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A Perfect Time for Data Use: Using Data-Driven Decision Making to Inform Practice
Abstract
Data-driven decision making has become an essential component of educational practice across all levels, from chief state school officers to classroom teachers, and has received unprecedented attention in terms of policy and financial support. It was included as one of the four pillars in the American Recovery and Reinvestment Act (2009), indicating that federal education officials seek to ensure that data and evidence are used to inform policy and practice. This article describes the emergence of data-driven decision making as a topic of interest, some of the challenges to and opportunities for data use, and how the principles of educational psychology can and must be used to inform how educators are using data and the examination of its impact on educational practice.
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Cites
Those Who Understand: Knowledge Growth in Teaching
SHULMAN, L. S. (1986) · Educational Researcher · doi:10.3102/0013189x015002004 · Cited by 18,546
Organizational Learning: A Theory of Action Perspective
Argyris, C., & Schön, D. A. (1997) · Reis · doi:10.2307/40183951 · Cited by 11,113 · cited 1×
“Thisconceptualization is consistent with existing literature from different disciplines (Argyris & Schoen, 1978; Williams & Hummelbrunner, 2011).”
ResultReview of research in education
Shulman, L. S., Mowry, S., & Berliner, D. C. (1973) · F.E. Peacock eBooks · Cited by 7,754
Desimone, L. M. (2009) · Educational Researcher · doi:10.3102/0013189x08331140 · Cited by 4,862
Self-Regulated Learning and Academic Achievement
(1989) · Springer Series in Cognitive Development · doi:10.1007/978-1-4612-3618-4 · Cited by 1,881
Desimone, L. M., Porter, A. C., Garet, M. S., Yoon, K. S., & Birman, B. F. (2002) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737024002081 · Cited by 1,688
Self-regulation of learning and performance: Issues and educational applications.
Schunk, D. H., & Zimmerman, B. J. (1994) · Cited by 1,492
Self-regulated learning : from teaching to self-reflective practice
Schunk, D. H., & Zimmerman, B. J. (1998) · Cited by 1,426
Self-Regulated Learning and Academic Achievement
(2013) · doi:10.4324/9781410601032 · Cited by 1,325
Assessment Crisis: The Absence of Assessment for Learning
Stiggins, R. J. (2002) · Phi Delta Kappan · doi:10.1177/003172170208301010 · Cited by 866
Research works printed but not linked: 48
- American Educational Research Association, 2003, Resolution on the essential elements of scientifically-based research
- Armstrong J., 2001, American School Board Journal, 188, 38
- Choppin J., 2002, Data use in practice: Examples from the school level
- Data Quality Campaign, 2010, DQC 2009–2010 annual survey results: Action 9
- Data Quality Campaign, 2011, Data for action 2010: Action 9
- Data Quality Campaign, 2012, State analysis by action: Promote educator professional development and credentialing
- Datnow A., 2007, Achieving with data: How high-performing school systems use data to improve instruction for elementary students
- Dillon S., 2010, The New York Times
- Duncan A., 2009, Federal leadership to support state longitudinal data systems
- Duncan A., 2009, Secretary Arne Duncan addresses the Fourth Annual IES Research Conference
- Duncan A., 2009, Testimony before the House Education and Labor Committee
- Duncan A., 2010, Keynote address
- Duncan A., 2010, Secretary Arne Duncan's remarks to National Council for Accreditation of Teacher Education
- Duncan A., 2012, Leading education into the information age: Improving student outcomes with data
- Easton J. Q., 2009, Using data systems to drive school improvement
- Feldman J., 2001, ERS Spectrum, 19, 10
- Heritage M., 2007, Formative assessment
- Heritage M., 2010, Formative assessment and next-generation assessment systems: Are we losing an opportunity?
