This report reviews the reach of the NCII tools charts on SEA websites and within SEA policy to support identification and implementation of evidence-based interventions and assessment tools.
Search
Resource Type
DBI Process
Subject
Implementation Guidance and Considerations
Student Population
Audience
Search
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
This brief reviews provides considerations for creating readiness to implement DBI to support successful implementation and scale-up in schools.
This question bank includes questions that teams can use to develop a hypothesis about why an individual or group of students may not be responding to an intervention. The hypothesis should help guide intervention planning and selection of intensification strategies using the Intervention Intensification Strategy Checklist. When developing a hypothesis, teams should consider the intervention design, fidelity of implementation, and learner needs. Intervention fidelity data collected using the Student Intervention Implementation Log and informal diagnostic data may help teams answer the questions included in the question bank.
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 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 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.