In this webinar, experts from the PROGRESS Center and National Center on Intensive Intervention (NCII) will model how practitioners can use data-based individualization (DBI) to develop and implement SDI for students with disabilities. A panel of special educators will share how using DBI improved the efficiency and effectiveness of their service delivery, communication with families, and collaboration with other educators.
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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).
This rubric uses descriptors of the dimensions of the Taxonomy of Intervention Intensity to support teams in selecting and evaluating validated interventions for small groups or individual students. Teams may consider using data available on the National Center on Intensive Intervention Academic Tools Chart and the publishers’ websites as well as results from previous implementation efforts. Each dimension will be rated on a scale of 0– Fails to Address Standard to 3 – Addresses Standard Well. Taxonomy of Intervention Intensity: Academic Rating Rubric Related Resources Taxonomy of Intervention Intensity Resources
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 webinar, Drs. Tessie Rose Bailey and Zach Weingarten from the National Center on Intensive Intervention and the PROGRESS Center, as well as Thom Jones from the Wyoming Department of Education and Justine Essex from Freedom Elementary School in Cheyenne, Wyoming shared how to set ambitious goals for students by selecting a valid, reliable progress monitoring measure, establishing baseline performance, choosing a strategy, and writing a measurable goal.
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).
The 2017 Supreme Court decision Endrew F. v. Douglas County School District highlighted the importance of monitoring students’ progress toward appropriately challenging individualized educational program (IEP) annual goals and making changes to students’ educational programs when needed. In this guide, we explain how educators can establish IEP goals that are measurable, ambitious, and appropriate in light of the student's 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.
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.