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.
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This Innovation Configuration can serve as a foundation for strengthening existing preparation programs so that educators exit with the ability to use various forms of assessment to make data-based educational and instructional decisions within an MTSS. The expectation is that these skills can be further honed and supported through inservice as practicing teachers.
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.
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.
The purpose of this guide is to provide an overview of behavioral progress monitoring and goal setting to inform data-driven decision making within tiered support models and individualized education programs (IEPs).
In this article, Dr. Jennifer Ledford shares information about single-case design research and how it relates to intensive intervention as well as resources from the Council for Exceptional Children Division for Research (CEC DR).
This is part 1 of the larger module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part is intended to provide an overview of common general outcome measures (GOM) used for progress monitoring in reading and mathematics, with guidance on selecting an appropriate measure.
This is part 4 of the module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part of the module is intended to provide participants with guidance for identifying skills to target in reading and math interventions.
This module serves as an introduction to important concepts and processes for implementing functional behavior assessment (FBA), including behavior basics such as reinforcement and punishment. Throughout this module, participants will discuss both real world and school based examples to become familiar with the FBA process and develop a deeper understanding and awareness of the functions of the behavior. Key topics include (a) defining FBAs in the context of DBI; (b) basic concepts in behavior, including antecedents, behaviors, and consequences; (c) levels of FBAs; and (d) considerations and procedures for conducting FBAs.