Skip to main content

Student-involved data use: Establishing the evidence base

Abstract

In this conceptual paper, we map the research terrain on what we term “student-involved data use” (SIDU)—that is, the practice of having students track, chart, and analyze their own data in formal and structured ways. Drawing on peer-reviewed research as well as practitioner-oriented literature, social media, and district websites, we leverage a lens informed by goal achievement theory to highlight the potential benefits of SIDU as well as the possible dangers of implementing SIDU in haphazard ways. We begin by tracing the rise of the modern iteration of this data-driven practice, rooted in the application of principles associated with the Malcolm Baldrige National Quality Award criteria to the classroom level and to the teacher–student dyad. We then describe common components of the practice, including the use of student “data folders” or “data binders,” semi-public classroom “data walls,” and “quality tools” (such as the Plan-Do-Study-Act cycle) that aim at building reflective practice among students. Beyond describing the increasingly popular practice, we note that the empirical base for SIDU is quite shallow at present: implementation is far outpacing the evidence on the processes involved in and outcomes of SIDU. Currently, practitioners and policymakers have little in the way of empirical evidence to guide efforts, potentially to the detriment of long-term educational outcomes. To this end, we posit a range of ways in which the research community can join in comprehensive inquiry efforts aimed at learning more about the conditions under which SIDU may be a positive and productive process for students.

This is a preview of subscription content, access via your institution.

Fig. 1

Notes

  1. While Park et al. (2013) point out that there are several such providers that offer this type of training, we focused on the materials offered by Jim Shipley & Associates because the company was referred to us by several district leaders and teachers around the Dallas-Fort Worth metroplex area. Upon learning that JSA has provided training for over a decade to districts in at least 12 states, we were comfortable that their materials well-represented the information included in introductions to classroom-based continuous improvement structures, including SIDU.

  2. Alternate versions of the SMART acronym offer “Achievable” for the A and “Realistic” or “Relevant” for the R.

References

  • Baldrige Performance Excellence Program. (2015). 20152016 Baldrige excellence framework: A systems approach to improving your organization’s performance (education). Gaithersburg, MD: US Department of Commerce, National Institute of Standards and Technology. http://www.nist.gov/baldrige

  • Banister, S. I. (2002). A question of quality: The Malcolm Baldrige criteria as applied to education. Journal of Research for Educational Leaders, 1(2), 44–65.

    Google Scholar 

  • Benjamin, S. (2000). The quality rubric: A systematic approach for implementing quality principles and tools in classrooms and schools. Milwaukee, WI: ASQ Quality Press.

    Google Scholar 

  • Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education, 5(1), 7–74.

    Article  Google Scholar 

  • Blackwell, L. D., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78(1), 246–263.

    Article  Google Scholar 

  • Booher-Jennings, J. (2005). Below the bubble: “Educational triage” and the Texas accountability system. American Educational Research Journal, 42(2), 231–268.

    Article  Google Scholar 

  • Burgard, J. J. (2009). Continuous improvement in the science classroom (2nd ed.). Milwaukee, WI: ASQ Quality Press.

    Google Scholar 

  • Byrnes, M. A., & Baxter, J. C. (2012). There is another way! Launch a Baldrige-based quality classroom (2nd ed.). Milwaukee, WI: ASQ Quality Press.

    Google Scholar 

  • Cauley, K. M., & McMillan, J. H. (2010). Formative assessment techniques to support student motivation and achievement. The Clearing House, 83(1), 1–6.

    Article  Google Scholar 

  • Chappuis, J. (2009). Seven strategies of assessment for learning (2nd ed.). Boston: Pearson.

  • Chappuis, J., Stiggins, R. J., Chappuis, S., & Arter, J. A. (2012). Classroom assessment for student learning doing it right—Using it well (2nd ed.). Boston: Pearson.

    Google Scholar 

  • Clymer, J. B., & Wiliam, D. (2007). Improv-ing the way we grade science. Educational Leadership, 64, 36–42.

