In this video, Dr. Lynn Fuchs, Nicholas Hobbs Professor of Special Education and Human Development at Vanderbilt University and Senior Advisor to the National Center on Intensive Intervention, shares considerations for adapting interventions when the validated intervention program wasn’t successful.
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DBI Process
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Implementation Guidance and Considerations
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This four-part webinar series is focused on the Taxonomy of Intervention Intensity. This series provides an overview of the dimensions of the Taxonomy of Intervention Intensity and case applications showing how the taxonomy can be used to guide the intensification of reading, mathematics, and behavior interventions.
This webinar demonstrates how the Taxonomy of Intervention Intensity can support educators in systematically selecting and modifying intensive behavior intervention based on student need.
This webinar introduces the Taxonomy of Intervention Intensity as a method for systematically selecting an intensive intervention and guide teachers through modifying the intervention based on student need.
This webinar demonstrates how the Taxonomy of Intervention Intensity can support educators in systematically selecting and modifying intensive literacy interventions based on student need.
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:
This webinar introduces the Taxonomy of Intervention Intensity as a method for systematically selecting an intensive intervention and guide teachers through modifying the intervention based on student need.
This webinar describes contextual factors that can support or impede the implementation of intensive intervention.
This video illustrates the use of manipulatives to help students integrate the concept of counting by ones with skill in grouping by tens.
This video demonstrates how to use base-10 blocks to help students solve multiplication problems that cannot be solved with automatic retrieval.