Data-based individualization (DBI) is a research-based process for individualizing and intensifying interventions through the systematic use of assessment data, validated interventions, and research-based adaptation strategies. This document introduces and describes the DBI process and how it can be used to support students who require intensive intervention in academics and/or behavior.
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In this webinar, Dr. Kristen McMaster provides an overview of Curriculum-Based Measurement (CBM) and discusses how CBM data can be used at the secondary level to monitor student progress. She discusses the purpose of CBM, provides a brief description of the research, and demonstrates how CBM data can be used to monitor student progress. She reviews CBM tools that are available for high schools in reading, mathematics, and the content areas, and provides instructions for developing CBM tools for use at the high school level. Following Dr. McMaster's presentation, representatives from Walla Walla High School in Walla Walla, Washington discuss how they have monitored school progress as part of their tiered intervention model.
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 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.
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.
This module discusses how to define, measure and monitor behavior. By the end of the module you should be able to: Select an appropriate target behavior Write an operational definition for a target behavior Identify relevant dimensions of behavior Choose a measurement system based on relevant dimensions of behavior Use graphing conventions to create meaningful visual displays of data
In this webinar panelists discuss strategies and frameworks to ensure educators are data literate and understand how data literacy can help districts and schools address learning opportunity gaps.
In this video, Dr. Devin Kearns, an Assistant Professor of Special Education in the Department of Education Psychology at the Neag School of Education at the University of Connecticut and NCII Trainer & Coach, discusses things to consider when selecting an academic progress monitoring tool.
In this video, Dr. Devin Kearns, an Assistant Professor of Special Education in the Department of Education Psychology at the Neag School of Education at the University of Connecticut and NCII Trainer & Coach, discusses importance of consistency when selecting, administering, and scoring progress monitoring tools.
This webinar shares 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.