Data-Based Decision Making
September 6, 2012
According to the Data Quality Campaign of 2011, every state now has the opportunity "from parents to policymakers, to use data to inform decisions that will improve student outcomes and system performance" (Data for Action, 2011). Studies have indicated that 36 states collect and provide information in regards to students past performance, and 33 states actually produce reports that measure students' individual growth over time.
The findings have further demonstrated that most states are hesitant to make their data publicly known. "Skepticism about the quality and use of data continues because data previously were commonly used to punish rather than inform" where improvement is needed or revealed (Data for Action, 2011). Most states have also yet to realize that test scores are not the only form of data that needs to be considered important. The most useful data includes: 1.
Longitudinal - following students over time
Actionable - timely, user-friendly and meaningful
Contextual - robuse, comparable and presented as part of the bigger picture 4.
Interopirable- matches, linked, and shared across sustems and sectors The Data Campaign conducted a study which concluded that there were several challenges faced by the states. First and foremost, the majority of states do not have capacity to prepare students for jobs due to the lack of understanding of needed connection between jobs and education. Second, states have not established a system to tackle time and trust issues, in addition to identifying critical questions of how to progress further. Finally, educators need training and professional development to be "data literate." These challenges need to be remedied in order to see positive change. (Data for Action, 2011)
"One danger in the current environment is that we lose sight of the fact that decisions are judgments about what needs to be done within a particular set of circumstances and in light of a school’s local mission and shared values. For example, if a school discovers that its testing program and curriculum are misaligned, it could embark on one of four possible paths: do nothing, change the curriculum, change the tests, change both" (Secada, 2011). Whatever the path chosen would be determined based on local circumstances, as well as judgments made by staff and administration. Any data collected should be used solely to guide and inform the decision on what to do, which still requires staff to meet and discuss the best course of action for improvement and success.
In 2001, Milwaukee teachers from six schools provided their insight of what they thought was the true definition of "data": the idea that there's more to it than just head counts, numbers, and test scores. The volunteer team members at each school worked collaboratively to collect data, learn different decision-making models, manage and interpret the data, while also how to effectively apply change. The volunteers admitted right away to lacking the necessary tools and training to collect, analyze, and interpret data; however, after the study the training proved to be successful. Team members walked away with a new course of action for improving students' reading scores by reallocating school resources, identifying low performing students to receive additional resources, and also by hiring two new reading specialists for the 2001-2002 school year. The members also planned to keep track of these plans to see if reading and student performance actually did improve. (Mason, 2001)
Most studies will indicate that one of the most sought after remedies to implementing "good" decision making processes is "taking advantage of accurate, timely, and useful data" (Picciano, 2006). According to Deborah Meir, there were several ways to improve education in the United States, including: 1.
Policymakers, teachers, parents, and children being involved...
References: Data Quality Campaign. (2011). Data for Action 2011. Retrieved on September 4, 2012 from http://www.dataqualitycampaign.org/files/DFA2011%20Mini%20report%20findings%20Dec1.pdf
Duncan, A. (2009, June). Robust data gives us the roadmap to reform. Presentation at the Fourth Annual IES Research Conference, Washington, DC. Retrieved September 4, 2012 from http://www.ed.gov/news/speeches/robust-data-gives-us-roadmap-reform
Education Northwest. (2012). Education Northwest Magazine: What the Research Says (or Doesn 't Say): Using Data for Decision-Making. Retrieved on September 4, 2012 from http://educationnorthwest.org/news/1644
Mason, Sarah. (2001). Turning Data Into Knowledge: Lessons from Six Milwaukee Public Schools. Retrieved on September 4, 2012 from http://www.wcer.wisc.edu/archive/ccvi/pub/newsletter/v6n1_spr01.pdf
Picciano, A.G. (2006). Data Driven Decision Making for Effective School Leadership. New Jersey: Pearson
Secada, Walter G. (2001). Using Data for Educational Decision Making. Newsletter for the Comprehensive Center Region VI. Volume 6, No.1. Retrieved on September 4, 2012 from http://www.wcer.wisc.edu/archive/ccvi/pub/newsletter/v6n1_spr01.pdf
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