"Doing data" : addressing capacity for data use through professional learning
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While school districts across the nation are pressed to make better and more frequent use of a range of educational data, they have few resources that help guide the process of improving educator capacity for data use. To date, there have been few efforts to examine the intersection of professional learning and data use to better guide efforts at improving educator data use capacity. In order to learn more about how school districts attempt to meet educator needs in terms of data-related learning, and how they use policies to approach this issue, I examined the intersection of data use and professional learning in three school districts. I used a qualitative case study methodology to examine these issues, and relied on interview data from n=110 individuals across the three districts, as well as document analysis in each district, to better understand the existing structures in each context and how those structures came to be. I also utilized random sampling for some focus groups, and used a peer nomination process for other focus groups, which allowed me to identify educators thought by their colleagues to be “exemplar” data users. I found that across the districts, educators at all levels articulated with remarkable consistency a range of skills and knowledge they said were essential to good data use. Also, educators were consistent in describing the kinds of professional learning structures they thought best supported their needs as learners. However, in most cases, district structures fell short of these ideals. The districts rarely codified expectations related to the structure of professional learning or to data-related skills and knowledge in formal policy, and planning related to data use tended to be fragmented among many departments and leaders. As a result, there were many assumptions that “someone else” or another department was providing support in terms of data-related professional learning, while many times data use-related learning simply fell between the cracks. Informed by existing research and the results of this study, I posited a model aimed at supporting policymakers as they engage in planning for data-related professional learning.