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.
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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.
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.
This presentation was delivered by Dr. Tessie Rose Bailey as part of the Colorado Multi-Tiered System of Support Virtual Summit 2020. In the presentation, Dr. Bailey focused on considerations for providing virtual intervention and progress monitoring and highlights resources developed by the National Center on Intensive Intervention. Related Resources Find additional resources for educators and families support students at home Supporting Students With Intensive Needs During COVID-19
Staff from the Exceptional Children department in Charlotte-Mecklenburg Schools convened a group of their teachers in Spring 2020 to share their perspectives and ideas. This advisory group includes approximately 20 teachers of exceptional children across Charlotte-Mecklenburg Schools. In this Voices from the Field video, the National Center on Intensive Intervention spoke with four teachers in the advisory group about their work during COVID-19 restrictions.
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.
NCII’s Faculty Professional Learning Series is an opportunity for teacher preparation faculty and professional development providers to learn from colleagues and NCII experts about available tools and resources to enhance coursework, field experiences, and professional development opportunities related to intensive intervention. Webinar 1: Preparing Teachers to Deliver Intervention in Virtual Settings This webinar presented five practical strategies for adapting preparation and professional learning experiences to help teachers develop skills for delivering intensive intervention in virtual settings.
The purpose of this document is to provide content-specific examples of how to structure educator-level and/or systems-level coaching as a mechanism to ensure ongoing professional learning to support tiered intervention. This document provides examples of coaching supports, models, and functions within the context of tiered intervention (e.g., RtI, PBIS, MTSS) and data-based decision making (e.g., data-based individualization [DBI]) for educators who already have foundational knowledge and/or experience with coaching.
This two page handout defines the Taxonomy of Intervention Intensity through guiding questions and highlights when the Taxonomy of Intervention Intensity can be used within the data-based individualization (DBI) process. Teams can use the dimensions to evaluate a current intervention, select a new intervention and intensify interventions when students do not respond.
In this Voices From the Field piece, the National Center on Intensive Intervention (NCII) speaks to Cyndi Caniglia, PhD, an assistant professor in the Department of Education at Whitworth University in Spokane, Washington about how she has meaningfully integrated the NCII Features of Explicit Instruction Course Content into her coursework.