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.
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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.
In this video, Dr. Chris Riley-Tillman a Professor at the University of Missouri and NCII Senior Advisor, discusses the research behind Direct Behavior Rating or DBR and its utility as a progress monitoring measure for behavior.
In this video, John M. Hintze, Professor in the Department of Student Development at the University of Massachusetts Amherst explains why it is important to consider whether an assessment is biased against a specific sub-group.
In this video, Dr. Chris Riley-Tillman, a Professor at the University of Missouri and NCII Senior Advisor, discusses the important considerations when selecting behavioral progress monitoring tools.
In this video, Michelle Hosp, Associate Professor in the College of Education at the University of Massachusetts Amherst discusses why your progress monitoring tool may not directly focus on the skills that you are teaching.
Why do we need to ensure we have multiple parallel or equated forms when measuring student progress?
In this video, Lee Branum-Martin, Associate Professor at Georgia State University explains why we need to ensure we have multiple parallel or equated forms when measuring student progress.
In this video, Dr. Devin Kearns, an Assistant Professor of Special Education in the Department of Education Psychology at the Neag School of Education at the University of Connecticut and NCII Trainer & Coach, discusses things to consider when selecting an academic progress monitoring tool.