Journal of Educational Change

, Volume 17, Issue 1, pp 7–28 | Cite as

Teacher capacity for and beliefs about data-driven decision making: A literature review of international research

  • Amanda DatnowEmail author
  • Lea Hubbard


Data-driven decision making continues to be a growing educational reform initiative across the globe. The effective use of data requires that teachers develop the knowledge and skills to analyze and use data to improve instruction. The purpose of this article is to examine teachers’ capacity for and beliefs about data use. These issues are examined through a review of research in the past decade. We find that teachers’ beliefs about and capacity for data use are often not connected within the literature or in practice, but we argue they are the heart of the connection between data and instructional change. Teachers’ capacity to use data and their beliefs about data use are shaped within their professional communities, in training sessions, and in their interactions with coaches, consultants, and principals. However, efforts to develop teachers’ capacity for data use often fall short of their goals. Correspondingly, teachers have varied beliefs about data use, and some feel they lack the ability to use data to inform instruction. In order to be more successful, capacity building should directly address teachers’ beliefs, and data use must be decoupled from external accountability demands and involve a variety of information on student learning.


Data-driven decision making Teacher capacity Teacher beliefs 


  1. Bambrick-Santoyo, P. (2010). Driven by data: A practice guide to improve instruction. San Francisco: Jossey Bass Publishers.Google Scholar
  2. Bernhardt, V. (2013). Data analysis for continuous improvement (3rd ed.). New York: Routledge.Google Scholar
  3. Blanc, S., Christman, J. B., Liu, R., Mitchell, C., Travers, E., & Bulkley, K. E. (2010). Learning to learn from data: Benchmarks and instructional communities. Peabody Journal of Education, 85(2), 205–225.CrossRefGoogle Scholar
  4. Bocala, C., & Boudett, K. P. (2015). Teaching educators habits of mind for using data wisely. Teachers College Record, 117(4), 1–20.Google Scholar
  5. Brookhart, S. M. (2011). Educational assessment knowledge and skills for teachers. Educational Measurement: Issues and Practice, 30(1), 3–12.CrossRefGoogle Scholar
  6. Brown, G. T., Lake, R., & Matters, G. (2011). Queensland teachers’ conceptions of assessment: The impact of policy priorities on teacher attitudes. Teaching and Teacher Education, 27(1), 210–220.CrossRefGoogle Scholar
  7. Bruning, R., Schraw, G., & Ronning, R. (1999). Cognitive psychology and instruction. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  8. Cho, V., & Wayman, J. (2014). District efforts for data use and computer data systems: The role of sensemaking in system use and implementation. Teachers College Record, 116, 020306.Google Scholar
  9. 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 from
  10. 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
  11. 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
  12. Cosner, S. (2011a). Teacher learning, instructional considerations and principal communication: Lessons from a longitudinal study of collaborative data use by teachers. Educational Management Administration and Leadership, 39(5), 568–589.CrossRefGoogle Scholar
  13. Cosner, S. (2011b). Supporting the initiation and early development of evidence-based grade-level collaboration in urban elementary schools: Key roles and strategies of principals and literacy coordinators. Urban Education, 46(4), 786–827.CrossRefGoogle Scholar
  14. 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
  15. Datnow, A., & Hubbard, L. (2015). Teachers’ use of data to inform instruction: Lessons from the past and prospects for the future. Teachers College Record, 117(4), 1–26.Google Scholar
  16. Datnow, A., & Park, V. (2014). Data-driven leadership. San Francisco: Jossey Bass.Google Scholar
  17. 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
  18. 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
  19. Diamond, J. B., & Cooper, K. (2007). The uses of testing data in urban elementary schools: Some lessons from Chicago. National Society for the Study of Education Yearbook, 106(1), 241–263.CrossRefGoogle Scholar
  20. Dunn, K. E., Airola, D. T., Lo, W., & Garrison, M. (2012). What teachers think about what they can do with data: Development and validation of the data-driven decision making efficacy and anxiety inventory. Contemporary Educational Psychology, 38, 87–98.CrossRefGoogle Scholar
  21. Earl, L., & Katz, S. (2006). Leading schools in a data-rich world. Thousand Oaks, CA: Corwin Press.Google Scholar
  22. Elmore, R. E. (1996). Getting to scale with good educational practice. Harvard Educational Review, 66(1), 1–26.CrossRefGoogle Scholar
