Factors that influence the collection, organization, and analysis of data in a small rural district: An explanatory sequential mixed methods study



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The use of data-driven instruction by teachers has risen in the past two decades with the passing of accountability mandates by the federal government. Data use, or data-driven instruction, has mostly been studied in the context of large, urban schools. Through these studies a series of promoting and hindering factors that influence the implementation of data-driven instruction have been identified; time, leadership, training, data literacy, comfort (self-efficacy), data coach/expert, and a school data culture. There is limited research for data-driven instruction in the context of a small, rural school. The purpose of this research was to examine if the seven factors identified in large, urban school influence the perceived success (from the teacher’s perspective) of their PLC team in collecting, organizing, and analyzing data in the context of a small, rural school district. This study used a mixed methods explanatory sequential design with the follow-up variant to collect data through a quantitative digital survey, teacher interview, district document analysis, observation, document analysis, and follow up focus group with a PLC Team, administrative interview, high school leadership team qualitative survey, and focus group with the 6-8 mathematics team. This study found that the seven influencing factors identified in large, urban school influence small, rural school’s implementation of data-driven instruction, but that influence was compounded and extrapolated by the characteristics of rural schools. Further, it was found that the seven factors are extremely dependent on one another. For example, if there is leadership that provides the needed training, then data literacy, comfort, and school data culture are improved.



data-driven instruction, data use, teacher