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.
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An effective and efficient data system is essential for successful implementation of a multi-tiered system of support (MTSS). However, prior to selecting an appropriate system, schools and districts must identify what its staff and community need and what resources the district or school has to support an MTSS data system. This two-step tool can help teams to consider both what their needs are and to evaluate available tools against those needs. Step 1 can help your team systematically identify and document your MTSS data system needs and current context and step 2 focuses on selecting and evaluating a data system for conducting screening and progress monitoring within a tiered system of support based on the identified needs and context from step 1
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.
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 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.
This online course helps educators learn how to set goals, collect data, and make decisions using academic progress monitoring data.
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 video, Sandra Chafouleas, Professor of Educational Psychology in the Neag School of Education at the University of Connecticut, discusses the importance of progress monitoring in behavior and how it differs from screening and diagnostic assessment.
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.
Data teams serve multiple roles in the data-based individualization (DBI) process and across a multi-tiered system of supports (MTSS). Although schools may have multiple teams that review different types of data across a multi-tiered system of supports (MTSS), the intensive intervention or DBI team is focused on the needs of individual students who are not making progress in their current intervention or special education program. It is critical that these meetings are driven by data, occur regularly, and use an efficient, consistent process that allows participants to review progress and make intervention decisions for students. NCII has created a series of tools to help teams establish efficient and effective individual student data meetings.