The purpose of this guide is to provide brief explanations of key practices that can be implemented when working with students in need of intensive intervention in mathematics. Special education instructors, math interventionists, and others working with students who struggle with mathematics may find this guide helpful. Strategies presented in this guide should be used in conjunction with teaching guides developed for specific mathematical concepts.
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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 webinar discusses four categories of practice for intensifying academic interventions for students with significant and persistent needs.
This webinar discusses 1) the importance of fractions instruction and typical challenges faced by students, 2) share recommendations for fractions instruction, and 3) provide considerations for supporting students within secondary or Tier 2 and intensive intervention.
What is an evidence-based practice? How do I know if evidence shows that a practice will be right for my students? Many practitioners ask these critical questions every day as they are faced with making decisions regarding how to best meet the needs of their students.
This webinar presents a data-based decision-making framework to individualize instruction for students with intensive needs in writing.
This webinar addresses a challenge faced by many teachers: feeling inundated by data while struggling to find useful information to guide intervention decision-making
The purpose of this document is to increase the capacity of practitioners and educational leaders to support a broad range of learners who need more literacy supports to become skilled readers and writers by identifying a set of essential practices that are research-supported and should be the focus of professional development. These practices for intensifying literacy instruction apply to those learners with severe and persistent reading and writing challenges who have not responded when provided with instruction aligned with state academic standards, regardless of disability status.
Providing more explicit instruction, captured within the comprehensiveness domain of the Taxonomy of Intervention Intensity, is critical within intensive intervention. The Recognizing Effective Special Education Teachers (RESET) project, funded by U.S. Department of Education Institute for Education Sciences (IES) and led by Evelyn Johnson at Boise State University, developed a series of rubrics based on evidence-based practices for students with high incidence disabilities. One set of rubrics focuses on explicit instruction. Based on the main ideas of Explicit Instruction, the Explicit Instruction Rubric was designed for use by supervisors and administrators to reliably evaluate explicit instructional practice, to provide specific, accurate, and actionable feedback to special education teachers about the quality of their explicit instruction, and ultimately, improve the outcomes for students with disabilities.
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
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