this model can better differentiate learning disabilities from language and literacy skill difficulties that occur during second language acquisition than the use of a single, universal screening tool. Li will work with the Houston Independent School District, in which nearly a third of students are ELLs, to refine the Lexical Specificity Task (LST), an interactive, computer-based training program and assessment used in the Netherlands, to assess the ease with which ELLs are able to identify the phoneme pairs necessary to read in English. During the first phase of the project, Li and her team will test the reliability and feasibility of using the adopted LST, sampling 50 first-grade ELLs who have been identified by the universal screening tool as at risk for learning disabilities and five teachers. The team will revise the LST’s items based on teacher surveys and data from both screening tools. During the second phase of the project, the team will test the LST as a supplement to the screening tool that HISD currently uses. They will recruit about 100 additional first-grade ELLs identified by the universal screener as at-risk for learning disabilities and ten additional teachers. Students will be randomly assigned to either the LST condition or a business-as-usual condition. Li and her team will examine the classification accuracy of the two-stage model and determine whether the two-stage model can effectively eliminate false-positive learning disability identifications.