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.

NCII partnered with Project STAIR (Supporting Teaching of Algebra: Individual Readiness) to host a series of three webinars focused on implementing data-based individualization (DBI) with a focus on mathematics during COVID-19 restrictions.   Webinar 1: Don't Panic, Pivot! Tips for Implementing Data-Based Individualization (DBI) for the Synchronous and Asynchronous Learner    

In this Voices from the Field piece, we talk to Dr. Chrissy Brown, a recent National Center for Leadership in Intensive Intervention (NCLII) scholar. Dr. Brown discusses the NCLII program and how it has guided her in preparing educators to implement intensive interventions. 

    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.  

This is the first module in a series of modules about intensive intervention in reading. There are two parts in this module that answer the questions (1) why is intensive intervention in reading important? and (2) how does data-based individualization (DBI) apply to reading?

The purpose of this guide is to provide an overview of behavioral progress monitoring and goal setting to inform data-driven decision making within tiered support models and individualized education programs (IEPs).

This module provides an overview of diagnostic assessments, using error analysis with CBMs, developing and using curriculum-based assessments (CBAs), and integrating diagnostic and progress monitoring data to inform instructional adaptations.

This collection highlights a sampling of recent research and journal articles focused on intensive intervention and data-based individualization (DBI). As different terms are used to describe intensive intervention, the collection of articles includes those that use various related terms such as precision teaching, data-based decision making (when in the context of providing individualized instruction), Tier 3, intervention adaptation, and individualization. In addition, although there is a wealth of research on key components of the DBI process (e.g., progress monitoring, validated intervention programs), this list is not intended to cover specific steps in the process nor is it an exhaustive review of all available literature. Additional articles and research will be added over time. The resource begins with a list of article citations, beginning with the most recent.

The facilitating ongoing data team meeting documents can assist teams in ensuring that ongoing meetings for students receiving intensive intervention run smoothly. These tools are intended to support teams as they review student progress monitoring data after the initial intervention plan has been put in place and determine whether the student is making progress at an acceptable rate or if adaptations to the intervention plan are necessary. This suite of tools includes a sample agenda, facilitator guide, participant guide, and note taking template.