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An Exploratory Study of Data-Driven Decision Making Supports in a Northern California School District

  • Author(s): Tjen-A-Looi, Raymond
  • Advisor(s): Gerber, Michael
  • Conley, Sharon
  • et al.
Abstract

This exploratory research study employed a mixed methods research design to examine the data-driven decision making supports of data system infrastructure, analytic capacity, and data-use leadership from the perspective of the DDAs (district data administrators that oversee the provisions of data-driven decision making supports throughout the school district) and from the perspective of school and district personnel (teachers, school and district administrators, school support staff, and district staff that actively use data-driven decision making toward their educational practices). Qualitative data were collected through five individual interviews of DDAs. Quantitative data were collected through a district-wide online survey of school and district personnel (N = 218). Qualitative and quantitative data were used together to capture the overall state of the data-driven decision making supports within the school district.

Findings indicate the district under study is still in the early phases of implementing quality data-driven decision making supports such that supports are provided, but they have limitations and are “a work in progress.” The quality of the district’s data-driven decision making supports is reflected in the perceptions of the school and district personnel. On average, the school and district personnel were between somewhat disagree to somewhat agree that the district provides quality data driven-decision making supports in the three areas of data system infrastructure, analytic capacity, and data-use leadership. The findings also show predictive relationships between data-driven decision making supports and data-driven decision making processes, indicating the importance of having quality data-driven decision making supports. The findings of the study also highlight notable considerations for implementing quality data-driven decision making supports such as implementation phase, district size and breadth, organizational structures, and time.

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