This document addresses five guiding questions for educators to consider when reviewing and interpreting assessment data for English Learners and includes links to selected resources.
Search
Resource Type
DBI Process
Subject
Implementation Guidance and Considerations
Student Population
Audience
Event Type
Search
NCII developed this resource to help educators better understand the purpose of and considerations surrounding behavior screening in schools. Educators can use the information on this resource in conjunction with the Behavior Screening Tools Chart to (a) design a screening process for their school and (b) select or evaluate screening tools.
This handout briefly defines the seven dimensions of the Taxonomy of Intervention Intensity for academics and behavior. The Taxonomy of Intervention Intensity was developed based on research to support educators in evaluating and building intervention intensity. The seven dimensions include strength, dosage, alignment, attention to transfer, comprehensiveness, behavior or academic support, and individualization.
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.
This two page handout highlights how to use the Taxonomy of Intervention Intensity when selecting, evaluating, and intensifying interventions for students who are English learners (ELs). Specific considerations for ELs are provided across the dimensions of strength, dosage, alignment. attention to transfer, comprehensiveness, behavioral support, and individualization.
Office discipline referrals (ODRs) are a data source commonly used by school teams to identify students who need behavioral intervention. In this brief, the National Center on Intensive Intervention (NCII) and the Center on Positive Behavioral Interventions and Supports (PBIS) provide a brief synthesis of the research on using ODRs has a behavioral screener and offer considerations for using ODRs to make data-based decisions.
This rubric uses descriptors of the dimensions of the Taxonomy of Intervention Intensity to support teams in selecting and evaluating validated interventions for small groups or individual students. Teams may consider using data available on the National Center on Intensive Intervention Academic Tools Chart and the publishers’ websites as well as results from previous implementation efforts. Each dimension will be rated on a scale of 0– Fails to Address Standard to 3 – Addresses Standard Well. Taxonomy of Intervention Intensity: Academic Rating Rubric Related Resources Taxonomy of Intervention Intensity Resources
This guide is a set of strategies and key practices with the ultimate goal of supporting students with the most intensive behavioral needs, their families, and educators in their transitions back to school during and following the global pandemic in a manner that prioritizes their health and safety, social and emotional needs, and behavioral and academic growth.
This resource developed by Sarah Thorud, Elementary Reading Specialist from Clatskanie School District in Oregon focuses on implementing screening and progress monitoring virtually. It includes guiding questions and considerations for implementation, video examples, and a sample sign-up sheet for screening and progress monitoring students virtually.
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.