To support English Learners (ELs) with intensive intervention needs it is important to (a) deliver instruction that represents culturally and linguistically sustaining best practices, and (b) distinguish the needs and assets of learners to improve progress (i.e., second-language acquisition, culture, learning challenges). This brief illustrates considerations for implementing data-based individualization (DBI) with ELs that accounts for their unique academic, social, behavioral, linguistic, and cultural experiences, assets, and needs.
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Special education teachers must have the skills to design and deliver intensive interventions for students with severe and persistent learning and behavioral needs. To ensure effective instruction for these students, preservice preparation programs must provide their teacher candidates with opportunities to learn, apply, and practice intensive intervention skills. Teacher preparation faculty play a critical role in ensuring the next generation of teachers have these opportunities.
This module is intended to help educators and administrators understand the dimensions of the Taxonomy of Intervention Intensity and how it can be used to select, evaluate, and intensify interventions.
This module is intended to help educators and administrators to dive deeper into the steps of the data-based individualization (DBI) process for individualizing and intensifying interventions.
This module provides the foundational information for users interested in learning more about intensive intervention and the DBI process. The module defines intensive intervention and DBI, describes how intensive intervention fits within a tiered system such as MTSS, RTI, or PBIS, demonstrates how intensive intervention can provide a systemic process to deliver specialized instruction for students with disabilities, and provides two case examples to allow viewers to apply new knowledge.
This IRIS Star Legacy Module, first in a series of two, overviews data-based individualization and provides information about adaptations for intensifying and individualizing instruction. 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).
This collection highlights a sampling of articles focused on intensive intervention and data-based individualization (DBI). 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 include articles that focus on specific steps in the DBI process, nor is it an exhaustive review of all available literature. In the list below, we highlight seminal research on DBI and articles published since 2011, when NCII was first funded.
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. The DBI process includes five iterative steps:
The DBI Implementation Rubric and the DBI Implementation Interview are intended to support monitoring of school-level implementation of data-based individualization (DBI). The rubric is based on the structure of the Center on Response to Intervention’s Integrity Rubric and is aligned with the essential components of DBI and the infrastructure that is necessary for successful implementation in Grades K–6. It describes levels of implementation on a 1–5 scale across DBI components. The rubric is accompanied by the DBI Implementation Interview which includes guiding questions that may be used for a self-assessment or structured interview of a school’s DBI leadership team.
This white paper summarizes the proceedings of a summit that was focused on integrating research knowledge on promising approaches into intensive intervention and implementation to improve academic outcomes for students with disabilities who have severe and persistent learning need. In addition, it includes responses from three participants representing perspectives from policy (David Chard, Wheelock College), research (Nathan Clemens, University of Texas at Austin), and practice (Steve Goodman, Michigan Integrated Behavior and Learning Support Initiative).