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.
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Monitoring Student Progress for Behavioral Interventions (DBI Professional Learning Series Module 3)
This brief reviews provides considerations for creating readiness to implement DBI to support successful implementation and scale-up in schools.
When a student fails to respond to a validated intervention, teams need to identify why the student is not responding to determine how to adapt the intervention. Diagnostic data can assist teams in this process. They may be used to understand a student’s specific skill deficits and strengths or to identify the environmental events that predict and maintain the student’s problem behavior.
Fidelity refers to how closely prescribed procedures are followed and, in the context of schools, the degree to which educators implement programs, assessments, and implementation plans the way they were intended. When we implement interventions and assessments with fidelity, intervention teams can make more accurate decisions about an individual student’s progress and future intervention needs. In addition, fidelity of implementation to the data-based individualization (DBI) process as a whole and across multiple students in a school, helps to ensure that staff have the necessary resources and processes in place to support strong implementation for individual students. The following tools assess and support fidelity of DBI implementation at the school, interventionist, and student levels.
This tool is designed to help educators collect and graph academic progress monitoring data across multiple measures as a part of the data-based individualization (DBI) process. This tool allows educators to store data for multiple students (across multiple measures), graph student progress, and set individualized goals for a student on specific measures.
During fall 2020, educators provided virtual, in-person, and hybrid intervention with an ongoing need to engage with and support parents and families. Although the context and environment may have changed, the focus on providing high-quality interventions with validated practices, monitoring student progress, and adapting and intensifying supports based on student data as outlined in the data-based individualization (DBI) process continues to be applicable across virtual, in-person, or hybrid models. This document presents considerations for implementing DBI in light of COVID-19 with an emphasis on delivery in virtual settings.
This checklist can be used by intervention providers or planning teams to review, document, and improve implementation of the data-based individualization (DBI) process and monitor whether the student intervention plans were implemented as intended.
Using multiple data sources, the teacher or team makes a decision to adapt the intervention program to better meet the student’s individual needs. The teacher or team outlines these adaptations in an individual student plan. The plan may include adaptation strategies along several dimensions. These strategies may include quantitative changes, such as providing more opportunities for a student to respond by increasing the length or frequency of the intervention, or decreasing the size of the intervention group.
The Colorado Department of Education (CDE) has been working closely with NCII to align and scale up use of data-based individualization (DBI) across the state. One of the strategies CDE has used is the development of virtual learning resources and online learning modules on DBI to help make professional learning accessible to all educators. In this Voices from the Field video, Dr. Jason Harlacher and Veronica Fielder share CDE’s process for developing virtual learning modules on DBI and their strategies for ensuring the modules are accessible to educators.
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.