Data-driven Decision Making in the United States

by

Heather Schwartz, Laura Hamilton

• LAST REVIEWED: 28 May 2013
• LAST MODIFIED: 28 May 2013
• DOI: 10.1093/obo/9780199756810-0127

Introduction

In the broadest terms, data-driven decision making (DDDM) in schools refers to the analysis of any form of information—whether it be financial statements; results from student assessments; student, parent, or teacher survey responses; human resources records; or some other data source—to inform choices about school operations, policies, and practices. This article, however, refers more narrowly to the use of student achievement results to make resource allocation decisions such as identifying and rewarding teachers who raise student achievement; or instructional decisions such as the adaption of classroom practices, goals, lessons, and assignments in response to student needs. Data-driven decision making is a popular concept in education reform that can mean many things, but there is little rigorous research to test its efficacy for improving student achievement. Most of the research on DDDM in schools consists of case studies about small numbers of schools or districts or surveys that include larger numbers of participants but that provide only suggestive evidence about how DDDM affects student achievement. Nevertheless, the rapid advances in educational technologies and the significant expansion of school data systems in the United States in particular has enabled new forms and more pervasive use of data in schools. Not surprisingly, educators tend to use data more frequently when it is accessible (as it increasingly becomes via online tools), timely (which is increasingly the case for systems that are synced and refreshed on a daily, weekly, or periodic basis) and perceived as valid. Professional development, staff capacity, and time to analyze and collaborate on responses to obtained results are other crucial ingredients for data use. The citations in this article are selected to familiarize the reader with the most customary ways educators have used student achievement data to make decisions about instruction or resource allocation. It is not an exhaustive list of all DDDM-related issues. However, the citations offer a comprehensive overview of DDDM and should provide a point of entry for the reader’s further research.

General Overviews

The following citations include both articles for researchers and practitioner guides. The What Works Clearinghouse Practice Guide (see Hamilton, et al. 2009) orients the reader in US-based research about the use of student achievement data to inform instruction. Ikemoto and Marsh 2007 provides a theoretical framework and parses the different conceptions of the much-used phrase “data-driven decision making,” and Coburn and Turner 2011 explores both the nature of data use and the broad range of factors that influence it. Marsh, et al. 2006 describes in more detail the types of data that school administrators and teachers tend to use. Kowalski and Lasley 2008 and Mandinach and Honey 2008 offer a combination of research and guidance for practitioners that covers the broader terrain of legal considerations, ethics, necessary support structures for data use, case studies of districts and schools, and uses of technology in the classroom. School leaders looking to develop systems for collecting, synthesizing, and analyzing student performance data can refer to Supovitz and Klein 2003, which offers a framework and detailed vignettes of schools that have done so. Bernhardt 2004 is the most applied and didactic example among these citations in a book intended to educate principals about where to start when determining what data to collect and how to collect and analyze it.

• Bernhardt, Victoria L. 2004. Data analysis for continuous school improvement. 2d ed. Larchmont, NY: Eye on Education.

  A practitioner guide intended to guide school leaders about why to collect data, how to collect it, and how to measure student learning, perceptions of schools, school processes, and how to analyze and communicate the knowledge gleaned from data.

• Coburn, Cynthia E., and Erica O. Turner. 2011. Research on data use: A framework and analysis. Measurement: Interdisciplinary Research & Perspective 9.4: 173–206.

  DOI: 10.1080/15366367.2011.626729

  A framework that explores the mechanisms through which data use might be expected to influence student outcomes. Points out the importance of considering the nature of data-use interventions, the political and organizational contexts in which educators work, and the specific ways in which educators use data.

• Hamilton, Laura, Richard Halverson, Sharnell S. Jackson, et al. 2009. Using student achievement data to support instructional decision making. NCEE 2009-4067. Washington, DC: National Center for Education Evaluation and Regional Assistance.

  Summarizes the best available evidence about data use as identified by a panel of experts. Offers five recommendations about data systems and data use.

• Ikemoto, Gina S., and Julie A. Marsh. 2007. Cutting through the “data-driven” mantra: Different conceptions of data-driven decision making. Yearbook of the National Society for the Study of Education 106.1: 105–131.

  DOI: 10.1111/j.1744-7984.2007.00099.x

  Offers a framework for data-driven decision making (DDDM), outlines four models of DDDM, and asserts that educators most commonly use the simplest types of data to do the simplest types of analysis.

• Kowalski, Theodore J., and Thomas J. Lasley, eds. 2008. Handbook on data-based decision-making in education. New York: Routledge.

  An edited volume on applications of data use in schools, supports needed for educators to use data, and essays that provide an overview of the ethics, legal dimensions, and private sector involvement in data applications.

• Mandinach, Ellen B., and Margaret Honey, eds. 2008. Data driven school improvement: Linking data and learning. New York: Teachers College Press.

  Offers a framework for how data is converted into knowledge and case studies about schools’ use of data. Offers a section on use of technology in classrooms.

• Marsh, Julie A., John F. Pane, and Laura S. Hamilton. 2006. Making sense of data-driven decision making in education: Evidence from recent RAND research. Santa Monica, CA: RAND.

  Examines what types of data school administrators and teachers are using, how are they using them, what support is available to help with the use of the data, and what factors influence the use of data for decision making.

• Supovitz, Jonathan A., and Valerie Klein. 2003. Mapping a course for improved student learning: How innovative schools systematically use student performance data to guide improvement. Philadelphia: Consortium for Policy Research in Education, Univ. of Pennsylvania Graduate School of Education.

  Case studies of five schools identify the ways they used student performance data; asserts the importance of school leaders to start a culture of inquiry in schools.