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Title page for ETD etd-03242018-172037
|Type of Document
|Author's Email Address
||Data Use For Instructional Improvement: Tensions, Concerns, And Possibilities For Supporting Ambitious And Equitable Instruction
||Learning, Teaching and Diversity
- mathematics education
- teacher learning
- data use
- accountability policy
|Date of Defense
Since the enactment of No Child Left Behind, U.S. education has been dominated by test-based accountability policies and subsequent calls for data-driven decision-making (DDDM). DDDM is often framed as a method for making instruction more rational and scientific. Yet there is little clarity or consensus around the DDDM process: What data do teachers use? How do they interpret data? Though data can be used for instructional improvement, the high pressure associated with test-based accountability often results in data use that has distortive effects on teaching and learning. In this dissertation, I build on the literature on educators’ data use in practice to investigate the tensions between test-based accountability policies and instructional improvement.
In Paper 1, I examine the existing data use literature to identify distortive data use practices and offer recommendations for using evidence of student learning in more responsive ways. Paper 2 is an analysis of the ways that test-based accountability policy shapes the data use practices of a middle-school mathematics teacher workgroup. The logic of accountability policy constrains their data use practices in ways that reinforce systemic oppression and limit opportunities for more equitable instruction. In Paper 3, I analyze the epistemic underpinnings of teacher’s data use through a comparative case study of two middle-school mathematics teacher workgroups. The workgroups take different epistemic stances on data, which shape their data use practices, what they consider evidence of learning, and the instructional responses they design. The educators who use data as an indicator of learning are better positioned for instructional improvement than those who use data as a measurement of learning.
These analyses inform the development of more productive data use practices. Despite the various calls for DDDM, there are few efforts to prepare teachers or instructional coaches to engage in nuanced discussions of data. By identifying potential pitfalls of data use and articulating ways to use data for instructional improvement, I provide recommendations that can support more ambitious and equitable instruction.
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