This training module, Using the Taxonomy of Intervention Intensity to Select, Design, and Intensify Intervention, introduces the Taxonomy of Intervention Intensity and describes how it supports the DBI process by helping provide explicit guidance on how to select and evaluate validated intervention programs to best meet students’ needs and intensify or adapt those interventions when students or groups of students do not adequately respond. At the end of the training participants will be able to:
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In this Voices from the Field, the National Center on Intensive Intervention (NCII) talks with Richard Carter, PhD, an assistant professor in the Department of Counseling, Leadership, Advocacy, and Design at the University of Wyoming. Dr. Carter teaches Mild and Moderate Disabilities, Assessment in Special Education, and Collaboration and is working to develop a micro-credentialing system for educators in the state. Dr. Carter discusses how he has integrated NCII’s data-based individualization (DBI) resources within his education preparation efforts
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 course collection provides a guide to available NCII courses for those who are newer to the DBI process or interested in learning more about how intensive intervention can support students with severe and persistent learning and/or social, emotional, or behavioral needs.
This handout briefly defines the seven dimensions of the Taxonomy of Intervention Intensity for academics and behavior. The Taxonomy of Intervention Intensity was developed based on research to support educators in evaluating and building intervention intensity. The seven dimensions include strength, dosage, alignment, attention to transfer, comprehensiveness, behavior or academic support, and individualization.
This module was adapted from a series of training modules developed by the National Center on Intensive Intervention (NCII) and is aimed at district or school teams involved in the initial planning for using data-based individualization (DBI) as a framework for providing intensive intervention in academics and behavior. The audience for this module may include school teams supporting academic intervention and progress monitoring in middle school mathematics.
This document presents considerations for implementing DBI in light of COVID-19 with an emphasis on delivery in virtual settings.
Intensive Intervention in Reading Course: Module 4 Overview This module provides an overview of data-based individualization (DBI), including using CBM measures, how to present level of performance and set student goals, and use data to make instructional decisions. This module is divided into five parts with an introduction and closing. A 508 compliant version of the full PowerPoint presentation across all parts of the module, a version of the PowerPoint that includes all the animations, and a workbook is available below.
The first module in the Intensive Intervention Math Course Content focuses on the mathematics content necessary to include within intensive intervention. This includes matching decisions about instruction and assessment to the mathematics content.
Research tells us that ongoing coaching is essential for achieving practice change. And without ongoing coaching and practice opportunities, professional development is highly unlikely to lead to increased knowledge and skills to implement a new practice soundly. This rings especially true for complex processes like data-based individualization (DBI). DBI requires that educators commit to engaging in the iterative process of providing intervention, analyzing progress monitoring data, and making data-based decisions to adapt and individualize interventions when needed. To help schools effectively implement DBI, ongoing implementation support in the form of coaching that provides opportunities to learn critical information, apply and receive feedback, and troubleshoot problems when they occur is essential.
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