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How do K–12 school districts build data-driven systems and utilize those systems to enhance student achievement

, The University of Wisconsin - Madison, United States

The University of Wisconsin - Madison . Awarded


Public educational institutions across the nation are being held to higher standards in the 21st Century than ever before in the history of schooling. States are requiring students to perform at higher levels and teachers are being held accountable for the learning of the students rather than simply the delivery of instruction. Student learning must be documented within the district or federal funding could be reduced or eliminated for K–12 school districts that do not demonstrate improvement. On January 8, 2002, newly elected President George W. Bush signed into law the reauthorization of the Elementary and Secondary Education Act (ESEA)—commonly referred to as the No Child Left Behind (NCLB) Act of 2001. For the first time in the history of K–12 public education, federal funding is based on student achievement. This new federal legislation is forcing school districts to meet escalating accountability requirements while continually improving student learning. School leaders are wrestling with a multitude of issues in their buildings from assessment to morale and are desperately seeking ways to plan for continuous improvement while meeting the needs of all learners, striving to attain the measures of accountability, and still respecting the creativity and individuality of the teaching staff. Now more than ever before, school leaders are faced with the need for a comprehensive global plan when inspiring the staff in their districts.

This study investigates how two rural Wisconsin school districts use data to inform instruction. The Data Driven Instructional System (DDIS) Model was used to examine how two K–12 school districts built their data-driven systems and utilized those systems to enhance student achievement. The DDIS Model is a cyclical model that is in perpetual motion and lead by the overarching goals of the school district. Through interviews, observation and focus groups conducted in these two school districts, data were gathered on each of the components in the DDIS Model. Data were analyzed that created a clear picture of how to build a data-driven system and utilize that system to enhance student achievement.


Blink, R.J. How do K–12 school districts build data-driven systems and utilize those systems to enhance student achievement. Ph.D. thesis, The University of Wisconsin - Madison. Retrieved May 23, 2019 from .

This record was imported from ProQuest on October 23, 2013. [Original Record]

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