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.
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This is part 2 of the module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part includes examples of graphed data and is intended to provide participants with guidance for reviewing progress monitoring data to determine if the instructional plan is working or if a change is needed.
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 webinar describes contextual factors that can support or impede the implementation of intensive intervention.
This webinar discusses the importance of family engagement and provides examples of ways to engage families to support students.
In this video, Mary Randel, a doctoral candidate in Special Education at Michigan State University & NCII Coach for the Swartz Creek School District, addresses the importance of ensuring that students with disabilities have access to supports across the tiers of a tiered frameworks, especially intensive intervention.
In this video, Dr. Evelyn Johnson, Associate Professor at Boise State University, discusses how data can be used to support eligibility decisions for students with disabilities.
This report from Jobs for the Future and Authored by Sharon Vaughn, Lou Danielson, Rebecca Zumeta Edmonds, and Lynn Holdheide, 1) reviews previous efforts to promote better educational outcomes for students with disabilities, 2) describes research-based instructional strategies that can support them and other struggling learners, and 3) shares the kinds of policies and local resources needed to ensure that all young people have meaningful opportunities to learn deeply and become truly prepared to succeed in college, careers, and civic life.
Data teams serve multiple roles in the data-based individualization (DBI) process and across a multi-tiered system of supports (MTSS). Although schools may have multiple teams that review different types of data across a multi-tiered system of supports (MTSS), the intensive intervention or DBI team is focused on the needs of individual students who are not making progress in their current intervention or special education program. It is critical that these meetings are driven by data, occur regularly, and use an efficient, consistent process that allows participants to review progress and make intervention decisions for students. NCII has created a series of tools to help teams establish efficient and effective individual student data meetings.