Abstract
Over the past decade, school districts in the United States have wrestled with the implementation of the No Child Left Behind Act (NCLB). This policy mandated improved student achievement for all, in part through a more widespread and sophisticated use of student data. The law placed strong incentives for districts to parse individual and collective student performance and use it to improve instruction.
Despite the law’s intention and expectation, districts are still struggling with transforming its data-driven promises into reality. This struggle derives largely from the fact that, although NCLB set high expectations regarding data use, it offered districts little guidance as to how they should actually use data. Furthermore, other than the test scores, disaggregated by race, socioeconomic status, and limited English proficiency, the policy is very vague about what data are to be used to improve instruction.
The writers of NCLB intentionally deferred to schools the internal processes of using data to inform instruction, carrying the implicit assumption that once districts were given this imperative, they would have (or could quickly create) the know-how to improve performance. This has proved an ambitious assumption, as research on data use has illuminated the substantial technological, pedagogical, and cultural challenges to educational data use (Datnow et al. 2007; Ingram et al. 2004; Means et al. 2010; Wayman et al. (in 2010a; Young 2006). Consequently, there remains a substantial gap between NCLB policy and its actual practice.
In this chapter, we explore the relationship between NCLB and data use. Our examination will be guided by Cohen and Moffitt’s (2009) framework that examined the entire history of the Elementary and Secondary Education Act (ESEA) of 1965 (of which NCLB was itself a reauthorization). Cohen and Moffit’s (2009) framework contains four parts: (1) policy aims and ambiguities, (2) policy instruments, (3) capabilities of policy, and (4) policy environment.
Cohen and Moffitt (2009) used their framework to broadly examine the NCLB policy. In this chapter, we will extend that work by using their framework to perform a specific analysis of the relationship between NCLB and the effective use of data. We will do this analysis in two stages: First, we will view current data use research through each of the framework’s four sections to describe the existing relationship between NCLB and school data use. Second, we will use the framework and current research to describe a systemic approach that would enable schools to effectively use data under NCLB. In doing so, we will argue that although NCLB may be an imperfect policy with plenty of room for improvement, there is nothing inherent to the law that would prevent a district from utilizing principles of good data use.
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- 1.
Federal funds may be withheld from schools or districts failing to make progress toward AYP.
References
Ashby, C. M. (2006). No Child Left Behind Act: Education’s data improvement efforts could strengthen the basis for distributing Title III Funds. Report to Congressional Requesters. GAO-07–140. Government Accountability Office.
Cohen, D. K., & Moffitt, S. L. (2009). The ordeal of equality: Did federal regulation fix the schools? Cambridge: Harvard University Press.
Copland, M. A. (2003). Leadership of inquiry: Building and sustaining capacity for school improvement. Education Evaluation and Policy Analysis, 25(4), 375–395.
Deike, M. A. (2009). The principal as an instructional leader within the context of effective data use. Unpublished doctoral dissertation, University of Texas at Austin.
Datnow, A., Park, V., & Wohlsetter. (2007). Achieving with data: How high performing school systems use data to improve instruction for elementary students. New Schools Venture Fund.
Hamilton, L., Halverson, R., Jackson, S. S., Mandinach, E., Supovitz, J. A., & Wayman, J. C., National Center for Education Evaluation and Regional Assistance (ED), et al. (2009). Using student achievement data to support instructional decision making. IES Practice Guide. NCEE 2009–4067. National Center for Education Evaluation and Regional Assistance.
Haycock, K. (2006). No more invisible kids. Educational Leadership, 64(3), 38–42.
Ingram, D., Louis, K. S., & Schroeder, R. G. (2004). Accountability policies and teacher decision making: Barriers to the use of data to improve practice. Teachers College Record, 106(6), 1258–1287.
Heilig, J. V. & Darling-Hammond, L. (2008). Accountability Texas-style: The progress and learning of urban minority students in a high-stakes testing context. Educational Evaluation and Policy Analysis. 30(2), 75–110.
Honig, M. I., & Coburn, C. (2008). Evidence-based decision making in school district central offices: Toward a policy and research agenda. Educational Policy, 22(4), 578–608.
Jacob, B.A. & Levitt, S.D. (2003). Rotten apples: An investigation of the prevalence and predictors of teacher cheating. The Quarterly Journal of Economics, 118(3), 843–877.
Jimerson, J. B. (2011). “Doing data”: Addressing capacity for data use through professional learning. Unpublished doctoral dissertation, University of Texas at Austin.
Jimerson, J. B., & Wayman, J. C. (2011, November). Data-related professional learning: What teachers need vs. what teachers get. Paper presented at the 2011 Annual Convention of the University Council for Educational Administration, Pittsburgh PA.
Koretz, D. M. (2002). Limitations in the use of achievement tests as measures of educators’ productivity. Journal of Human Resources, 37(4), 752–777.
Lachat, M. A., & Smith, S. (2005). Practices that support data use in urban high schools. Journal of Education for Students Placed At Risk 10(3), 333–349.
Loeb, H., Knapp, M., & Elfers, A. (2008). Teachers’ response to standards based reform: Probing assumptions in Washington state. Education Policy Analysis Archives, 16(8), 32.
Massey, D. S., & Denton, N. A. D. (1993). American apartheid: Segregation and the making of the underclass. Cambridge: Harvard University Press.
Means, B., Padilla, C., & Gallagher, L. (2010). Use of education data at the local level from accountability to instructional improvement. US Department of Education.
Meier, D., Kohn, A., Darling-Hammond, L., Sizer, T., & Wood, G. (2004). Introduction. In D. Meier & G. Wood (Ed.), How the No Child Left Behind Act is damaging our children and our schools: Many children left behind. (pp. vii–xv). Boston: Beacon.
