This video features reflections from Bill Rasplica, the former executive director of Franklin Pierce Schools, about his experiences implementing DBI, lessons learned, and recommendations for other district leaders.
Implementation Guidance and Considerations
In this Voices from the Field post, we archive the presentations from day 1 of the NCII 10-year celebration of the implementation of intensive intervention. On this day, panelists shared stories focused on creating the systems to support implementation of intensive intervention.
Within a multi-tiered system of supports (MTSS), intensive intervention, also known as Tier 3, is designed to support students with the most severe and persistent learning and/or behavior difficulties. 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 three-part Voices from the Field video series profiles how Education Service Center (ESC) 15 in Texas approached implementing the DBI process in San Saba Independent School District (ISD). In these videos, Dedra Carter and Valerie Moos from ESC 15 and Jenna McSherry from San Saba ISD, discuss their experiences and recommendations for other districts implementing DBI.
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).
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).
During fall 2020, educators provided virtual, in-person, and hybrid intervention with an ongoing need to engage with and support parents and families. Although the context and environment may have changed, the focus on providing high-quality interventions with validated practices, monitoring student progress, and adapting and intensifying supports based on student data as outlined in the data-based individualization (DBI) process continues to be applicable across virtual, in-person, or hybrid models. This document presents considerations for implementing DBI in light of COVID-19 with an emphasis on delivery in virtual settings.
This Innovation Configuration can serve as a foundation for strengthening existing preparation programs so that educators exit with the ability to use various forms of assessment to make data-based educational and instructional decisions within an MTSS. The expectation is that these skills can be further honed and supported through inservice as practicing teachers.
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.
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.