This webinar reviews keys recommendations and lessons learned to help school and district leaders establish the conditions needed for educators to successfully implement data-based individualization (DBI) for students with the most intensive needs
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DBI Process
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Implementation Guidance and Considerations
Student Population
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Recording available for Planning for Success: Building Readiness to Implement Data-Based Individualization webinar.
This brief reviews provides considerations for creating readiness to implement DBI to support successful implementation and scale-up in schools.
Are your intervention planning meetings taking up too much time or resulting in limited solutions? This webinar, Better Together! Keys to Creating Collaborative, Efficient, and Effective Intensive Intervention Team Meetings, shares the important role teams can play in implementation of intensive intervention and identifies strategies to improve meeting efficiency and effectiveness. Presenters, Sarah Benz, Amy Peterson, and Nicole Bucka, introduce a series of data teaming tools designed to help facilitators and participants before, during, and after their intervention meeting. These tools allow for active participation in individual problem-solving meetings, which can provide a clear plan for intensifying an intervention based on a student’s unique needs. Presenters discuss how tools may be used and highlight lessons learned from district and school-level implementers.
State education agencies (SEAs) have an important role in initiating, supporting, and sustaining district- and school-level implementation of intensive intervention for students with severe and persistent learning and behavior needs. This document outlines five recommendations offered by SEA personnel who successfully led DBI capacity-building efforts in their states.
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.
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. Teams may consider using data available on the National Center on Intensive Intervention Academic Tools Chart and the publishers’ websites as well as results from previous implementation efforts. Each dimension will be rated on a scale of 0– Fails to Address Standard to 3 – Addresses Standard Well. Taxonomy of Intervention Intensity: Academic Rating Rubric Related Resources Taxonomy of Intervention Intensity Resources
This guide is a set of strategies and key practices with the ultimate goal of supporting students with the most intensive behavioral needs, their families, and educators in their transitions back to school during and following the global pandemic in a manner that prioritizes their health and safety, social and emotional needs, and behavioral and academic growth.
Successful implementation of a multi-tiered system of supports (MTSS) and, specifically, intensive intervention through the data-based individualization (DBI) process, demands the collection and analysis of data. As teams consider data collection, challenges may occur with assessment administration, scoring, and data entry (Taylor, 2009). This resource reviews three data collection and entry challenges and strategies to ensure data about risk status and responsiveness accurately represent student performance and minimize measurement errors.