This lesson, featuring Karen McWilliams, a 504 Coordinator and Dyslexia Teacher in Rochelle ISD in Texas, supports educators in using technology to teach foundational reading skills to students in elementary grades. During this virtual literacy lesson, students engage in a variety of facilitated activities to support phonemic awareness, phoneme–grapheme correspondence, irregular and high-frequency words, writing, and connected text. Educators may present this lesson to students one-on-one or in a small group. The templates were adapted from content developed by the University of Florida Literacy Institute to support educators implementing virtual instruction. The collection includes a tip sheet, a video examples, and slides illustrating the lesson.
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This lesson features Carla Jo Whatley, a First Grade Teacher at Ferris Intermediate in Ferris ISD in Texas. In the lesson she illustrates how to use virtual manipulatives within a math lesson. These manipulatives allow educators and students to engage in the Concrete-Representational-Abstract approach without having the physical materials in front of them. For some educators, switching between platforms has been challenging. This lesson can be used synchronously or asynchronously, does not require using multiple platforms, and allows educators to apply the features of interactive base ten blocks. The collection includes a tip sheet, two video examples, and slides with virtual base ten block practice examples.
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.
With the closure of schools due to the COVID-19 pandemic, educators and administrators need to rethink how they collect and analyze progress monitoring data in a virtual setting. This collection of frequently asked questions is intended to provide a starting place for consideration.
This template is intended to assist with the planning and documentation of dimensions of an intervention for small groups or an individual student within the data-based individualization (DBI) process.
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.
Support from leaders is essential for effective DBI implementation. This resource illustrates how DBI can help principals and local level administrators leverage existing resources, integrate supports for academics and behavior, define Tier 3, align special education and MTSS, establish effective data meetings, and improve outcomes for students who are at-risk for poor learning outcomes. In addition, the resource shares strategies and resources available to support implementation
Teams are a vital part of an effective multi-tiered system of supports (MTSS) across both academics and behavior as well as special education. Making connections across the across the various teams used in MTSS and special education can be challenging. This resource from NCII and the PBIS Center, provides information about how DBI can support IEP implementation and provides a table with key considerations for teams working across the MTSS system.
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. The DBI process includes five iterative steps: