When a student fails to respond to a validated intervention, teams need to identify why the student is not responding to determine how to adapt the intervention. Diagnostic data can assist teams in this process. They may be used to understand a student’s specific skill deficits and strengths or to identify the environmental events that predict and maintain the student’s problem behavior.
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This module was adapted from a series of training modules developed by the National Center on Intensive Intervention (NCII) and is aimed at district or school teams involved in the initial planning for using data-based individualization (DBI) as a framework for providing intensive intervention in academics and behavior. The audience for this module may include school teams supporting academic intervention and progress monitoring in middle school mathematics.
This brief offers recommendations to support educators to efficiently collect, analyze, and use diagnostic data to adapt or intensify intervention.
Intensive Intervention in Reading Course: Module 4 Overview This module provides an overview of data-based individualization (DBI), including using CBM measures, how to present level of performance and set student goals, and use data to make instructional decisions. This module is divided into five parts with an introduction and closing. A 508 compliant version of the full PowerPoint presentation across all parts of the module, a version of the PowerPoint that includes all the animations, and a workbook is available below.
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.
In this video, Dr. Devin Kearns, an Assistant Professor of Special Education in the Department of Education Psychology at the Near School of Education at the University of Connecticut and NCII Trainer & Coach, discusses considerations for progress monitoring.
This webinar describes how the RIOT/ICEL matrix can support problem-solving by helping teams to organize their diagnostic data, refine hypotheses, and guide decision making.
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