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).
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In this webinar panelists discuss strategies and frameworks to ensure educators are data literate and understand how data literacy can help districts and schools address learning opportunity gaps.
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
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 presentation provides an overview of the Direct Behavior Rating (DBR). DBR is a method for collecting data on student behavior that merges a rating scale approach and direct observation. The presentation describes: (a) considerations before using the DBR, (b) completing the DBR and (c) using the DBR to monitor progress and evaluate behavior.
With the closure of schools due to the COVID-19 pandemic, educators and administrators need to rethink how they collect and analyze progress monitoring data in a virtual setting. This collection of frequently asked questions is intended to provide a starting place for consideration.
NCII, through a collaboration with the University of Connecticut, developed a set of course content focused on developing educators’ skills in designing and delivering intensive mathematics instruction. This content is designed to support faculty and professional development providers with instructing pre-service and in-service educators who are developing and/or refining their implementation of intensive mathematics intervention
In this webinar, Dr. Sarah Powell an Associate Professor in the Department of Special Education at the University of Texas at Austin highlights freely available tools and resources that can help educators consider a scope and sequence for math skills, assessment and intervention practices, instructional delivery, concepts and procedures for whole and rational numbers, intensification considerations, and more. The webinar reviews the content available from the Intensive Intervention Math Course Content. The course content consists of eight modules covering a range of math related topics. Each module includes video lessons, activities, knowledge checks, practice-based opportunities, coaching materials and other resources.
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 is part 2 of the module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part includes examples of graphed data and is intended to provide participants with guidance for reviewing progress monitoring data to determine if the instructional plan is working or if a change is needed.
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