This series of videos provides brief instructional examples for supporting students who need intensive instruction in the area of fractions. Within college- and career-ready standards fractions are typically taught in Grades 3-5. Developing an understanding of fractions as numbers includes part/whole relationship, number on the number line, equivalent fractions, whole numbers as fractions, and comparing fractions These videos may be used as these concepts are introduced, or with students in higher grade levels who continue to struggle with the concepts. Special education teachers, math interventionists, and others working with struggling students may find these videos helpful.
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Module 6 is the second in a set of four course modules focused on explicit instruction. This module introduces the concept of supporting practices necessary for successful implementation of explicit instruction. The module introduces how to use effective methods to elicit frequent responses. Throughout the module, educators will learn how eliciting frequent responses support instruction within the DBI framework.
In this video, Amy McKenna, a special educator in Bristol Warren Regional School District shares her experience with data-based individualization (DBI). Amy discusses how she learned about DBI, the impact her use of the DBI process had on students she worked with, and how DBI helped changed her practice as a special educator.
Intensive Intervention in Reading Course: Module 4 Overview This module provides an overview of data-based individualization (DBI), including using CBM measures, how to present level of performance and set student goals, and use data to make instructional decisions. This module is divided into five parts with an introduction and closing. A 508 compliant version of the full PowerPoint presentation across all parts of the module, a version of the PowerPoint that includes all the animations, and a workbook is available below.
In this video, Mary Randel, a doctoral candidate in Special Education at Michigan State University & NCII Coach for the Swartz Creek School District, addresses the importance of ensuring that students with disabilities have access to supports across the tiers of a tiered frameworks, especially intensive intervention.
In this video, Dr. Evelyn Johnson, Associate Professor at Boise State University, discusses how data can be used to support eligibility decisions for students with disabilities.
In this webinar, Dr. Sarah Powell an Associate Professor in the Department of Special Education at the University of Texas at Austin introduces a new free resource from NCII that can be used by faculty to develop or supplement coursework to ensure educators are prepared to support students with intensive math needs. The Intensive Intervention Math Course Content consists of eight modules covering a range of math related topics. Each module includes video lessons, activities, knowledge checks, practice-based opportunities, and more! In this webinar, Dr. Powell reviews the content available, discusses how it could be used as you develop courses, and answers questions that you might have.
This resource developed by Sarah Thorud, Elementary Reading Specialist from Clatskanie School District in Oregon focuses on implementing screening and progress monitoring virtually. It includes guiding questions and considerations for implementation, video examples, and a sample sign-up sheet for screening and progress monitoring students virtually.
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 module discusses how to define, measure and monitor behavior. By the end of the module you should be able to: Select an appropriate target behavior Write an operational definition for a target behavior Identify relevant dimensions of behavior Choose a measurement system based on relevant dimensions of behavior Use graphing conventions to create meaningful visual displays of data