This paper uses RAND research to show how schools and districts are analyzing achievement test results and other types of data to make decisions to improve student success. It examines data-driven decision-making (DDDM) policies and suggests future research in the field. A conceptual framework, adapted from the literature and used to organize the discussion, recognizes that multiple data types (input, outcome, process and satisfaction data) can inform decisions, and that the presence of raw data does not ensure its effective use.
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