Crawford, V. M., Schlager, M., Penuel, W. R., & Toyama, Y. (2008). Supporting the art of teaching in a data-rich, high performance learning environment. In E. B. Mandinach & M. Honey (Eds.), Linking data and learning (pp. 109-129). New York: Teachers College Press.
Data-driven decision-making (DDDM) has become part of the school accountability and reform lexicon. The term commonly refers to policies and practices involving the use of student achievement and other data (such as attendance, course taking patterns and grades, and demographic data) to drive school improvement at the school, district, and state levels. In recent years, policymakers and administrators have come to view data-driven decision-making as having significant potential to improve education reform efforts and enhance student learning outcomes. The logic of DDDM is that analysis of data bearing on achievement and other related data will enable schools, districts, and states to identify areas needing improvement and to determine the degree of success or failure of actions taken to improve educational systems at various levels (Elmore & Rothman, 1999, cited in Massell, 2001).