Module 2 of the Intensive Intervention in Mathematics Course Content focuses on the assessment components of intensive intervention. We provide an overview of assessments before diving into instruction in order to stress the importance that intensive intervention cannot occur without adequate assessments in place.
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DBI Process
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Data-based individualization (DBI) is a research-based process for individualizing and intensifying interventions through the systematic use of assessment data, validated interventions, and research-based adaptation strategies. This document introduces and describes the DBI process and how it can be used to support students who require intensive intervention in academics and/or behavior.
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.
The Taxonomy of Intervention Intensity (Fuchs, Fuchs, & Malone, 2017) can be used to select or evaluate an intervention platform used as the validated intervention platform or the foundation of the DBI process. It can also be used to guide the adaptation of intensification of an intervention during the intervention adaptation step of the DBI process. The Taxonomy includes the following dimensions:
Module 4 of the Intensive Intervention in Mathematics Course Content focuses on the delivery of the instructional platform. We rely on evidence-based strategies to inform how teachers should deliver the instructional platform.
It is important that the instructional practices and interventions delivered within a school’s multi-tiered system of support (MTSS) be grounded in evidence. However, the “practice” that happens within each tier is different; therefore, the type of evidence that is required for each tier also must be different. A useful way to think about evidence-based practices in MTSS is to think about levels of evidence that vary and correspond to the different levels of intervention intensity at each tier. In the tables below, find resources to support the selection and evaluation of Tier 1, Tier 2, and Tier 3 or intensive interventions.
NCII, through a collaboration with the University of Connecticut, developed a set of course modules focused on developing educators’ skills in using explicit instruction. These course modules are designed to support faculty and professional development providers with instructing pre-service and in-service educators who are developing and/or refining their implementation of explicit instruction.
An effective and efficient data system is essential for successful implementation of a multi-tiered system of support (MTSS). However, prior to selecting an appropriate system, schools and districts must identify what its staff and community need and what resources the district or school has to support an MTSS data system. This two-step tool can help teams to consider both what their needs are and to evaluate available tools against those needs. Step 1 can help your team systematically identify and document your MTSS data system needs and current context and step 2 focuses on selecting and evaluating a data system for conducting screening and progress monitoring within a tiered system of support based on the identified needs and context from step 1
This activity was developed by Tammy Moran a special education teacher in Ferris Independent School District. In this lesson, she illustrates the use of the Understand-Plan-Solve-Evaluate (UPSE) Method. This method is a problem-solving strategy that can be used to support students struggling with word problems. The lesson can be used synchronously or asynchronously and does not require using multiple platforms. This collection includes a tip sheet, a video example, slides to facilitate the lesson, a UPSE template, and reflection questions.
This video shows how to use the set model to represent the fraction 3/4 with two-colored counting chips and clips. Individual chips within the set, represent the fractional parts. It is important that students be exposed to the set model because fractions in real-world settings are often represented this way.