The purpose of this training is to gain foundational knowledge of how all behavior serves a purpose or function. This foundational knowledge is core to understanding behavior, supporting students with challenging behavior, and diagnosing the function of behavior and developing effective behavioral interventions. This module introduces function of behavior and provides suggestions for how you can use this understanding within the context of a data-based individualization (DBI) process. While this module briefly mentions the role of a Functional Behavioral Assessment (FBA), this is not the focus of this module.
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This module provides an overview of diagnostic assessments, using error analysis with CBMs, developing and using curriculum-based assessments (CBAs), and integrating diagnostic and progress monitoring data to inform instructional adaptations.
Diagnostic tools provide data to assist educators in designing individualized instruction and intensifying intervention for students who do not respond to validated intervention programs. Diagnostic tools can be either informal, which are easy-to-use tools that can be administered with little training, or standardized, which must be delivered in a standard way by trained staff. Teams may find it helpful to initially consider using more informal and easily accessible diagnostic tools and data to avoid loss of instructional time. Standardized diagnostic tools, which require more time to administer and interpret, may be required for students who continually demonstrate a lack of response or who require special education.
This is part 4 of the module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part of the module is intended to provide participants with guidance for identifying skills to target in reading and math interventions.
When a student fails to respond to a validated intervention, teams need to identify why the student is not responding to determine how to adapt the intervention. Diagnostic data can assist teams in this process. They may be used to understand a student’s specific skill deficits and strengths or to identify the environmental events that predict and maintain the student’s problem behavior.
This is part 3 of the larger module, “Informal Academic Diagnostic Assessment: Using Data to Guide Intensive Instruction.” This part is intended to provide participants with an introduction to error analysis of curriculum-based measures for the purpose of identifying skill deficits and providing examples of error analysis in reading and mathematics. Part 4, “Identifying Target Skills,” will further link these skill deficits to intervention.
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.
This module serves as an introduction to important concepts and processes for implementing functional behavior assessment (FBA), including behavior basics such as reinforcement and punishment. Throughout this module, participants will discuss both real world and school based examples to become familiar with the FBA process and develop a deeper understanding and awareness of the functions of the behavior. Key topics include (a) defining FBAs in the context of DBI; (b) basic concepts in behavior, including antecedents, behaviors, and consequences; (c) levels of FBAs; and (d) considerations and procedures for conducting FBAs.
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.
Successful implementation of a multi-tiered system of supports (MTSS) and, specifically, intensive intervention through the data-based individualization (DBI) process, demands the collection and analysis of data. As teams consider data collection, challenges may occur with assessment administration, scoring, and data entry (Taylor, 2009). This resource reviews three data collection and entry challenges and strategies to ensure data about risk status and responsiveness accurately represent student performance and minimize measurement errors.