How can data-based individualization (DBI) help educators to address the growing expectations for literacy outcomes for students with intellectual and developmental disabilities? In this webinar, Dr. Chris Lemons an NCII Advisor, Associate Professor of Special Education in the Graduate School of Education at Stanford University, and Co-Director of the Stanford Down Syndrome Research Center, provides an overview of activities conducted through an Office of Special Education Programs model demonstration project. This project focused on increased literacy outcomes using DBI, inclusion, and enhancing individualized education programs. The webinar shares project findings and provides recommendations for integrating those findings into professional development and practice to improve student outcomes.
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Using DBI to Improve Literacy Outcomes for Students with Intellectual and Developmental Disabilities
This webinar, Getting it Right from the Start: Why Embedding Data-Based Individualization in Teacher Education Programs Matters, discusses strategies to help teacher education faculty integrate the principles of intensive intervention within undergraduate and graduate-level programs for aspiring and current teachers. The webinar highlights free and publicly available resources for faculty to embed within courses and clinical experiences, including materials from a recent community of practice (CoP) that comprised educator preparation faculty.
This webinar, featuring Drs. Donna Sacco, John Hoover, and Tracy Spies, illustrates considerations for implementing data-based individualization (DBI) with ELs that accounts for their unique academic, social, behavioral, linguistic, and cultural experiences, assets, and needs. They share why it is important to (a) deliver instruction that represents culturally and linguistically sustaining best practices, and (b) distinguish the needs and assets of learners to improve progress (i.e., second-language acquisition, culture, learning challenges).
To support English Learners (ELs) with intensive intervention needs it is important to (a) deliver instruction that represents culturally and linguistically sustaining best practices, and (b) distinguish the needs and assets of learners to improve progress (i.e., second-language acquisition, culture, learning challenges). This brief illustrates considerations for implementing data-based individualization (DBI) with ELs that accounts for their unique academic, social, behavioral, linguistic, and cultural experiences, assets, and needs.
Special education teachers must have the skills to design and deliver intensive interventions for students with severe and persistent learning and behavioral needs. To ensure effective instruction for these students, preservice preparation programs must provide their teacher candidates with opportunities to learn, apply, and practice intensive intervention skills. Teacher preparation faculty play a critical role in ensuring the next generation of teachers have these opportunities.
The pandemic has disrupted and, in many cases hindered, learning for all students – most particularly for our most vulnerable populations. Data literacy is key to understanding and tailoring instructional decisions to address students’ varying needs. In this webinar panelists discuss strategies and frameworks to ensure educators are data literate and understand how data literacy can help districts and schools address learning opportunity gaps.
This Innovation Configuration can serve as a foundation for strengthening existing preparation programs so that educators exit with the ability to use various forms of assessment to make data-based educational and instructional decisions within an MTSS. The expectation is that these skills can be further honed and supported through inservice as practicing teachers.
This webinar challenges current thinking about how to set appropriately ambitious and measurable behavioral goals in light of the 2017 Endrew F. v. Douglas County School District decision by the United States Supreme Court. Dr. Teri A. Marx from the National Center on Intensive Intervention and the PROGRESS Center, as well as Dr. Faith G. Miller from the University of Minnesota—Twin Cities, share how to set ambitious behavioral goals for students by using a valid, reliable progress monitoring measure, and how to write measurable and realistic goals focused on the replacement behavior.
The purpose of this guide is to provide an overview of behavioral progress monitoring and goal setting to inform data-driven decision making within tiered support models and individualized education programs (IEPs).
This collection highlights a sampling of articles focused on intensive intervention and data-based individualization (DBI). Although there is a wealth of research on key components of the DBI process (e.g., progress monitoring, validated intervention programs), this list is not intended to include articles that focus on specific steps in the DBI process, nor is it an exhaustive review of all available literature. In the list below, we highlight seminal research on DBI and articles published since 2011, when NCII was first funded.