Journal of Educational Change

, Volume 19, Issue 2, pp 131–152 | Cite as

Opening or closing doors for students? Equity and data use in schools

  • Amanda DatnowEmail author
  • Vicki Park


Ensuring equitable opportunities and outcomes for all students is a top priority of many educators and policymakers across the globe. Data use can be an important lever for achieving equity, but how this can occur is not well understood. In this article, we draw upon knowledge gained in a decade of in-depth qualitative research to examine the ways in which data use helps to open or close doors for students. We discuss data use practices that influence equity goals: (1) accountability-driven data use and data use for continuous improvement; (2) using data to confirm assumptions and using data to challenge beliefs, and (3) tracking and flexible grouping to promote student growth. Along each of these dimensions, there are active decision makers, complex processes of data use at play, and a great deal of variation both within and across contexts. Ultimately, educators and policymakers are faced with critical choices regarding data use that can profoundly affect students’ daily educational experiences and trajectories.


Data use Equity Accountability 



We gratefully acknowledge the Spencer Foundation’s support of our recent research on data use and instructional differentiation. We also wish to thank New Schools Venture Fund for their support of our early studies of data use in 2006–2008.


  1. Bertrand, M., & Marsh, J. A. (2015). Teachers’ sensemaking of data and implications for equity. American Educational Research Journal, 52(5), 861–893.CrossRefGoogle Scholar
  2. Booher-Jennings, J. (2005). Below the bubble: “Educational triage” and the Texas accountability system. American Educational Research Journal, 42(2), 231–268.CrossRefGoogle Scholar
  3. Braaten, M., Bradford, C., Kirchgasler, K. L., & Baracos, S. (2017). How data use for accountability undermines equitable science education. Journal of Educational Administration, 55(4), 427–446.CrossRefGoogle Scholar
  4. Christman, J. B., Neild, R. C., Bulkley, K., Blanc, S., Liu, R., Mitchell, C., & Travers, E. (2009). Making the most of interim assessment data. Lessons from Philadelphia. Retrieved on April 24, 2018 from
  5. Coburn, C., & Talbert, J. (2006). Conceptions of evidence use in school districts: Mapping the terrain. American Journal of Education, 112(4), 469–495.CrossRefGoogle Scholar
  6. Coburn, C. E., & Turner, E. O. (2011). Research on data use: A framework and analysis. Measurement: Interdisciplinary Research and Perspectives, 9(4), 173–206.Google Scholar
  7. Daly, A. J. (2012). Data, dyads, and dynamics: Exploring data use and social networks in educational improvement. Teachers College Record, 114(11), 110305.Google Scholar
  8. Datnow, A., Choi, B., Park, V., & St. John, E. (2018). Teacher talk about student ability and achievement in the era of data-driven decision making. Teachers College Record, 120(4), 1–34.Google Scholar
  9. Datnow, A., & Park, V. (2009). School system strategies for supporting data. In T. Kowalski & T. Lasley (Eds.), Handbook of data-based decision making for education (pp. 191–206). New York: Routledge.Google Scholar
  10. Datnow, A., & Park, V. (2014). Data-driven leadership. San Francisco: Jossey Bass.Google Scholar
  11. Datnow, A., Park, V., & Kennedy, B. (2008). Acting on data: How urban high schools use data to inform instruction. Los Angeles, CA: Center on Educational Governance, USC Rossier School of Education.Google Scholar
  12. Datnow, A., Park, V., & Kennedy-Lewis, B. (2012). High school teachers’ use of data to inform instruction. Journal of Education for Students Placed At Risk, 17, 247–265.CrossRefGoogle Scholar
  13. Datnow, A., Park, V., & Kennedy-Lewis, B. (2013). Affordances and constraints in the context of teacher collaboration for the purpose of data use. Journal of Educational Administration, 51(3), 341–362.CrossRefGoogle Scholar
  14. Datnow, A., Park, V., & Wohlstetter, P. (2007). Achieving with data: How high performing districts use data to improve instruction for elementary school students. Los Angeles, CA: Center on Educational Governance, USC Rossier School of Education.Google Scholar
  15. Davidson, K. L., & Frohbieter, G. (2011). District adoption and implementation of interim and benchmark assessments (Report No. 806). Los Angeles, CA: National Center for Research on Evaluation, Standards, and Student Testing (CRESST).Google Scholar
  16. Diamond, J. B., & Cooper, K. (2007). The uses of testing data in urban elementary schools: Some lessons from Chicago. Yearbook of the National Society for the Study of Education, 106(1), 241–263.CrossRefGoogle Scholar
  17. Dowd, A. (2005). Data don’t drive: Building a practitioner-driven culture of inquiry to assess community college performance. Boston: University of Massachusetts, Lumina Foundation for Education.Google Scholar
