Science teachers’ data use practices: A descriptive analysis

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Abstract

There is a debate on students’ low science achievement in the United States, particularly among low income, African American students, and Latino students. An important part of the education community’s response to low achievement generally and in science specifically has been the implementation of high stakes accountability policies. Because of accountability’s emphasis on educator data use, much research has examined different facets of it throughout educational organizations, but research has not focused on the extent to which data use might be content-specific. The purpose of this paper, then, was to investigate the data use practices of science teachers. Drawing from a broader study of science teachers in grades 5-8 across six school districts, this study reports results from teacher surveys and interviews. The findings indicate that while there were examples of science-specific data use, most of the science teachers used data in ways consistent with previous content-agnostic research. Implications for future research, policy, and practice are discussed.

 

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How to Cite
Snodgrass Rangel, V., Monroy, C., & Bell, E. (2016). Science teachers’ data use practices: A descriptive analysis. Education Policy Analysis Archives, 24, 86. https://doi.org/10.14507/epaa.24.2348
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Author Biographies

Virginia Snodgrass Rangel, University of Houston

Dr. Snodgrass Rangel is an Assistant Professor in the Department of Educational Leadership and Policy Studies at the University of Houston. Her research interests include teacher leadership, data use policy and practice, policy implementation, school reform, and STEM education. Her work has appeared in the American Educational Research Journal, Educational Policy, and the Journal of School Leadership.

Carlos Monroy, Rice University

Dr. Monroy is a Research Scientist in the Computer Science Department at Rice University. He works with the Pliny Project, which seeks to gather hundreds of billions of lines of publicly available open-source computer code, and to mine that code to create a searchable database of properties, behaviors, and vulnerabilities. His research interests include data mining, information retrieval and visualization, and learning analytics. The goal of his work is to harness data science methods for fostering innovation, create meaningful contexts, and improve knowledge discovery.

Elizabeth Bell, Accelerate Learning Inc.

Dr. Bell is the Research Manager at Accelerate Learning Inc. Her research interests include social and cognitive development as it applies to early childhood education as well as the implementation and impact of science curriculum in diverse classrooms.