    Google Scholar 

  • Coburn, C. E., Honig, M. I., & Stein, M. K. (2009). What’s the evidence on districts’ use of evidence? In J. D. Bransford, D. J. Stipek, N. J. Vye, L. M. Gomez, & D. Lam (Eds.), The role of research in educational improvement (pp. 67–87). Cambridge: Harvard Education Press.

    Google Scholar 

  • Copland, M., Knapp, M., & Swinnerton, J. (2009). Data informed leadership and school improvement. In T. J. Kowalski & T. J. Lasley (Eds.), Handbook on data-based decision making in education (pp. 153–172). New York: Routledge.

    Google Scholar 

  • Courtney, L. (2014). In Pinterest [“Data Binders” board]. Retrieved December 21, 2014 from http://pinterest.com/lianec/data-binders/

  • Daly, A. (2009). Rigid response in an age of accountability: The potential of leadership and trust. Educational Administration Quarterly, 45(2), 168–216.

    Article  Google Scholar 

  • Datnow, A., Park, V., & Wohlstetter, P. (2007). Achieving with data: How high-performing school systems use data to improve instruction for elementary students. Retrieved August 13, 2010 from New Schools Venture Fund website: http://www.newschools.org/files/AchievingWithData.pdf

  • Dunlap Community Unit Schools #323 (DCUS). (2013). Continuous improvement. Retrieved September 19, 2013 from http://www.dunlapcusd.net/CI/Pages/StudentDataFolders.aspx

  • Dweck, C. S. (2006). Mindset: The new psychology of success. New York: Ballantine.

    Google Scholar 

  • Farley-Ripple, E. N., & Cho, V. (2014). Depth of use: How district decision-makers did and did not engage with evidence. In A. Bowers, A. R. Shoho, & B. G. Barnett (Eds.), Using data in schools to inform leadership and decision-making—international research on school leadership (Vol. 5, pp. 229–252). Charlotte, NC: Information Age Publishing Inc.

    Google Scholar 

  • Flynn, B. B., & Saladin, B. (2001). Further evidence on the validity of the theoretical models underlying the Baldrige criteria. Journal of Operations Management, 19, 617–652.

    Article  Google Scholar 

  • Hackman, J. R., & Wageman, R. (1995). Total quality management: Empirical, conceptual, and practical issues. Administrative Science Quarterly, 40(2), 309–342.

    Article  Google Scholar 

  • Halverson, R. (2010). School formative feedback systems. Peabody Journal of Education, 85, 130–146.

    Article  Google Scholar 

  • Hamilton, L. S., Halverson, R., Jackson, S. S., Mandinach, E., Supovitz, J. A., & Wayman, J. C. (2009). Using student achievement data to support instructional decision making. Washington, DC: Institute of Education Sciences and the National Center for Education Evaluation.

    Google Scholar 

  • Hattie, J. (2011). Visible learning for teachers: Maximizing impact on learning. New York: Routledge.

    Google Scholar 

  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.

    Article  Google Scholar 

  • Heritage, M. (2007). Formative assessment: What do teachers need to know and do? Phi Delta Kappan, 89, 140–145.

    Article  Google Scholar 

  • Heritage, M. (2010). Formative assessment and next-generation assessment systems: Are we losing an opportunity? Washington, DC: Council of Chief State School Officers.

    Google Scholar 

  • Hurst-Euless-Bedford Independent School District (HEBISD). (2011). Continuous improvement. Retrieved September 19, 2013 from http://www.hebisd.edu/CI/ci-default.asp

  • Ikemoto, G. S., & Marsh, J. A. (2007). Cutting through the “data-driven” mantra: Different conceptions of data-driven decision making. In P. A. Moss (Ed.), Evidence and decision making (pp. 105–131). Malden, MA: Blackwell.

    Google Scholar 

  • Jimerson, J. B. & McGhee, M. (2013). Leading inquiry in schools: Examining mental models of datainformed practice. Current Issues in Education, 16(1), 1–20.

  • Jim Shipley & Associates. (2012a). Continuous classroom improvement: First steps in using a systems approach to improve learning results (3rd ed.). North Redington Beach, Florida: Jim Shipley & Associates.

