Exploring Practitioner Data Use to Support Improvement Work in EducationPremo, Anna Elisabeth (2024) Exploring Practitioner Data Use to Support Improvement Work in Education. Doctoral Dissertation, University of Pittsburgh. (Unpublished)
AbstractImprovement networks have become a popular approach to educational change. In theory, data is meant to fundamentally anchor these efforts. However, there are gaps in terms of which, when, and why data are used in practice and how they should be created to better meet practitioner needs. Across three studies, this dissertation provides an in-depth exploration of practitioner data creation and use in a focal improvement network. Study 1: This mixed-methods case study investigated different ways in which data were created and used, as well as when created data was not used and when data was desired but not available. The findings showed while improvement data were predominantly created, evaluation data were more frequently used to navigate the complex socio-political dynamics typical of improvement networks. Study 2: Building on practitioner needs identified in Study 1, this study develops an approach for creating and using evaluation data to understand the relationship between network processes and outcomes. In particular, it explored analytic techniques for probing the relationship between teacher implementation of proven instructional practices and growth in student performance. Analyses consistently showed benefits for student growth, overall and for historically underserved and underrepresented students, when teachers more consistently implemented the targeted instructional practices. Study 3: Addressing another need identified in Study 1, this study develops an approach for creating and using evaluation data to understand the overall impact of the improvement network on student outcomes. In particular, it develops and tests a coarsened exact matching approach for evaluating effects of the network on growth of student outcomes relative to a set of algorithmically matched non-network schools. Overall, many schools participating in the network showed growth that outpaced matched schools, with historically disadvantaged students benefiting especially. This dissertation suggests implications for theory and practice in terms of how data can be created in order to be more effectively used in improvement networks. Importantly, it emphasizes intentional data creation and use that minimizes associated practitioner effort while maximizing data value to practitioners. It also provides cross-validated examples of new approaches to improvement network evaluation that should be replicated in other initiatives. Share
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