This checklist can be used by intervention providers or planning teams to review, document, and improve implementation of the data-based individualization (DBI) process and monitor whether the student intervention plans were implemented as intended.
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The purpose of this module is to introduce schools interested in implementing intensive intervention to the infrastructure needed to implement data-based individualization (DBI). The module includes presentation slides with integrated activities and handouts to help teams determine their readiness and develop an action plan for implementation.
NCII, through a collaboration with the University of Connecticut, developed a set of course content focused on developing educators’ skills in designing and delivering intensive reading instruction. This content is designed to support faculty and professional development providers with instructing pre-service and in-service educators who are developing and/or refining their implementation of intensive reading intervention.
NCII, through a collaboration with the University of Connecticut, developed a set of course content focused on developing educators’ skills in designing and delivering intensive mathematics instruction. This content is designed to support faculty and professional development providers with instructing pre-service and in-service educators who are developing and/or refining their implementation of intensive mathematics intervention
In this Voices from the Field post, Emma Shanahan reflects on her experiences with progress monitoring and data-based decision making as a teacher and shares findings from her recent research on DBI professional development.
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.
Data teams serve multiple roles in the data-based individualization (DBI) process and across a multi-tiered system of supports (MTSS). Although schools may have multiple teams that review different types of data across a multi-tiered system of supports (MTSS), the intensive intervention or DBI team is focused on the needs of individual students who are not making progress in their current intervention or special education program. It is critical that these meetings are driven by data, occur regularly, and use an efficient, consistent process that allows participants to review progress and make intervention decisions for students. NCII has created a series of tools to help teams establish efficient and effective individual student data meetings.
NCII, through a collaboration with the University of Connecticut, developed a set of course content focused on developing educators’ skills in designing and delivering behavior support in intensive intervention. This content is designed to support faculty and professional development providers with instructing pre-service and in-service educators who are developing and/or refining their implementation of behavior support in intensive intervention.
This series of lessons from the State Implementation and Scaling-up of Evidence-based Practices (SISEP) Center highlights a research driven approach to coaching. The series begins with an overview lesson and includes additional lessons that cover how to deliver effective and efficient prompting, performance feedback, and scaffolding and using data to identify recipients current phase of learning. A coaching practice profile is also available.
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.