  23. Farley-Ripple, E., & Buttram, J. (2015). The development of capacity for data use: The role of teacher networks in an elementary school. Teachers College Record, 117(4), 1–34.Google Scholar
  24. 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
  25. Gummer, E., & Mandinach, E. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117(4), 1–22.Google Scholar
  26. 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
  27. Hamilton, L., Halverson, R., Jackson, S. S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). IES Practice Guide: Using student achievement data to support instructional decision making (NCEE 2009-4067). Washington, DC: National Center for Education Evaluation and Regional Assistance. Retrieved from
  28. Honig, M. I., & Ikemoto, G. (2008). Adaptive assistance for learning improvement efforts: The case of the Institute for Learning. Peabody Journal of Education, 83(3), 328–363.CrossRefGoogle Scholar
  29. Honig, M. I., & Venkateswaran, N. (2012). School–central office relationships in evidence use: Understanding evidence use as a systems problem. American Journal of Education, 118(2), 199–222.CrossRefGoogle Scholar
  30. Horn, I. S., Kane, B. D., & Wilson, J. (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
  31. Horn, I. S., & Little, J. W. (2010). Attending to problems of practice: Routines and resources for professional learning in teachers’ workplace interactions. American Educational Research Journal, 47(1), 181–217.CrossRefGoogle Scholar
  32. Hubbard, L., Datnow, A., & Pruyn, L. (2014). Multiple initiatives, multiple challenges: The promise and pitfalls of implementing data use. Studies in Educational Evaluation, 42, 54–62.CrossRefGoogle Scholar
  33. Huguet, A., Marsh, J. A., & Farrell, C. C. (2015) Building teachers’ data-use capacity: Insights from strong and struggling coaches. Education Policy Analysis Archives, 22(52), 1–26.
  34. Ingram, D., Louis, K. S., & Schroeder, R. (2004). Accountability policies and teacher decision making: Barriers to the use of data to improve practice. Teachers College Record, 106(6), 1258–1287.CrossRefGoogle Scholar
  35. Jimerson, J. B. (2014). Thinking about data: Exploring the development of mental models for “data use” among teachers and school leaders. Studies in Educational Evaluation, 42, 5–14.CrossRefGoogle Scholar
  36. 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
  37. Kerr, K. A., Marsh, J. A., Ikemoto, G. S., Darilek, H., & Barney, H. (2006). Strategies to promote data use for instructional improvement: Actions, outcomes, and lessons from three urban districts. American Journal of Education, 112(3), 496–520.CrossRefGoogle Scholar
  38. Knapp, M. S., Copland, M. A., Swinnerton, J. A. (2007). School district roles and resources: Understanding the promise and dynamics of data-informed leadership. In P. A. Moss (Ed.), Evidence and decision making (National Society for the Study of Education Yearbook, Vol. 106, Issue 1, pp. 74–104). Chicago: National Society for the Study of Education.Google Scholar
  39. 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 Change for Students Placed At-Risk, 10(3), 333–349.CrossRefGoogle Scholar
  40. Levin, J., & Datnow, A. (2012). The principal as agent of mediated educational reform: Dynamic models of case Studies of data driven decision making. School Effectiveness and School Improvement, 23(2), 179–201.CrossRefGoogle Scholar
  41. Long, L., Rivas, L. M., Light, D., & Mandinach, E. B. (2008). The evolution of the homegrown data warehouse: TUSDSstats. In E. B. Mandinach & M. Honey (Eds.), Data-driven school improvement: Linking data and learning. New York: Teachers College Press.Google Scholar
  42. Mandinach, E. B., & Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30–37.CrossRefGoogle Scholar
  43. Mandinach, E. B., Gummer, E. S., & Friedman, J. M. (2015). How can schools of education help to build educators’ capacity to use data: A systemic view of the issue. Teachers College Record, 117(4), 1–50.Google Scholar
  44. Mandinach, E. B., & Honey, M. (Eds.). (2008). Data driven school improvement: Linking data and learning. New York, NY: Teachers College Press.Google Scholar
  45. Marsh, J. A. (2012). Interventions promoting educators’ use of data: Research insights and gaps. Teachers College Record, 114(11), 1–48.Google Scholar
  46. Marsh, J. A., Bertrand, M., & Huguet, A. (2015). Using data to alter instructional practice: The mediating role of coaches and professional learning communities. Teachers College Record, 117(4), 1–40.Google Scholar
  47. Marsh, J. A., & Farrell, C. C. (2015). Supporting teachers with data-driven decision making: A framework for understanding capacity-building. Education Management Administration and Leadership, 43(2), 269–289.CrossRefGoogle Scholar
