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.
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This online course helps educators learn how to set goals, collect data, and make decisions using academic progress monitoring data.
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.
This IRIS Star Legacy Module, the second in a series on intensive intervention, offers information on making data-based instructional decisions. Specifically, the resource discusses collecting and analyzing progress monitoring and diagnostic assessment data. Developed in collaboration with the IRIS Center and the CEEDAR Center, this resource is designed for individuals who will be implementing intensive interventions (e.g., special education teachers, reading specialists, interventionists).
Office discipline referrals (ODRs) are a data source commonly used by school teams to identify students who need behavioral intervention. In this brief, the National Center on Intensive Intervention (NCII) and the Center on Positive Behavioral Interventions and Supports (PBIS) provide a brief synthesis of the research on using ODRs has a behavioral screener and offer considerations for using ODRs to make data-based decisions.
This webinar challenges current thinking about how to set appropriately ambitious and measurable behavioral goals in light of the 2017 Endrew F. v. Douglas County School District decision by the United States Supreme Court. Dr. Teri A. Marx from the National Center on Intensive Intervention and the PROGRESS Center, as well as Dr. Faith G. Miller from the University of Minnesota—Twin Cities, share how to set ambitious behavioral goals for students by using a valid, reliable progress monitoring measure, and how to write measurable and realistic goals focused on the replacement behavior.
The purpose of this guide is to provide an overview of behavioral progress monitoring and goal setting to inform data-driven decision making within tiered support models and individualized education programs (IEPs).
These five screening one-page documents provide a brief overview of each of the NCII screening standards. They include a definition and information on why that particular standard is important for understanding the quality of screening tools.
This module identifies Tier II and Tier III interventions for students at risk and high risk for behavioral challenges. By the end of this module you should be able to: Describe the decision-making process to indicate Tier II is appropriate Identify critical features of Tier II Discuss how to modify Tier II interventions to meet the needs of more students Highlight critical elements of a Functional Behavior Assessment (FBA) Choose a desired and replacement behavior Complete a Competing Pathway Model Begin to identify strategies to make the problem behavior irrelevant, inefficient, and ineffective
This module discusses how to define, measure and monitor behavior. By the end of the module you should be able to: Select an appropriate target behavior Write an operational definition for a target behavior Identify relevant dimensions of behavior Choose a measurement system based on relevant dimensions of behavior Use graphing conventions to create meaningful visual displays of data