Mieles, T., & Foley, E. (2005). Data warehousing: Preliminary findings from a study of implementing districts. Philadelphia: Annenberg Institute for School Reform.
Moll, K. A. (2009). Central office data use: A focus on district and school goals. Unpublished doctoral dissertation, University of Texas at Austin.
O’Day, J. (2002). Complexity, accountability, and school improvement. Harvard Educational Review, 72(3), 293–329.
Opfer, V. D., Henry, G. T., & Mashburn, A. J. (2008). The district effect: systemic responses to accountability in six states. American Journal of Education, 114, 299–332.
Orfield, G., Bachmeier, M. D., James, D. R., & Eitle, T. (1997). Deepening segregation in American public schools: A special report from the Harvard project on school desegregation. Equity and Excellence in Education, 30(2), 5–24.
Orfield, G. & Gordon, N. (2001). Schools more separate: Consequences of a decade of desegregation. Cambridge: Civil Rights Project, Harvard University.
Ravitch, D. (2010). The death and life of the Great American school system. New York: Basic.
Rorrer, A. K., Skrla, L., & Scheurich, J. J. (2008). Districts as institutional actors in educational reform. Educational Administration Quarterly, 44(3), 307–358.
Schildkamp, K., & Kuiper, W. (2010). Data informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education, 26, 482–496.
Senge, P. M. (2006). The fifth discipline: The art & practice of the learning organization. New York: Currency Doubleday.
Spillane, J. P. (1998). State policy and the non-monolithic nature of the local school district: Organizational and professional considerations. American Educational Research Journal, 35(1), 33–63.
Stringfield, S., & Land, D. (Eds.). (2002). Educating at-risk students. Chicago: National Society for the Study of Education.
Sunderman, G. L., & Orfield, G. (2007). Do states have the capacity to meet the NCLB mandates? Phi Delta Kappan, 89(2), 137–139.
Supovitz, J. A. (2009). Knowledge-based organizational learning for instructional improvement. In A. Hargreaves, A. Lieberman, M. Fullan, & D. Hopkins (Eds.), Second international handbook of educational change (Vol. 23, pp. 707–723). Dordrecht: Springer Netherlands.
Supovitz, J. A. (2006). The case for district-based reform: Leading, building, and sustaining school improvement. Cambridge: Harvard Education.
Supovitz, J. A., & Klein, V. (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-University of Pennsylvania.
US Department of Education. (2004, February 10). No Child Left Behind Act: Overview: Executive summary. Retrieved from http://www2.ed.gov/nclb/overview/intro/execsumm.html. Accessed 17 July 2012.
US Department of Education, National Center for Education Statistics. (2010a). Table 90: Number of public school districts and public and private elementary and secondary schools: Selected years, 1909–1970 through 2008–2009. In US Department of Education, National Center for Education Statistics Digest of Education Statistics (2010 ed.). Retrieved from http://nces.ed.gov/programs/digest/d10/tables/dt10_090.asp.
US Department of Education, National Center for Education Statistics. (2010b). Table 91: Number and enrollment of regular public school districts, by enrollment size of district: Selected years, 1979–1980 through 2008–2009. In US Department of Education, National Center for Education Statistics (Ed.), Digest of Education Statistics (2010 ed.). Retrieved from http://nces.ed.gov/programs/digest/d10/tables/dt10_090.asp. Accessed 17 July 2012.
Valli, L., & Buese, D. (2007). The changing roles of teachers in an era of high-stakes accountability. American Education Research Journal, 44(3), 519–558.
Wayman, J. C. (2005). Involving teachers in data-driven decision-making: Using computer data systems to support teacher inquiry and reflection. Journal of Education for Students Placed At Risk, 10(3), 295–308.
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.
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.
Wayman, J. C., Stringfield, S., & Yakimowski, M. (2004). Software enabling school improvement through analysis of student data (No. 67). Center for Research on the Education for Students Placed At Risk.
Wayman, J. C., Midgley, S. & Stringfield, S. (2006). Leadership for data-based decision-making: Collaborative data teams. In A. Danzig, K. Borman, B. Jones & B. Wright (Eds.), New models of professional development for learner centered leadership. Erlbaum.
Wayman, J. C., Cho, V., & Johnston, M. T. (2007). The data-informed district: A district-wide evaluation of data use in the Natrona County School District. Austin: The University of Texas.
Wayman, J. C., Cho, V., & Shaw, S. M. (2009). First-year results from an efficacy study of the Acuity data system. Austin: Authors.
Wayman, J. C., Cho, V., Jimerson, J. B., & Spikes, D. D. (in press). District-wide effects on data use in the classroom. Education Policy Analysis Archives, 20, 25.
Wayman, J. C., Cho, V., & Richards, M. P. (2010). Student data systems and their use for educational improvement. In P. Peterson, E. Baker & B. McGraw (Eds.), International Encyclopedia of Education (Vol. 8, pp. 14–20). Oxford: Elsevier.
Wayman, J. C., Jimerson, J. B., & Cho, V. (2012) Organizational considerations in establishing the data-informed district. School Effectiveness and School Improvement, 23(2), 159–178.
Young, V. M. (2006). Teachers’ use of data: Loose couple, agenda setting, and norms. American Journal of Education, 521–548.
Acknowledgment
The authors would like to thank Kim Schildkamp, Mei Kuin Lai, and Lorna Earl for their insightful comments in improving their chapter and for the opportunity to participate in this volume.
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Wayman, J., Spikes, D., Volonnino, M. (2013). Implementation of a Data Initiative in the NCLB Era. In: Schildkamp, K., Lai, M., Earl, L. (eds) Data-based Decision Making in Education. Studies in Educational Leadership, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4816-3_8
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