  48. Means, B., Chen, E., DeBarger, A. & Padilla, C. (2011). Teachers’ ability to use data to inform instruction: Challenges and supports. US Department of Education, Office of Planning, Evaluation, and Policy Development, Washington, DC.Google Scholar
  49. Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing data-informed decision making in schools—Teacher access, supports and use. Washington, DC: US Department of Education, Office of Planning, Evaluation, and Policy Development.Google Scholar
  50. Means, B., Padilla, C., & Gallagher, L. (2010). Use of education data at the local level: From accountability to instructional improvement. US Department of Education, Office of Planning, Evaluation, and Policy Development, Washington, DC.Google Scholar
  51. Nelson, T. H., & Slavit, D. (2007). Collaborative inquiry among science and mathematics teachers in the USA: Professional learning experiences through cross-grade, cross-discipline dialogue. Professional Development in Education, 33(1), 23–39.Google Scholar
  52. Park, V., Daly, A. J., & Guerra, A. W. (2012). Strategic framing: How leaders craft the meaning of data use for equity and learning. Educational Policy, 27(4), 645–675. doi: 10.1177/0895904811429295.CrossRefGoogle Scholar
  53. 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
  54. Remesal, A. (2011). Primary and secondary teachers’ conceptions of assessment: A qualitative study. Teaching and Teacher Education, 27(2), 472–482.CrossRefGoogle Scholar
  55. Schildkamp, K., & Kuiper, W. (2010). Data-informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education, 26(3), 482–496.CrossRefGoogle Scholar
  56. Schildkamp, K., & Lai, M. K. (2012). Introduction. In K. Schildkamp, M. K. Lai, & L. Earl (Eds.), Data-based decision making in education: Challenges and opportunities (pp. 1–9). Dordrecht: Springer.Google Scholar
  57. Schildkamp, K., & Poortman, C. (2015). Factors influencing the functioning of data teams. Teachers College Record, 117(4), 1–42.Google Scholar
  58. Schnellert, L. M., Butler, D. L., & Higginson, S. K. (2008). Co-constructors of data, co-constructors of meaning: Teacher professional development in an age of accountability. Teaching and Teacher Education, 24(3), 725–750.CrossRefGoogle Scholar
  59. Senge, P. (1990). The fifth discipline: The art and practice of the learning organization. New York: Doubleday.Google Scholar
  60. Spillane, J. (2012). Data in practice: Conceptualizing the data-based decision-making phenomena. American Journal of Education, 118, 113–141.CrossRefGoogle Scholar
  61. Spillane, J., & Miele, D. (2007). Evidence in practice: A framing of the terrain. In P. A. Moss (Ed.), Evidence and decision making (National Society for the Study of Education Yearbook, Vol. 106, Issue 1, pp. 46–73). Chicago: National Society for the Study of Education.Google Scholar
  62. Takahashi, S. (2011). Co-constructing efficacy: A “communities of practice” perspective on teachers’ efficacy beliefs. Teaching and Teacher Education, 27, 732–741.CrossRefGoogle Scholar
  63. TERC. (n.d.). Using data to improve learning for all. Retrieved from
  64. Timperley, H. (2009). Evidence-informed conversations making a difference to student achievement. In L. Earl & H. Timperley (Eds.), Professional learning conversations: Challenges in using evidence for improvement (pp. 69–79). New York: Springer.CrossRefGoogle Scholar
  65. Tschannen-Moran, M., & Woolfolk-Hoy, A. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17, 783–805.CrossRefGoogle Scholar
  66. Wayman, J. C., & Cho, V. (2008). Preparing educators to effectively use student data systems. In T. J. Kowalski & T. J. Lasley (Eds.), Handbook on data-based decision-making in education (pp. 89–104). New York: Routledge.Google Scholar
  67. Wenger, E. (1998). Communities of practice: Learning, meaning and identity. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  68. White, P. A. U. L., & Anderson, J. U. D. Y. (2011). Teachers’ use of national test data to focus numeracy instruction. Mathematics: Traditions and [new] practices, 777–785.Google Scholar
  69. Woolfolk, A. E., Rossoff, B., & Hoy, W. K. (1990). Teachers’ sense of efficacy and their beliefs about managing students. Teaching and Teacher Education, 6, 137–148.CrossRefGoogle Scholar
  70. Young, V. M. (2006). Teachers’ use of data: Loose coupling, agenda setting, and team norms. American Journal of Education, 112(4), 521–548.CrossRefGoogle Scholar
  71. Young, V. M. & Kim, D. H. (2010). Using assessments for instructional improvement: A literature review. Educational Policy Analysis Archives, 18(19). Retrieved from

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.University of California, San DiegoLa JollaUSA
  2. 2.University of San DiegoSan DiegoUSA

Personalised recommendations