This webinar describes how the RIOT/ICEL matrix can support problem-solving by helping teams to organize their diagnostic data, refine hypotheses, and guide decision making.
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DBI Process
Subject
Implementation Guidance and Considerations
Student Population
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This resource is a companion to NCII’s Clarifying Questions to Create a Hypothesis to Guide Intervention Changes: Question Bank and provides additional questions for teams to consider for students who are English learners.
This document addresses five guiding questions for educators to consider when reviewing and interpreting assessment data for English Learners and includes links to selected resources.
This brief offers recommendations to support educators to efficiently collect, analyze, and use diagnostic data to adapt or intensify intervention.
The purpose of this training is to gain foundational knowledge of how all behavior serves a purpose or function. This foundational knowledge is core to understanding behavior, supporting students with challenging behavior, and diagnosing the function of behavior and developing effective behavioral interventions. This module introduces function of behavior and provides suggestions for how you can use this understanding within the context of a data-based individualization (DBI) process. While this module briefly mentions the role of a Functional Behavioral Assessment (FBA), this is not the focus of this module.
This question bank includes questions that teams can use to develop a hypothesis about why an individual or group of students may not be responding to an intervention.
This IRIS Star Legacy Module, the second in a series on intensive intervention, offers information on making data-based instructional decisions. Specifically, the resource discusses collecting and analyzing progress monitoring and diagnostic assessment data. Developed in collaboration with the IRIS Center and the CEEDAR Center, this resource is designed for individuals who will be implementing intensive interventions (e.g., special education teachers, reading specialists, interventionists).
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 Innovation Configuration can serve as a foundation for strengthening existing preparation programs so that educators exit with the ability to use various forms of assessment to make data-based educational and instructional decisions within an MTSS. The expectation is that these skills can be further honed and supported through inservice as practicing teachers.
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.