This IRIS Star Legacy Module, first in a series of two, overviews data-based individualization and provides information about adaptations for intensifying and individualizing instruction. Developed in collaboration with the IRIS Center and the CEEDAR Center, this resource is designed for individuals who will be implementing intensive interventions (e.g., special education teachers, reading specialists, interventionists).
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This IRIS Star Legacy Module, the second in a series on intensive intervention, offers information on making data-based instructional decisions. Specifically, the resource discusses collecting and analyzing progress monitoring and diagnostic assessment data. Developed in collaboration with the IRIS Center and the CEEDAR Center, this resource is designed for individuals who will be implementing intensive interventions (e.g., special education teachers, reading specialists, interventionists).
This rubric uses descriptors of the dimensions of the Taxonomy of Intervention Intensity to support teams in selecting and evaluating validated interventions for small groups or individual students. Teams may consider using data available on the National Center on Intensive Intervention Academic Tools Chart and the publishers’ websites as well as results from previous implementation efforts. Each dimension will be rated on a scale of 0– Fails to Address Standard to 3 – Addresses Standard Well. Taxonomy of Intervention Intensity: Academic Rating Rubric Related Resources Taxonomy of Intervention Intensity Resources
This guide is a set of strategies and key practices with the ultimate goal of supporting students with the most intensive behavioral needs, their families, and educators in their transitions back to school during and following the global pandemic in a manner that prioritizes their health and safety, social and emotional needs, and behavioral and academic growth.
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 module describes how to use data (Module 6) to inform decision making in the classroom. How do you know you are choosing the right interventions, and implementing with the right intensity, to influence a change in student behavior? By the end of this module you should be able to: Describe why we use data for decision making Determine if core features of classroom management practices are in place with fidelity Determine if all individuals in your classroom are achieving desired outcomes Develop an action plan to enhance or intensify support as needed
This fourteen minute video shares Wyoming’s journey in building the capacity of educators to implement data-based individualization (DBI) to improve academic and behavior outcomes for students with disabilities as part of their state systemic improvement plan (SSIP). Wyoming administrators, teachers, parents and students from Laramie County School District # 1 and preschool sites share how DBI implementation impacted teacher efficacy, team meetings, quality of services, student confidence, and state and local collaboration.
In this article, Dr. Carrie Thomas Beck from the Oregon Department of Education discusses the dyslexia law in Oregon, the role of intensive intervention in Oregon’s dyslexia initiative, and provides advice for states defining their dyslexia frameworks.
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 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.