Data-Based Decision-Making: Developing a Method for Capturing Teachers’ Understanding of CBM Graphs

Christine A. Espin, Miya Miura Wayman, Stanley L. Deno, Kristen L McMaster, Mark de Rooij

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

In this special issue, we explore the decision-making aspect of data-based decision-making. The articles in the issue address a wide range of research questions, designs, methods, and analyses, but all focus on data-based decision-making for students with learning difficulties. In this first article, we introduce the topic of data-based decision-making and provide an overview of the special issue. We then describe a small, exploratory study designed to develop a method for studying teachers’ understanding and interpretation of Curriculum-Based Measurement (CBM) graphs. Specifically, we examine whether think-alouds scored for coherence, specificity, reflectivity, and accuracy differentiate teachers with more or less understanding of CBM data. We conclude the article by discussing the importance of, and the need for, research on teachers’ understanding, interpretation, and use of data for instructional decision-making.

Original languageEnglish (US)
Pages (from-to)8-21
Number of pages14
JournalLearning Disabilities Research and Practice
Volume32
Issue number1
DOIs
StatePublished - Feb 1 2017

Bibliographical note

Funding Information:
Research supported by Research Institute on Progress Monitoring (RIPM) (Grant # H324H30003) awarded to the Institute on Community Integration (UCEDD) in collaboration with the Department of Educational Psychology, College of Education and Human Development, at the University of Minnesota, by the Office of Special Education Programs. See progressmonitoring.net.

Publisher Copyright:
© 2017 The Division for Learning Disabilities of the Council for Exceptional Children

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