Assessment is an essential part of the data-based individualization (DBI) process and a multi-tiered system of support (MTSS). Without technically sound assessment, which provides accurate, meaningful information, a teacher has no objective method for determining what a student needs or how to intensify instruction to meet those needs. The close connection between assessment and intervention is at the foundation of the DBI process. This connection is what drives teacher decision making. With the right assessment tools and guidance on how to use them, teachers can make sound, data-based decisions about who needs intensive intervention, when to make instructional changes, and what skills to focus on. In the tables below, find resources to support the selection and evaluation of screening, progress monitoring and diagnostic assessments.
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The first module in the Intensive Intervention Math Course Content focuses on the mathematics content necessary to include within intensive intervention. This includes matching decisions about instruction and assessment to the mathematics content.
In this Voices from the Field video, Dr. Jason Harlacher and Veronica Fielder share CDE’s process for developing virtual learning modules on DBI and their strategies for ensuring the modules are accessible to educators.
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
This training module, Using the Taxonomy of Intervention Intensity to Select, Design, and Intensify Intervention, introduces the Taxonomy of Intervention Intensity and describes how it supports the DBI process by helping provide explicit guidance on how to select and evaluate validated intervention programs to best meet students’ needs and intensify or adapt those interventions when students or groups of students do not adequately respond. At the end of the training participants will be able to:
Monitoring Student Progress for Behavioral Interventions (DBI Professional Learning Series Module 3)
This module focuses on behavioral progress monitoring within the context of the DBI process and addresses: (a) methods available for behavioral progress monitoring, including but not limited to Direct Behavior Rating (DBR), and (b) using progress monitoring data to make decisions about behavioral interventions.
Research indicates that for successful implementation to occur, it is important to look at not only what is being implemented but how it is implemented. Implementing DBI often necessitates that educators make school-wide instructional adaptations, engage in systematic data analysis, and conduct individualized student-level decisions at levels that task the bandwidth of resources, staffing, and budgets of many schools. Assessing readiness from multiple stakeholders with different perspectives prior to implementation allows educators across many levels (schools, districts, and states) to prioritize areas for their initial efforts and then slowly use the momentum to build capacity toward full implementation. In this section, find training materials, lessons learned from those who have implemented DBI, and tools to assess your initial readiness and build capacity to implement intensive intervention.
This course collection provides a guide to available NCII courses for those who are newer to the DBI process or interested in learning more about how intensive intervention can support students with severe and persistent learning and/or social, emotional, or behavioral needs.
This tool is designed to help educators collect and graph academic progress monitoring data across multiple measures as a part of the data-based individualization (DBI) process. This tool allows educators to store data for multiple students (across multiple measures), graph student progress, and set individualized goals for a student on specific measures.
The purpose of this document is to provide content-specific examples of how to structure educator-level and/or systems-level coaching as a mechanism to ensure ongoing professional learning to support tiered intervention. This document provides examples of coaching supports, models, and functions within the context of tiered intervention (e.g., RtI, PBIS, MTSS) and data-based decision making (e.g., data-based individualization [DBI]) for educators who already have foundational knowledge and/or experience with coaching.
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