  18. Firestone, W. A., & González, R. A. (2007). Culture and processes affecting data use in school districts. In P. A. Moss (Ed.), Evidence and decision making. Yearbook of the National Society for the study of education (pp. 132–154). Malden, MA: Blackwell.Google Scholar
  19. Gannon-Slater, N., La Londe, P. G., Crenshaw, H. L., Evans, M. E., Greene, J. C., & Schwandt, T. A. (2017). Advancing equity in accountability and organizational cultures of data use. Journal of Educational Administration, 55(4), 361–375.CrossRefGoogle Scholar
  20. Garner, B., Kahn, J., & Horn, I. (2017). Teachers interpreting data for instructional decisions: Where does equity come in? Journal of Educational Administration, 55(4), 407–426.CrossRefGoogle Scholar
  21. Gillborn, D., & Youdell, D. (1999). Rationing education: Policy, practice, reform, and equity. Buckingham: Open University Press.Google Scholar
  22. Goertz, M. E., Nabors Oláh, L., & Riggan, M. (2010). From testing to teaching: The use of interim assessments in classroom instruction (CPRE Research Report No. RR-65). Philadelphia, PA: Consortium for Policy Research in Education.Google Scholar
  23. Halverson, R., Grigg, J., Prichett, R., & Thomas, C. (2007). The new instructional leadership: Creating data-driven instructional systems in schools. Journal of School Leadership, 17(2), 159–193.Google Scholar
  24. Hargreaves, A., & Shirley, D. (2012). The global fourth way: The quest for educational excellence. Thousand Oaks, CA: Corwin Press.Google Scholar
  25. Heppen, J., Jones, W., Faria, A., Sawyer, K., Lewis, S., Horwitz, A., et al. (2012). Using data to improve instruction in the Great City Schools: Documenting current practice. Washington, DC: American Institutes for Research and The Council of Great City Schools.Google Scholar
  26. Horn, I., Kane, B., & Wilson, B. (2015). Making sense of student performance data: Data use logics and mathematics teachers’ learning opportunities. American Educational Research Journal, 52(2), 208–242.CrossRefGoogle Scholar
  27. Huguet, A., Farrell, C. C., & Marsh, J. A. (2017). Light touch, heavy hand: Principals and data-use PLCs. Journal of Educational Administration, 55(4), 376–389.CrossRefGoogle Scholar
  28. Jimerson, J. B., & Childs, J. (2017). Signal and symbol: How state and local policies address data-informed practice. Educational Policy, 31(5), 584–614.CrossRefGoogle Scholar
  29. Jimerson, J. B., & Wayman, J. C. (2015). Professional learning for using data: Examining teacher needs and supports. Teachers College Record, 117(4), 1–36.Google Scholar
  30. Kennedy, B., & Datnow, A. (2011). Bolstering student engagement through the inclusion of voice: A case study of data-driven decision making in schools. Youth and Society, 43(4), 1246–1271.CrossRefGoogle Scholar
  31. Knapp, M. S., Copland, M. A., & Swinnerton, J. A. (2007). Understanding the promise and dynamics of data-informed leadership. Yearbook of the National Society for the Study of Education, 106(1), 74–104.CrossRefGoogle Scholar
  32. Lachat, M. A., & Smith, S. (2005). Practices that support data use in urban high schools. Special Issue on transforming data into knowledge: Applications of data-based decision making to improve instructional practice. Journal of Education for Students Placed At-Risk, 10(3), 333–339.CrossRefGoogle Scholar
  33. Ladson-Billings, G. (2006). From the achievement gap to the education debt: Understanding achievement in U.S. schools. Educational Researcher, 35(7), 3–12.CrossRefGoogle Scholar
  34. Lai, M. K., & Schildkamp, K. (2016). In-service teacher professional learning: Use of assessment in data-based decision-making. In G. T. L. Brown & L. R. Harris (Eds.), Handbook of human and social conditions in assessment (pp. 77–94). New York: Routledge.Google Scholar
  35. Lipman, P. (2004). High stakes education: Inequality, globalization, and urban school reform. New York: Routledge Falmer Press.CrossRefGoogle Scholar
  36. Little, J. W. (2012). Understanding data use practices among teachers: The contribution of micro-process studies. American Journal of Education, 118(2), 143–166.CrossRefGoogle Scholar
  37. Mandinach, E. B., & Honey, M. (Eds.). (2008). Data driven school improvement: Linking data and learning. New York, NY: Teachers College Press.Google Scholar
  38. Marsh, J. A. (2012). Interventions promoting educators’ use of data: Research insights and gaps. Teachers College Record, 114(11), 1–48.Google Scholar
  39. Marsh, J., & Farrell, C. (2014). How leaders can support teachers with data-driven decision making. Education Management Administration and Leadership, 43(2), 269–289.CrossRefGoogle Scholar
  40. Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education. Santa Monica, CA: RAND.Google Scholar
  41. McNeil, L. (2002). Contradictions of school reform: Educational costs of standardized testing. New York: Routledge.Google Scholar
  42. McNeil, L. M. (2005). Faking equity: High-stakes testing and the education of Latino youth. In A. Valenzuela (Ed.), Leaving children behind: How” Texas-style” accountability fails Latino youth (pp. 57–111). Albany, NY: SUNY Press.Google Scholar
