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.
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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 Voices From the Field piece, the National Center on Intensive Intervention (NCII) speaks to Cyndi Caniglia, PhD, an assistant professor in the Department of Education at Whitworth University in Spokane, Washington about how she has meaningfully integrated the NCII Features of Explicit Instruction Course Content into her coursework.
This fourteen minute video shares Wyoming’s journey in building the capacity of educators to implement data-based individualization (DBI) to improve academic and behavior outcomes for students with disabilities as part of their state systemic improvement plan (SSIP). Wyoming administrators, teachers, parents and students from Laramie County School District # 1 and preschool sites share how DBI implementation impacted teacher efficacy, team meetings, quality of services, student confidence, and state and local collaboration.
In this Voices from the Field, the National Center on Intensive Intervention (NCII) talks with Richard Carter, PhD, an assistant professor in the Department of Counseling, Leadership, Advocacy, and Design at the University of Wyoming. Dr. Carter teaches Mild and Moderate Disabilities, Assessment in Special Education, and Collaboration and is working to develop a micro-credentialing system for educators in the state. Dr. Carter discusses how he has integrated NCII’s data-based individualization (DBI) resources within his education preparation efforts
If you are like most educators, you agree with the idea of providing intensive intervention for students with the most intractable academic and behavior problems. The question you may be asking is, how do I find the time? This guide includes strategies that educators can consider when trying to determine how to find the time for this intensification within the constraints of busy school schedules. Supplemental resources, planning questions, and example schedules are also provided.
In this article, Drs. Ketterlin Geller, Lembke, and Powell discuss how they are supporting educators to implement (1) the process of data-based individualization (DBI), (2) the principles of explicit and systematic instruction, and (3) key components of algebra readiness as part of Project STAIR (Supporting Teaching of Algebra: Individual Readiness).
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.