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.
The facilitating ongoing data team meeting documents can assist teams in ensuring that ongoing meetings for students receiving intensive intervention run smoothly. These tools are intended to support teams as they review student progress monitoring data after the initial intervention plan has been put in place and determine whether the student is making progress at an acceptable rate or if adaptations to the intervention plan are necessary. This suite of tools includes a sample agenda, facilitator guide, participant guide, and note taking template.
The initial data team meeting documents can assist teams in facilitating an efficient and effective process for analyzing data and designing intensive intervention plans for students.
Before a student is referred for intensive intervention, it is important that the team get a holistic sense of the student, including relevant background information, current performance, current supports and previously attempted intervention(s), and other relevant data. These data meeting tools focused on preparing for the meeting ensure that team members are prepared to discuss students.
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 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.
This training module, includes four sections that (a) provide an overview of administering common general outcome measures for progress monitoring in reading and mathematics, (b) review graphed progress monitoring data, and (c) provide guidance on identifying what type of skills the intervention should target to be most effective in reading and mathematics.
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 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.