This collection of training materials can be used to provide an overview of the Taxonomy of Intervention Intensity for selecting, evaluating, and intensifying interventions or to provide specific examples in reading, mathematics, and behavior. The training materials include presentation slides with suggested speaker notes and workbooks with application activities. The modules are intended to be delivered by a trained, knowledgeable professional.
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DBI Process
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Implementation Guidance and Considerations
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This checklist can be used by teams to help identify ideas to intensify interventions based on their hypothesis for why the student may not be responding to an intervention. The checklist is aligned with the dimensions of the Taxonomy of Intervention Intensity.
This question bank includes questions that teams can use to develop a hypothesis about why an individual or group of students may not be responding to an intervention. The hypothesis should help guide intervention planning and selection of intensification strategies using the Intervention Intensification Strategy Checklist. When developing a hypothesis, teams should consider the intervention design, fidelity of implementation, and learner needs. Intervention fidelity data collected using the Student Intervention Implementation Log and informal diagnostic data may help teams answer the questions included in the question bank.
The Colorado Department of Education (CDE) has been working closely with NCII to align and scale up use of data-based individualization (DBI) across the state. One of the strategies CDE has used is the development of virtual learning resources and online learning modules on DBI to help make professional learning accessible to all educators. In this Voices from the Field video, Dr. Jason Harlacher and Veronica Fielder share CDE’s process for developing virtual learning modules on DBI and their strategies for ensuring the modules are accessible to educators.
This module is intended to help educators and administrators understand the dimensions of the Taxonomy of Intervention Intensity and how it can be used to select, evaluate, and intensify interventions.
This module is intended to help educators and administrators to dive deeper into the steps of the data-based individualization (DBI) process for individualizing and intensifying interventions.
This module provides the foundational information for users interested in learning more about intensive intervention and the DBI process. The module defines intensive intervention and DBI, describes how intensive intervention fits within a tiered system such as MTSS, RTI, or PBIS, demonstrates how intensive intervention can provide a systemic process to deliver specialized instruction for students with disabilities, and provides two case examples to allow viewers to apply new knowledge.
This IRIS Star Legacy Module, first in a series of two, overviews data-based individualization and provides information about adaptations for intensifying and individualizing instruction. Developed in collaboration with the IRIS Center and the CEEDAR Center, this resource is designed for individuals who will be implementing intensive interventions (e.g., special education teachers, reading specialists, interventionists).
This IRIS Star Legacy Module, the second in a series on intensive intervention, offers information on making data-based instructional decisions. Specifically, the resource discusses collecting and analyzing progress monitoring and diagnostic assessment data. Developed in collaboration with the IRIS Center and the CEEDAR Center, this resource is designed for individuals who will be implementing intensive interventions (e.g., special education teachers, reading specialists, interventionists).
The pandemic has disrupted and, in many cases hindered, learning for all students – most particularly for our most vulnerable populations. Data literacy is key to understanding and tailoring instructional decisions to address students’ varying needs. 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.