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.
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DBI Process
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Implementation Guidance and Considerations
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Using DBI to Improve Literacy Outcomes for Students with Intellectual and Developmental Disabilities
This webinar provides an overview of a project focused on increasing literacy outcomes using DBI, inclusion, and enhancing individualized education programs.
This webinar discusses strategies to help teacher education faculty integrate the principles of intensive intervention within undergraduate and graduate-level programs for aspiring and current teachers.
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.
This webinar shared an overview of how social emotional learning (SEL) relates to intensive intervention and offer sample strategies and resources for building social and emotional competencies for students in need of intensive learning, social, emotional, or behavioral supports.
This question bank includes questions that teams can use to develop a hypothesis about why an individual or group of students may not be responding to an intervention. The hypothesis should help guide intervention planning and selection of intensification strategies using the Intervention Intensification Strategy Checklist. When developing a hypothesis, teams should consider the intervention design, fidelity of implementation, and learner needs. Intervention fidelity data collected using the Student Intervention Implementation Log and informal diagnostic data may help teams answer the questions included in the question bank.
The pandemic has disrupted and, in many cases hindered, learning for all students – most particularly for our most vulnerable populations. Data literacy is key to understanding and tailoring instructional decisions to address students’ varying needs. In this webinar panelists discuss strategies and frameworks to ensure educators are data literate and understand how data literacy can help districts and schools address learning opportunity gaps.
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.
Teams are a vital part of an effective multi-tiered system of supports (MTSS) across both academics and behavior as well as special education. Making connections across the across the various teams used in MTSS and special education can be challenging. This resource from NCII and the PBIS Center, provides information about how DBI can support IEP implementation and provides a table with key considerations for teams working across the MTSS system.
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: