This webinar reviews keys recommendations and lessons learned to help school and district leaders establish the conditions needed for educators to successfully implement data-based individualization (DBI) for students with the most intensive needs
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DBI Process
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Implementation Guidance and Considerations
Student Population
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This three-part course provides a guide to available NCII self-paced learning courses that focus on academic progress monitoring. The collection begins with an overview of progress monitoring and the role of progress monitoring within the DBI process. The second module focuses defining two types of academic progress monitoring measures (general outcome measures and mastery measures) and considerations for identifying a target behavior and selecting a valid and reliable academic progress monitoring tool. The final module focuses on how you collect, graph, and make decisions based on academic progress monitoring data. While it is possible to take the courses individually or in a different order, this collection provides a suggested order for engaging in learning about academic progress monitoring.
This course collection provides a guide to available NCII courses for those who are newer to the DBI process or interested in learning more about how intensive intervention can support students with severe and persistent learning and/or social, emotional, or behavioral needs.
This 4-part self-paced course reviews an explicit instruction model and the supporting practices required for effective implementation.
In this Voices from the Field post, Emma Shanahan reflects on her experiences with progress monitoring and data-based decision making as a teacher and shares findings from her recent research on DBI professional development.
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.
Using DBI to Improve Literacy Outcomes for Students with Intellectual and Developmental Disabilities
How can data-based individualization (DBI) help educators to address the growing expectations for literacy outcomes for students with intellectual and developmental disabilities? In this webinar, Dr. Chris Lemons an NCII Advisor, Associate Professor of Special Education in the Graduate School of Education at Stanford University, and Co-Director of the Stanford Down Syndrome Research Center, provides an overview of activities conducted through an Office of Special Education Programs model demonstration project. This project focused on increased literacy outcomes using DBI, inclusion, and enhancing individualized education programs. The webinar shares project findings and provides recommendations for integrating those findings into professional development and practice to improve student outcomes.
This online course helps educators learn how to set goals, collect data, and make decisions using academic progress monitoring data.
In this webinar, experts from the PROGRESS Center and National Center on Intensive Intervention (NCII) model how practitioners can use data-based individualization (DBI) to develop and implement SDI for students with disabilities and a panel of special educators share how using DBI improved the efficiency and effectiveness of their service delivery, communication with families, and collaboration with other educators.
This course is the second in a series on progress monitoring. This module describes two types of academic progress monitoring measures and considerations for selecting an academic progress monitoring tool.