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.
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.
Progress monitoring is an essential part of a multi-tiered system of supports (MTSS) and, specifically, the data-based individualization (DBI) process. It allows educators and administrators to understand whether students are responding to intervention and if adaptations are needed. In addition, these data are often used to set high-quality academic and behavioral goals within the individualized education program (IEP) for students with disabilities. With the closure of schools due to the COVID-19 pandemic, educators and administrators need to rethink how they collect and analyze progress monitoring data in a virtual setting. This collection of frequently asked questions is intended to provide a starting place for consideration.
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).
For children with the most severe and persistent academic and/or behavioral challenges, parent and family involvement is vital. School teams can use this guide to better understand intensive intervention and how to engage parents and families with the process.
Teams are a vital part of an effective multi-tiered system of supports (MTSS) across both academics and behavior as well as special education. Making connections across the across the various teams used in MTSS and special education can be challenging. This resource from NCII and the PBIS Center, provides information about how DBI can support IEP implementation and provides a table with key considerations for teams working across the MTSS system.
Intensive intervention teams can use these checklists to monitor implementation of the data-based individualization (DBI) process during initial planning and ongoing review (progress monitoring) meetings in order to ensure teams develop high quality student plans. These detailed checklists may be most beneficial for less experienced teams. As teams become more familiar with DBI implementation, they may choose to use the checklists less frequently or focus on only a subset of items.
The purpose of this document is to provide an overview of the Center’s accomplishments and to highlight a set of lessons learned from the 26 schools that implemented intensive intervention while receiving technical support from the Center.
This tool is designed to help educators collect and graph academic progress monitoring data across multiple measures as a part of the data-based individualization (DBI) process. This tool allows educators to store data for multiple students (across multiple measures), graph student progress, and set individualized goals for a student on specific measures.