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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DBI Process
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Implementation Guidance and Considerations
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NCII, through a collaboration with the University of Connecticut, developed a set of course modules focused on developing educators’ skills in using explicit instruction. These course modules are 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 explicit instruction.
This training module demonstrates how academic progress monitoring fits into the Data-Based Individualization (DBI) process by (a) providing approaches and tools for academic progress monitoring and (b) showing how to use progress monitoring data to set ambitious goals, make instructional decisions, and plan programs for individual students with intensive needs.
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 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.
Progress monitoring, a key component of a multi-tiered system of support (MTSS), occurs throughout the data-based individualization (DBI) process to assess responsiveness to the validated intervention platform, as well as adaptations to the intervention. Prior to delivering the validated intervention platform, intervention teams should develop a progress monitoring plan that outlines the progress monitoring tool, student goal, and frequency of data collection and review. During delivery of the validated and adapted intervention, educators should collect and graph frequent progress monitoring data.
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.
This webinar illustrates considerations for implementing data-based individualization (DBI) with English Learners that accounts for their unique academic, social, behavioral, linguistic, and cultural experiences, assets, and needs.
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.
Monitoring Student Progress for Behavioral Interventions (DBI Professional Learning Series Module 3)
This module focuses on behavioral progress monitoring within the context of the DBI process and addresses: (a) methods available for behavioral progress monitoring, including but not limited to Direct Behavior Rating (DBR), and (b) using progress monitoring data to make decisions about behavioral interventions.