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.
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Progress monitoring is an essential part of a multi-tiered system of supports (MTSS) and, specifically, the data-based individualization (DBI) process. It allows educators and administrators to understand whether students are responding to intervention and if adaptations are needed. In addition, these data are often used to set high-quality academic and behavioral goals within the individualized education program (IEP) for students with disabilities. 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.
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).
The 2017 Supreme Court decision Endrew F. v. Douglas County School District highlighted the importance of monitoring students’ progress toward appropriately challenging individualized educational program (IEP) annual goals and making changes to students’ educational programs when needed. In this guide, we explain how educators can establish IEP goals that are measurable, ambitious, and appropriate in light of the student's circumstances.
The Behavior Progress Monitoring Tools Chart is comprised of evidence-based progress monitoring tools that can be used to assess students’ social, emotional or behavioral performance, to quantify a student rate of improvement or responsiveness to instruction, and to evaluate the effectiveness of instruction. The chart displays ratings on technical rigor of performance level standards (reliability and validity) and growth standards (sensitivity and decision rules) and provides information on the whether a bias analysis was conducted, and key usability features. The chart is intended to assist educators and families in becoming informed consumers who can select behavior progress monitoring tools that address their specific needs. The presence of a particular tool on the chart does not constitute endorsement and should not be viewed as a recommendation from either the TRC on Behavior Progress Monitoring or NCII.
The Academic Progress Monitoring Tools Chart is comprised of evidence-based progress monitoring tools that can be used to assess students’ academic performance, to quantify a student rate of improvement or responsiveness to instruction, and to evaluate the effectiveness of instruction. The chart displays ratings on technical rigor of performance level standards (reliability and validity) and growth standards (sensitivity, alternate forms, and decision rules) and provides information on the whether a bias analysis was conducted, and key usability features. The chart is intended to assist educators and families in becoming informed consumers who can select academic progress monitoring tools that address their specific needs. The presence of a particular tool on the chart does not constitute endorsement and should not be viewed as a recommendation from either the TRC on Academic Progress Monitoring or NCII.
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. This document introduces and describes the DBI process and how it can be used to support students who require intensive intervention in academics and/or behavior.
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.
These two modules from the IRIS Center introduce users to progress monitoring in reading and mathematics. Progress monitoring is a type of formative assessment in which student learning is evaluated to provide useful feedback about performance to both learners and teachers. Because the overall progress monitoring process is almost identical for any subject area, the content in the two modules is very similar.
Norms for oral reading fluency (ORF) can be used to help educators make decisions about which students might need intervention in reading and to help monitor students’ progress once instruction has begun. This paper describes the origins of the widely used curriculum-based measure of ORF and how the creation and use of ORF norms has evolved over time. Using data from three widely-used commercially available ORF assessments (DIBELS, DIBELS Next, and easyCBM), a new set of compiled ORF norms for grade 1-6 are presented here along with an analysis of how they differ from the norms created in 2006.