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.
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Staff from the Exceptional Children department in Charlotte-Mecklenburg Schools convened a group of their teachers in Spring 2020 to share their perspectives and ideas. This advisory group includes approximately 20 teachers of exceptional children across Charlotte-Mecklenburg Schools. In this Voices from the Field video, the National Center on Intensive Intervention spoke with four teachers in the advisory group about their work during COVID-19 restrictions.
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.
This webinar challenges current thinking about how to set appropriately ambitious and measurable behavioral goals in light of the 2017 Endrew F. v. Douglas County School District decision by the United States Supreme Court. Dr. Teri A. Marx from the National Center on Intensive Intervention and the PROGRESS Center, as well as Dr. Faith G. Miller from the University of Minnesota—Twin Cities, share how to set ambitious behavioral goals for students by using a valid, reliable progress monitoring measure, and how to write measurable and realistic goals focused on the replacement behavior.
In this video, Drs. Mitch Yell and Tessie Bailey share information about the 2017 Endrew F. v. Douglas County School District decision by the U.S. Supreme Court. They highlight implications for writing a student's IEP and discuss the importance of setting setting ambitious IEP goals to ensure that students make progress in light of their individual circumstances.
In this video, Amy McKenna, a special educator in Bristol Warren Regional School District shares her experience with data-based individualization (DBI). Amy discusses how she learned about DBI, the impact her use of the DBI process had on students she worked with, and how DBI helped changed her practice as a special educator.
Norms for oral reading fluency (ORF) can be used to help educators make decisions about which students might need intervention in reading and to help monitor students’ progress once instruction has begun. This paper describes the origins of the widely used curriculum-based measure of ORF and how the creation and use of ORF norms has evolved over time. Using data from three widely-used commercially available ORF assessments (DIBELS, DIBELS Next, and easyCBM), a new set of compiled ORF norms for grade 1-6 are presented here along with an analysis of how they differ from the norms created in 2006.
In Module 8 of the Intensive Intervention in Mathematics Course Content we highlight the necessity for implementing evidence-based practices with fidelity. We also explain how to make adaptations to the instructional platform when students demonstrate inadequate progress. We finish this module by putting all the information learned across modules together with the intensive intervention framework.
In this Voices from the Field post, Emma Shanahan reflects on her experiences with progress monitoring and data-based decision making as a teacher and shares findings from her recent research on DBI professional development.
This interactive self-paced module is intended to help educators and administrators learn about using teaming to support the data-based individualization (DBI) process.