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.
Keywords
- Professional Learning
- Professional Learning Community
- Data Initiative
- Adequate Yearly Progress
- Computer Data System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Federal funds may be withheld from schools or districts failing to make progress toward AYP.
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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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