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 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.
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 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. 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.
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).
The purpose of this document is to provide an overview of the Center’s accomplishments and to highlight a set of lessons learned from the 26 schools that implemented intensive intervention while receiving technical support from the Center.
This brief provides a framework for using Response to Intervention (RTI) with students who are English Language Learners (ELL) from Hispanic backgrounds. It examines the characteristics of these students; defines the RTI process; and then models how students’ linguistic, cultural, and experiential backgrounds can guide appropriate screening, progress monitoring, and goal setting that will help promote English literacy. The brief concludes with a case study that provides specific recommendations for how to apply screening and progress monitoring with ELLs.
Norms for oral reading fluency (ORF) can be used to help educators make decisions about which students might need intervention in reading and to help monitor students’ progress once instruction has begun. This paper describes the origins of the widely used curriculum-based measure of ORF and how the creation and use of ORF norms has evolved over time. Using data from three widely-used commercially available ORF assessments (DIBELS, DIBELS Next, and easyCBM), a new set of compiled ORF norms for grade 1-6 are presented here along with an analysis of how they differ from the norms created in 2006.