The pandemic has disrupted and, in many cases hindered, learning for all students – most particularly for our most vulnerable populations. Data literacy is key to understanding and tailoring instructional decisions to address students’ varying needs. In this webinar panelists discuss strategies and frameworks to ensure educators are data literate and understand how data literacy can help districts and schools address learning opportunity gaps.
This presentation was delivered by Dr. Tessie Rose Bailey as part of the Colorado Multi-Tiered System of Support Virtual Summit 2020. In the presentation, Dr. Bailey focused on considerations for providing virtual intervention and progress monitoring and highlights resources developed by the National Center on Intensive Intervention. Related Resources Find additional resources for educators and families support students at home Supporting Students With Intensive Needs During COVID-19
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.
Successful implementation of a multi-tiered system of supports (MTSS) and, specifically, intensive intervention through the data-based individualization (DBI) process, demands the collection and analysis of data. As teams consider data collection, challenges may occur with assessment administration, scoring, and data entry (Taylor, 2009). This resource reviews three data collection and entry challenges and strategies to ensure data about risk status and responsiveness accurately represent student performance and minimize measurement errors.
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.
In this webinar, Dr. Sarah Powell an Associate Professor in the Department of Special Education at the University of Texas at Austin highlights freely available tools and resources that can help educators consider a scope and sequence for math skills, assessment and intervention practices, instructional delivery, concepts and procedures for whole and rational numbers, intensification considerations, and more. The webinar reviews the content available from the Intensive Intervention Math Course Content. The course content consists of eight modules covering a range of math related topics. Each module includes video lessons, activities, knowledge checks, practice-based opportunities, coaching materials and other resources.
In this webinar, held February 19, 2019, Drs. Rebecca Zumeta Edmonds, Sarah Powell, and Devin Kearns, 1) reviewed the evidence-base behind explicit instruction for students with disabilities and 2) highlighted recently released course content that is designed to help educators learn how to deliver explicit instruction and review their current practices.
This video demonstrates how to use fraction tiles and the set model to convert mixed numbers to improper fractions. It is important that students have the opportunity to convert fractions using both models of representation.
This video demonstrates how to use the set model to convert mixed numbers to improper fractions. It is important that students are exposed to converting fractions using this model because it is often how fractions are represented in the real world. Beginners and students who struggle may find the set model difficult to understand because the whole (1) is represented by a set of chips (4 chips in this example); therefore, students will benefit from explicit modeling and several opportunities to engage in guided and independent practice.