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
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, will provide 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 will share project findings and provide recommendations for integrating those findings into professional development and practice to improve student outcomes.
In this webinar, Drs. Tessie Rose Bailey and Zach Weingarten from the National Center on Intensive Intervention and the PROGRESS Center, as well as Thom Jones from the Wyoming Department of Education and Justine Essex from Freedom Elementary School in Cheyenne, Wyoming shared how to set ambitious goals for students by selecting a valid, reliable progress monitoring measure, establishing baseline performance, choosing a strategy, and writing a measurable goal.
This training module demonstrates how academic progress monitoring fits into the Data-Based Individualization (DBI) process by (a) providing approaches and tools for academic progress monitoring and (b) showing how to use progress monitoring data to set ambitious goals, make instructional decisions, and plan programs for individual students with intensive needs.
This training module, includes four sections that (a) provide an overview of administering common general outcome measures for progress monitoring in reading and mathematics, (b) review graphed progress monitoring data, and (c) provide guidance on identifying what type of skills the intervention should target to be most effective in reading and mathematics.
This is part 1 of the larger module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part is intended to provide an overview of common general outcome measures (GOM) used for progress monitoring in reading and mathematics, with guidance on selecting an appropriate measure.
This is part 3 of the larger module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part is intended to provide participants with an introduction to error analysis of curriculum-based measures for the purpose of identifying skill deficits and providing examples of error analysis in reading and mathematics. Part 4, “Identifying Target Skills,” will further link these skill deficits to intervention.
This is part 4 of the module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part of the module is intended to provide participants with guidance for identifying skills to target in reading and math interventions.
This webinar presents a data-based decision-making framework to individualize instruction for students with intensive needs in writing.