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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The Taxonomy of Intervention Intensity (Fuchs, Fuchs, & Malone, 2017) can be used to select or evaluate an intervention platform used as the validated intervention platform or the foundation of the DBI process. It can also be used to guide the adaptation of intensification of an intervention during the intervention adaptation step of the DBI process. The Taxonomy includes the following dimensions:
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.
It is important that the instructional practices and interventions delivered within a school’s multi-tiered system of support (MTSS) be grounded in evidence. However, the “practice” that happens within each tier is different; therefore, the type of evidence that is required for each tier also must be different. A useful way to think about evidence-based practices in MTSS is to think about levels of evidence that vary and correspond to the different levels of intervention intensity at each tier. In the tables below, find resources to support the selection and evaluation of Tier 1, Tier 2, and Tier 3 or intensive interventions.
This four-part webinar series is focused on the Taxonomy of Intervention Intensity. This series provides an overview of the dimensions of the Taxonomy of Intervention Intensity and case applications showing how the taxonomy can be used to guide the intensification of reading, mathematics, and behavior interventions.
This webinar focuses on ways educators and educational leaders can increase their capacity to develop skilled readers and writers, identify critical dimensions for designing intervention platforms as the foundation for effective instruction, and adapt interventions to increase the instructional intensity.
This guide was developed by Melanie Kowalick an MTSS Curriculum Specialist in Wichita Falls Independent School District. This planning guide may be used for planning short intervention activities, review and practice activities, or progress monitoring checks. During school closures, we learned that virtual intervention does not look the same as face-to-face intervention. Parent support and planning are going to be the key to helping our students who have difficulties with reading and mathematics. For educators or parents, part of this support includes simple ways to monitor student progress.
This resource developed by Sarah Thorud, Elementary Reading Specialist from Clatskanie School District in Oregon focuses on implementing screening and progress monitoring virtually. It includes guiding questions and considerations for implementation, video examples, and a sample sign-up sheet for screening and progress monitoring students virtually.
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, Michelle Hosp, Associate Professor in the College of Education at the University of Massachusetts Amherst discusses why your progress monitoring tool may not directly focus on the skills that you are teaching.