In this video, Mary Little, Professor and Program Coordinator of the Department of Child, Family, and Community Services at the University of Central Florida discusses why data and data-based decision making such a critical part of instruction and intervention.
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In this video, Sarah Powell, Assistant Professor in the Department of Special Education at the University of Texas at Austin, discusses key considerations when teaching students with math difficulty.
In this video, John M. Hintze, Professor in the Department of Student Development at the University of Massachusetts Amherst explains why it is important to consider whether an assessment is biased against a specific sub-group.
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).
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.
Teachers often note that students struggle with the transition between core instruction and intervention in mathematics. Thus, the purpose of these curriculum crosswalks is to identify points of alignment and misalignment between commonly used mathematics intervention and core instructional materials, with a particular focus on mathematics practice standards and vocabulary. We offer recommendations for improving alignment to help students more successfully participate in math instruction across settings. Math Curriculum Crosswalk: Grade 1 Math Curriculum Crosswalk: Grade 2 Math Curriculum Crosswalk: Grade 3
In this webinar, Dr. Kristen McMaster provides an overview of Curriculum-Based Measurement (CBM) and discusses how CBM data can be used at the secondary level to monitor student progress. She discusses the purpose of CBM, provides a brief description of the research, and demonstrates how CBM data can be used to monitor student progress. She reviews CBM tools that are available for high schools in reading, mathematics, and the content areas, and provides instructions for developing CBM tools for use at the high school level. Following Dr. McMaster's presentation, representatives from Walla Walla High School in Walla Walla, Washington discuss how they have monitored school progress as part of their tiered intervention model.
Watch and listen to Erica Lembke address the question, "So I’ve been collecting progress monitoring data, when do I know it’s time to make an intervention change?"
This toolkit provides activities and resources to assist practitioners in designing and delivering intensive interventions in reading and mathematics for K–12 students with significant learning difficulties and disabilities. Grounded in research, this toolkit is based on the Center on Instruction’s Intensive Interventions for Students Struggling in Reading and Mathematics: A Practice Guide, and includes the following resources:
How might MTSS affect what's measured on report cards, including academic and non-academic measures?
In this video, Dr. Rebecca Zumeta Edmonds, Co-Director of NCII, discusses ways that a multi-tiered system of supports (MTSS) can help educators consider what student report cards can measure beyond academic performance.
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