  43. Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing data-informed decision making in schools—Teacher access, supports and use. Washington, DC: U.S Department of Education, Office of Planning, Evaluation, and Policy Development.Google Scholar
  44. Neuman, S. B. (2016). Code red: The danger of data-driven instruction. Educational Leadership, 74(3), 24–29.Google Scholar
  45. Oakes, J. (2005). Keeping track: How schools structure inequality (2nd ed.). New Haven, CT: Yale University Press.Google Scholar
  46. Oakes, J., Gamoran, A., & Page, R. N. (1992). Curriculum differentiation: Opportunities, outcomes, and meanings. In P. Jackson (Ed.), Handbook of research on curriculum (pp. 570–608). New York: Macmillan.Google Scholar
  47. Oakes, J., Wells, A. S., Jones, M., & Datnow, A. (1997). Detracking: The social construction of ability, cultural politics, and resistance to reform. Teachers College Record, 98(3), 482–510.Google Scholar
  48. Oláh, L. N., Lawrence, N. R., & Riggan, M. (2010). Learning to learn from benchmark assessment data: How teachers analyze results. Peabody Journal of Education, 85, 226–245.CrossRefGoogle Scholar
  49. Park, V. (2018). Data conversation moves: Towards data-informed leadership for equity and learning. Educational Administration Quarterly, 1–34.
  50. Park, V., Daly, A. J., & Guerra, A. W. (2013). Strategic framing: How leaders craft the meaning of data use for equity and learning. Educational Policy, 27(4), 645–675.CrossRefGoogle Scholar
  51. Park, V., & Datnow, A. (2009). Co-constructing distributed leadership: District and school connections in data-driven decision making. School Leadership and Management, 29(5), 477–494.CrossRefGoogle Scholar
  52. Park, V., & Datnow, A. (2017). Ability grouping and differentiated instruction in an era of data-driven decision making. American Journal of Education, 123(2), 281–306.CrossRefGoogle Scholar
  53. Park, V., St. John, E., Datnow, A., & Choi, B. (2017). The balancing act: Student placement routines and the uses of data in elementary schools. Journal of Educational Administration, 55(4), 390–406.CrossRefGoogle Scholar
  54. Pierce, R., & Chick, H. (2011). Teachers’ intentions to use national literacy and numeracy assessment data: A pilot study. Australian Educational Research, 38(3), 433–477.CrossRefGoogle Scholar
  55. Pollock, M. (2017). Schooltalk: Rethinking what we say to—And about—Students every day. New York: New Press.Google Scholar
  56. Santelices, M. V., & Wilson, M. (2010). Unfair treatment? The case of Freedle, the SAT, and the standardization approach to differential item functioning. Harvard Educational Review, 80(1), 106–134.CrossRefGoogle Scholar
  57. Schildkamp, K., Karbautzki, L., & Vanhoof, J. (2014). Exploring data use practices around Europe: Identifying enablers and barriers. Studies in Educational Evaluation, 42, 15–24.CrossRefGoogle Scholar
  58. Schildkamp, K., & Poortman, C. (2015). Factors influencing the functioning of data teams. Teachers College Record, 117(4), 040310.Google Scholar
  59. Shepard, L., Davidson, K., & Bowman, R. (2011). How middle school mathematics teachers use interim and benchmark assessment data (CSE Technical Report). Los Angeles, CA: University of California, National Center for Research on Evaluation, Standards, and Student Testing (CRESST).Google Scholar
  60. Shirley, D. (2017). The new imperatives of educational change: Achievement with integrity. New York: Routledge.Google Scholar
  61. Skrla, L., Scheurich, J. J., Garcia, J., & Nolly, G. (2004). Equity audits: A practical leadership tool for developing equitable and excellent schools. Educational Administration Quarterly, 40(1), 133–161.CrossRefGoogle Scholar
  62. Snodgrass Rangel, V., Bell, E., & Monroy, C. (2017). Teachers’ sensemaking and data use implementation in science classrooms. Education and Urban Society.
  63. Valenzuela, A. (Ed.). (2005). Leaving children behind: How” Texas-style” accountability fails Latino youth. Albany, NY: SUNY Press.Google Scholar
  64. Valli, L., Cooper, D., & Frankes, L. (1997). Professional development schools and equity: A critical analysis of rhetoric and research. In M. W. Apple (Ed.), Review of research in education, (Vol. 22, pp. 251–304). Washington, DC: American Educational Research Association.Google Scholar
  65. Vanlommel, K., Van Gasse, R., Vanhoof, J., & Petegem, V. (2017). Teachers’ decision making: Data-based or intuition driven? International Journal of Educational Research, 83, 75–83.CrossRefGoogle Scholar
  66. Wardrip, P. S., & Herman, P. (2017). ‘We’re keeping on top of the students’: Making sense of test data with more informal data in a grade-level instructional team. Teacher Development. Scholar

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of California, San DiegoLa JollaUSA
  2. 2.San Jose State UniversitySan JoseUSA

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