Progress monitoring is an essential part of a multi-tiered system of supports (MTSS) and, specifically, the data-based individualization (DBI) process. It allows educators and administrators to understand whether students are responding to intervention and if adaptations are needed. In addition, these data are often used to set high-quality academic and behavioral goals within the individualized education program (IEP) for students with disabilities. With the closure of schools due to the COVID-19 pandemic, educators and administrators need to rethink how they collect and analyze progress monitoring data in a virtual setting. This collection of frequently asked questions is intended to provide a starting place for consideration.
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This series includes video examples and tip sheets to help educators and families in using the NCII reading and mathematics sample lessons to support students with intensive needs. These lessons provide short instructional routines to encourage multiple practice opportunities using explicit instruction principles. The videos and tip sheets describe how educators can use the sample lessons to support instruction in a virtual setting, how educators can share these lessons with parents, and how parents can also implement the lessons to provide additional practice opportunities.
These videos and tips are part of a series of products to support students with intensive needs in the face of COVID-19. These videos illustrate how parents and grandparents can implement the NCII reading and mathematics sample lessons to provide additional practice. In addition to the video examples, a tip sheet is available to help parents implement the lessons. Implementation of Reading Lesson: Parent Example
This video and tips are part of a series of products to support students with intensive needs in the face of COVID-19. The series illustrates how educators can implement the NCII reading and mathematics sample lessons through virtual learning and provide tips for there use. These lessons provide short instructional routines to encourage multiple practice opportunities using explicit instruction principles. Tips for how educators can share these lessons with parents and families and video examples of family members implementing the lessons to enhance practice opportunities are also available.
This template is intended to assist with the planning and documentation of dimensions of an intervention for small groups or an individual student within the data-based individualization (DBI) process.
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.
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).
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.
If we don’t implement critical components of an intervention with consistency, we cannot link student outcomes to the instruction provided. Fidelity can help us to determine the effectiveness of an intervention, and identify if a student requires more intensive supports. This resource outlines five elements of fidelity and provides guiding questions for each.
This collection highlights a sampling of articles focused on intensive intervention and data-based individualization (DBI). Although there is a wealth of research on key components of the DBI process (e.g., progress monitoring, validated intervention programs), this list is not intended to include articles that focus on specific steps in the DBI process, nor is it an exhaustive review of all available literature. In the list below, we highlight seminal research on DBI and articles published since 2011, when NCII was first funded.