This webinar reviews keys recommendations and lessons learned to help school and district leaders establish the conditions needed for educators to successfully implement data-based individualization (DBI) for students with the most intensive needs
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Resource Type
DBI Process
Subject
Implementation Guidance and Considerations
Student Population
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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 brief reviews provides considerations for creating readiness to implement DBI to support successful implementation and scale-up in schools.
This document highlights some common misconceptions about intensive academic and behavior interventions that experts from the Center on Positive Behavioral Interventions and Supports and NCII have observed in supporting the implementation of intensive intervention within the context of MTSS.
This module is intended to help educators and administrators to dive deeper into the steps of the data-based individualization (DBI) process for individualizing and intensifying interventions.
This self-paced module provides the foundational information for users interested in learning more about intensive intervention and the DBI process.
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 document presents considerations for implementing DBI in light of COVID-19 with an emphasis on delivery in virtual settings.
Successful implementation of a multi-tiered system of supports (MTSS) and, specifically, intensive intervention through the data-based individualization (DBI) process, demands the collection and analysis of data. As teams consider data collection, challenges may occur with assessment administration, scoring, and data entry (Taylor, 2009). This resource reviews three data collection and entry challenges and strategies to ensure data about risk status and responsiveness accurately represent student performance and minimize measurement errors.
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).