1) Evaluating and extending multivariate-multistage screening models to early identify children at high risk for developing reading difficulties.The authors describe analyses of a large, population-level database to test the effectiveness of multi-stage models’ predictions of reading difficulties in situations where learner data is limited. Next, the authors investigate advanced modeling techniques to identify more effective methods of identifying children at-risk for reading problems under sparse data conditions with the goal of expanding the effectiveness of broader screening efforts in real world settings.
2) Examining Language and Risk: Linguistic Difference and the Development of Early Reading Skills. The presenter will describe work with African American children who speak a non-mainstream American English (NMAE) dialect, which is different from the instructional dialect in most schools, and live in poverty. The presentation will highlight what is known about reading development in children who speak NMAE and its implications for how reading disability is identified and/or classified so that interventions can be better targeted to these students earlier.
3) Early Identification of Reading Disabilities in Spanish-Speaking English Learners.This paper describes research examining the identification and classification of reading and language disabilities among Spanish-speaking English language learners (ELLs), the fastest growing subgroup of students in U.S. public schools and who are disproportionately at risk for poor academic outcomes. The presenter will discuss comparisons of different disability identification and classification methods and examine student and contextual factors related to the consistency and inconsistency in identifications within and across classifications over time.
A reading expert, who works with at-risk children, will serve as discussant and address common issues across presentations, challenges for future research, and engage attendees to encourage comments and questions.