- Herman J., 2001, Lessons learned in using data to support school inquiry and continuous improvement: Final report to the Stuart Foundation
- Hupert N., 2008, Data-driven school improvement: Linking data and learning, 130
- Institute of Education Sciences, 2011, WWC procedures and standards handbook
- Long L., 2008, Data-driven school improvement: Linking data and learning, 209
- Mandinach E. B., 2009, Data use: What we know about school-level use
- Mandinach E. B., 2009, How LEAs use data to inform practice: The opportunities for and challenges to use in schools and districts
- Mandinach E. B., 2010, On data-driven decision making
- Mandinach E. B., 2010, Using data-driven decision making to inform practice: From research to practice
- Mandinach E. B., 2010, An examination of what schools of education are doing to improve human capacity regarding data
- Mandinach E. B., 2011, The complexities of integrating data-driven decision making into course offerings at schools of education
- Mandinach E. B., 2011, Defining data literacy in educators: What is essential and what is missing
- Mandinach E. B., 2012, Data literacy: What educators need to know, how it is defined, and the next steps to improving educators’ capacity to use data
- Mandinach E. B., 2008, . Data-driven school improvement: Linking data and learning.
- Mandinach , E. B. Honey , M. Light , D. and Brunner , C. 2008. A conceptual framework for data-driven decision making. , Edited by: Mandinach , E. B. and Honey , M. 13–31. New York, NY: Teachers College Press. Data-driven school improvement: Linking data and learning
- Mandinach E. B., 2010, District level use of data
- Mandinach E. B., 2011, Leveraging the power of state longitudinal data systems: Building capacity to turn data into useful information
- Mann B., 2010, Teaching teachers to use data
- NCES, 2010, 20 states win grants for longitudinal data systems
- NCES, 2011, Grants for statewide longitudinal data systems
- Petrides L. A., 2006, THE Journal
- Schneider M., 2009, The Education Gadfly, 9
- Schunk D. H., 1994, Self-regulation and performance: Issues and educational applications
- Smith N., 2009, Data accuracy in longitudinal data systems
- Spellings M. A., 2005, Seeing the data, meeting the challenge
- U. S. Department of Education, 2010, Race to the Top Assessment program
- Wayman J. C., 2005, Journal of Education for Students Placed at Risk, 10
- Wayman J. C., 2007, Educational Technology Research Symposium: Vol. 1, 156
- Wayman J. C., 2007, The data-informed district: A district-wide evaluation of data use in the Natrona County School District
- White S. H., 2005, Beyond the numbers: Making data work for teachers and school leaders
- Whitehurst G. J., 2003, The Institute for Education Sciences: New wine and new bottles
Cited by
A Systemic View of Implementing Data Literacy in Educator Preparation
Mandinach, E. B., & Gummer, E. S. (2013) · Educational Researcher · doi:10.3102/0013189x12459803 · Cited by 492
Self-citationMandinach, E. B., & Gummer, E. S. (2016) · Teaching and Teacher Education · doi:10.1016/j.tate.2016.07.011 · Cited by 363
Self-citationMhlongo, S., Mbatha, K., Ramatsetse, B., & Dlamini, R. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16348 · Cited by 337
Marsh, J. A., & Farrell, C. C. (2015) · Educational Management Administration & Leadership · doi:10.1177/1741143214537229 · Cited by 233
Teachers’ Sensemaking of Data and Implications for Equity
Bertrand, M., & Marsh, J. A. (2015) · American Educational Research Journal · doi:10.3102/0002831215599251 · Cited by 209
Metadata
- Authors
- Mandinach, E. B.
- Venue
- Educational Psychologist
- Publisher
- Informa UK Limited
- Published
- 2012-04
- Volume
- 47
- Issue
- 2
- Pages
- 71-85
- ISSN
- 0046-1520 (ISSN-L)
- 1532-6985
- OpenAlex
- W2136737278
- Cited by
- 465
- References
- 50
- FWCI
- 37.64
- Type
- journal article
- Language
- en
- Open access
- closed
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