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Dissertation/Thesis Abstract

District level practices in data driven decision making
by Danielian, Hasmik J., Ed.D., University of Southern California, 2009, 207; 3355413
Abstract (Summary)

Forty years after the Elementary and Secondary Education Act, numerous school reforms have attempted to tackle the same problems initially addressed by the ESEA. Most recently, The No Child Left Behind (NCLB) Act of 2001, the latest in the series of educational reform initiatives, distinguishes itself by its unparalleled focus on accountability.

As mandated by NCLB, policy makers had hoped that publishing disaggregated data on standardized test scores would result in increased awareness for educators of existing achievement gaps—in short, create a “culture of inquiry,” where the data creates an atmosphere that promotes awareness. However, what this chain of assumptions failed to take into account was the challenges associated with attempting to implement increased student achievement for all students through data-driven decision making (DDDM). The extant research on DDDM suggests that conditions at the district level (as well as at the school and state levels) clearly impact the nature and extent of DDDM that is put in place at a given district. This qualitative research study adds to the field literature by developing an increased understanding of DDDM by district leaders in 4 districts. The study focused specifically on superintendents, assistant superintendents, and assessment and/or technology office leaders known for their effectiveness in the use of data-driven decision making to improve and inform instruction that results in increased student achievement.

Each of the primary sections in this study, examine in careful detail the six, key themes that were generated by the qualitative data; these six themes are: the No Child Left Behind Act and the impact of this law on data use, the Processes of DDDM, the Types of data utilized, the Culture of data use, the District Structures/Practices with regards to data, and the Challenges to DDDM in each district.

Consequently, this study will be useful to district leaders who intend to utilize data-driven decision making to ensure school/district improvement. For districts that do not currently use data-driven decision making, it is hoped that the findings will be both inspirational as well as material in assisting districts to form a theory of action.

Indexing (document details)
Advisor: Datnow, Amanda
Commitee: Bewer, Dominic, Vargas, Edward Lee
School: University of Southern California
Department: Education(Leadership)
School Location: United States -- California
Source: DAI-A 70/05, Dissertation Abstracts International
Subjects: School administration
Keywords: Best practices, Culture of data use, Data-driven decision-making, District leaders, District leaders role, Multiple measures, School district, Structures/practices, Theory of action
Publication Number: 3355413
ISBN: 978-1-109-13966-2
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