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. This document introduces and describes the DBI process and how it can be used to support students who require intensive intervention in academics and/or behavior.
Error message
The page you requested does not exist. For your convenience, a search was performed using the words in the page you tried to access.
Search
Resource Type
DBI Process
Subject
Implementation Guidance and Considerations
Student Population
Audience
Search
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 updated training module provides a rationale for intensive intervention and an overview of data-based individualization (DBI), NCII’s approach to providing intensive intervention. DBI is a research-based process for individualizing validated interventions through the systematic use of assessment data to determine when and how to intensify intervention. Two case studies, one academic and one behavioral, are used to illustrate the process and highlight considerations for implementation.
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.
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
When a student fails to respond to a validated intervention, teams need to identify why the student is not responding to determine how to adapt the intervention. Diagnostic data can assist teams in this process. They may be used to understand a student’s specific skill deficits and strengths or to identify the environmental events that predict and maintain the student’s problem behavior.
For children with the most severe and persistent academic and/or behavioral challenges, parent and family involvement is vital. School teams can use this guide to better understand intensive intervention and how to engage parents and families with the process.
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 (ELs). Teams may use these questions when considering a student’s progress monitoring data, intervention fidelity data, and informal diagnostic data.
This brief offers recommendations to support educators to efficiently collect, analyze, and use diagnostic data to adapt or intensify intervention.
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.