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
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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.
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.
Module 2 of the Intensive Intervention in Mathematics Course Content focuses on the assessment components of intensive intervention. We provide an overview of assessments before diving into instruction in order to stress the importance that intensive intervention cannot occur without adequate assessments in place.
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 webinar presents a data-based decision-making framework to individualize instruction for students with intensive needs in writing.
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.
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.
This training module demonstrates how academic progress monitoring fits into the Data-Based Individualization (DBI) process by (a) providing approaches and tools for academic progress monitoring and (b) showing how to use progress monitoring data to set ambitious goals, make instructional decisions, and plan programs for individual students with intensive needs.