    Google Scholar 

  • Jim Shipley & Associates. (2012b). Doing it right! A guide to student data folders and student-led conferences. North Redington Beach, Florida: Jim Shipley & Associates.

    Google Scholar 

  • Jim Shipley & Associates. (2013). Retrieved October 13, 2013 from www.jimshipley.net

  • Kennedy, B. L., & Datnow, A. (2011). Student involvement and data-driven decision making: Developing a new typology. Youth & Society, 43(4), 1246–1271.

    Article  Google Scholar 

  • Kerr, K. I., 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, 496–520.

    Article  Google Scholar 

  • Loeb, H., Knapp, M. S., & Elfers, A. M. (2008). Teachers’ response to standards-based reform: Probing reform assumptions in Washington State. Education Policy Analysis Archives, 16(8), 1–32.

    Google Scholar 

  • Louis, K. S., Leithwood, K., Wahlstrom, K. L., Anderson, S. E., Michlin, M., Mascall, B., et al. (2010). Learning from leadership: Investigating the links to improved student learning (final report of research findings). New York, NY: The Wallace Foundation.

    Google Scholar 

  • Maggin, D. M., Chafouleas, S. M., Goddard, K. M., & Johnson, A. H. (2011). A systematic evaluation of token economies as a classroom management tool for students with challenging behavior. Journal of School Psychology, 49, 529–554.

    Article  Google Scholar 

  • Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice. Educational Psychologist, 47(2), 71–85.

    Article  Google Scholar 

  • Mandinach, E. B., & Jackson, S. S. (2012). Transforming teaching and learning through data-driven decision making. Thousand Oaks, CA: SAGE Publications.

    Google Scholar 

  • Marsh, J. A. (2012). Interventions promoting educators’ use of data: Research insights and gaps. Teachers College Record, 114(11), 1–48.

  • Marsh, J. A., Farrell, C. C., & Bertrand, M. (2014). Trickle-down accountability: How middle school teachers engage students in data use. Educational Policy. doi:10.1177/0895904814531653

    Google Scholar 

  • Marsh, J. A., McCombs, J. S., & Martorell, F. (2010). How instructional coaches support data-driven decision making: Policy implementation and effects in Florida middle schools. Educational Policy, 24(6), 872–907.

    Article  Google Scholar 

  • Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education. RAND. Retrieved December 17, 2012 from http://www.rand.org/content/dam/rand/pubs/occasional_papers/2006/RAND_OP170.pdf

  • Martin, B. (2011.) Tutorials-student data folders. Cedar Rapids Community School District (CRCSD). Retrieved September 19, 2013 from http://quality.cr.k12.ia.us/index_search.asp

  • Means, B., Chen, E., DeBarger, A., & Padilla, C. (2011). Teachers’ ability to use data to inform instruction: Challenges and supports. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation, and Policy Development.

    Google Scholar 

  • Meece, J. L., Anderman, E. M., & Anderman, L. H. (2006). Classroom goal structure, student motivation, and academic achievement. Annual Review of Psychology, 57, 487–503.

    Article  Google Scholar 

  • Miller, M. N., Bell, E. V., & Holland, D. F. (2009). Slow turn ahead: 5 principles guide district through a changing demographic landscape. Journal of Staff Development, 30(4), 46–50.

    Google Scholar 

  • Montgomery County Public Schools (MCPS). (2010). Data notebooks/folders. Retrieved September 20, 2013 from http://montgomeryschoolsmd.org/info/baldrige/staff/datanotebooks.shtm

  • Neves, J. S., & Nakhai, B. (1993). The Baldrige award framework for teaching total quality management. Journal of Education for Business, 69(2), 121–125.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Park, S., Hironaka, S., Carver, P., & Nordstrum, L. (2013). Continuous improvement in education. Stanford, CA: Carnegie Foundation for the Advancement of Teaching.

    Google Scholar 

  • Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–686.

    Article  Google Scholar 

  • Plank, S. B., & Condliffe, B. F. (2013). Pressures of the season: An examination of classroom quality and high-stakes accountability. American Educational Research Journal, 50(5), 1152–1182.

