In this video, Sandra Chafouleas, Professor of Educational Psychology in the Neag School of Education at the University of Connecticut, discusses the importance of progress monitoring in behavior and how it differs from screening and diagnostic assessment.
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This webinar presents a data-based decision-making framework to individualize instruction for students with intensive needs in writing.
This webinar shares an evidence-based framework for providing effective writing instruction including a variety of techniques that teachers can use today to improve their students’ writing.
Module 5 begins a series of modules on the topic of explicit instruction. Explicit instruction is about modeling and practicing to help students reach academic goals. Throughout the module, educators will learn how selecting an important objective and learning outcomes, designing structured instructional experiences, explaining directly, modeling the skills being taught and providing scaffolded practice to achieve mastery can be used within the DBI framework to support instruction.
This video from the REL Midwest features Michigan educators discussing how districts can accelerate reading growth for young learners. Educators and leaders from Chippewa Hills School District, specifically discuss the use of data-based individualization (DBI).
The What Works Clearinghouse has released a new practice guide focused on reading interventions for students in fourth through ninth grade. This guide includes four evidence-based and practical recommendations that were compiled by a panel of national experts. The recommendations include: Build decoding skill so students can read complex multisyllabic words. Provide purposeful fluency-building activities to help students read effortlessly. Routinely use a set of comprehension-building practices to help students make sense of the text. Provide students with opportunities to practice making sense of stretch or challenging text that will expose them to complex ideas and information.
These two modules from the IRIS Center introduce users to progress monitoring in reading and mathematics. Progress monitoring is a type of formative assessment in which student learning is evaluated to provide useful feedback about performance to both learners and teachers. Because the overall progress monitoring process is almost identical for any subject area, the content in the two modules is very similar.
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.
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
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:
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