This is part 1 of the larger module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part is intended to provide an overview of common general outcome measures (GOM) used for progress monitoring in reading and mathematics, with guidance on selecting an appropriate measure.
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DBI Process
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In this video, Mary Little, Professor and Program Coordinator of the Department of Child, Family, and Community Services at the University of Central Florida discusses why data and data-based decision making such a critical part of instruction and intervention.
In this video, Dr. Rob Horner, Professor of Special Education at the University of Oregon and co-Director of OSEP Technical Assistance Center on PBIS and the OSEP Research and Demonstration Center on School-wide Behavior Support discusses key considerations for developing effective information systems.
In this video, Michele Walden-Doppke, M.A., CAGS, Response to Intervention (RTI) Technical Assistance Provider with Northern Rhode Island Collaborative for Rhode Island Department of Education (RIDE) and NCII Coach in Coventry Public Schools discusses infrastructure elements that support the implementation of intensive intervention.
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 audio story shares New York City's DBI implementation approach, successes, and lessons learned about sustainability
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.
This webinar introduce a series of data teaming tools designed to help facilitators and participants before, during, and after their intervention meeting.
These two self-paced modules address the four practices coaches can use to improve teaching and student learning. Module 1 addresses the four practices coaches can use to improve teaching and student learning. These practices include observation, modeling, providing performance feedback, and using alliance-building strategies. Module 2 addresses how to measure the fidelity of coaching practice to increase the impact it has on teaching and learning. We strongly recommend watching both modules to fully enhance the coaching of teachers. Module 1: Effective Practices for Coaches Module 2: Measuring the Fidelity of Coaching
This interactive self-paced module is intended to help educators and administrators learn about using teaming to support the data-based individualization (DBI) process.