    Article  Google Scholar 

  • Pollock, M. (2013). It takes a network to raise a child—Improving the communication infrastructure of public education. Teachers College Record, 115(7), 1–28.

    Google Scholar 

  • Quebodeaux, P. S. (2010). Quality in education in the Calcasieu Parish school system: Experiences of administrators. Dissertation, University of New Orleans.

  • Reeves, D. B., & Flach, T. (2011). Data: Meaningful analysis can rescue schools from drowning in data. Journal of Staff Development, 32(4), 34–40.

    Google Scholar 

  • Rhim, L. M. (2011). Learning how to dance in the Queen City: Cincinnati Public Schools’ turnaround initiative. Darden/Curry Partnership for Leaders in Education, University of Virginia, Charlottesville, VA. Retrieved August 19, 2013 from http://www.darden.virginia.edu/web/uploadedFiles/Darden/Darden_Curry_PLE/UVA_School_Turnaround/DanceInTheQueenCity_Sept2011.pdf

  • Senge, P. (2006). The fifth discipline: The art & practice of the learning organization (Rev). New York: Currency Doubleday.

    Google Scholar 

  • Senge, P., Cambron-McCabe, N., Lucas, T., Smith, B., & Dutton, J. (2012). Schools that learn (updated and revised): A fifth discipline fieldbook for educators, parents, and everyone who cares about education. New York: Crown Publishing.

    Google Scholar 

  • Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189.

    Article  Google Scholar 

  • Simonsen, B., Fairbanks, S., Briesch, A., Myers, D., & Sugai, G. (2008). Evidence-based practices in classroom management: Considerations for research to practice. Education and Treatment of Children, 31, 351–380.

    Article  Google Scholar 

  • Stiggins, R. (2005). From formative assessment to assessment for learning: A path to success in standards-based schools. Phi Delta Kappan, 87(4), 324–328.

    Article  Google Scholar 

  • Stuart, S. K., & Rinaldi, C. (2011). Response to intervention for English learners. The Journal of Multiculturalism in Education, 7(3), 1–16.

    Google Scholar 

  • Sugai, G., Horner, R. H., Dunlap, G., Hieneman, M., Lewis, T. J., Nelson, C. M., et al. (2000). Applying positive behavior support and functional behavioral assessment in schools. Journal of Positive Behavior Interventions, 2(3), 131–143.

    Article  Google Scholar 

  • Valli, L., & Buese, D. (2007). The changing roles of teachers in an era of high-stakes accountability. American Educational Research Journal, 44(3), 519–558.

    Article  Google Scholar 

  • Wahlstrom, K. L., Louis, K. S., Leithwood, K., & Anderson, S. E. (2010). Investigating the links to improved student learning. Executive summary of research findings. New York: The Wallace Foundation.

    Google Scholar 

  • Wayman, J. C., & Jimerson, J. B. (2014). Teacher needs for data-related professional learning. Studies in Educational Evaluation, 42, 25–34.

  • Wayman, J. C., Spring, S. D., & Lemke, M. A., Lehr, M. D. (2012). Using data to inform practice: Effective principal leadership strategies. Paper presented at the 2012 Annual Meeting of the American Educational Research Association, Vancouver, Canada.

  • Wayman, J. C., & Stringfield, S. (2006). Technology-supported involvement of entire faculties in examination of student data for instructional improvement. American Journal of Education, 112(August), 549–571.

    Article  Google Scholar 

  • Weaver, T. (1992). Total quality management. ERIC Digest No. 73. Retrieved from ERIC database (ED347670).

  • Wiliam, D. (2011). What is assessment for learning? Studies in Educational Evaluation, 37(1), 3–14.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jo Beth Jimerson.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jimerson, J.B., Reames, E. Student-involved data use: Establishing the evidence base. J Educ Change 16, 281–304 (2015). https://doi.org/10.1007/s10833-015-9246-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10833-015-9246-4

Keywords

  • Data-driven decision making (DDDM)
  • Continuous improvement
  • Educational data use
  • Student-involved data use (SIDU)
  • School reform
  • School improvement
  • Student data folders
  • Data walls