Data Driven Decision Making

Accountability is an exercise in hope. When we raise academic standards, children raise their academic sights. When children are regularly tested, teachers know where and how to improve. When scores are known to parents, parents are empowered to push for change. When accountability for our schools is real, the results for our children are real.
—U.S. President George W. Bush (US Department of Education, 2002a)

Data driven decision making (DDDM) is a buzzword often heard in education systems across the nation. DDDM is a process that consists of collecting data and using that data to inform and guide decisions to improve teaching and student learning. “Gut feelings” and teacher instincts are no longer enough to direct the curricula of today’s schools and maintain accountability. No Child Left Behind has presented schools with no other choice but to turn to DDDM to earn incentives and opportunities to improve their curriculum and student performance. DDDM is multi-faceted, in that, there are numerous types of data collected, many uses for the data collected, and a system that is required to make it effective in school districts.
School districts may collect a wide range of data to be used by the central office, administrators and teachers, all with different roles and perspectives in education. First, input data may identify school expenditures or demographics of the student population. Next, process data may provide reports such as financial operations or quality of teacher instruction within the schools. Thirdly, outcome data, which is most commonly used to drive curriculum design and classroom instruction, may be data that reports dropout rates or student test scores. Lastly, satisfaction data would discuss opinions from teachers, students, and parents/community members. These four types of data are systematically collected throughout the year to make decisions within a school district.

Once this large quantity of data is collected, it is said to then go through a cycle. Continuously collected, data is then organized and synthesized by a school leadership team. A leadership team eliminates a heavy load on any one person. Members may include board members, administration, technical specialists, curriculum supervisors, teachers and community members. After the data is collected, it will then be used to make a variety of decisions. Data can be used to inform, identify, or clarify. For example, decisions may be made about the effectiveness of practices within the classroom, the progress made towards goals, the reallocation of resources, or whether all students needs are being met. Most commonly, though, DDDM is being used to determine student present level of performance and what needs to happen to have them reach curriculum goals.

In addition, DDDM can be extremely helpful when identifying patterns of outcomes within a school district. DDDM can strengthen a school by pinpointing successes and challenges, and evaluating effectiveness of programs. DDDM provides accountability for schools, which is an advantage to implementing data driven decisions in a district. Ultimately, DDDM helps teachers to collaborate and work together when discussing the data collected within the classroom. This type of collaboration is what builds strong, empowered teachers, which in turn builds students who have a will to learn. Students are given appropriate opportunities to learn because differentiation of instruction and learning styles are identified by the data collected. Education is no longer about the “average” student. Through DDDM teachers can reach accelerated students as well as those below grade level.
As these are all extremely strong arguments for implementing DDDM in school districts, much is to be considered when administrators and teachers are asked to utilize DDDM. As the cycle indicates, it must be organized and synthesized. Too much data means too much time demanded. Just because the data is collected doesn’t mean that it is being used effectively.

Time and money are the largest factors when discussing DDDM. Following the data collection, there is a need for time to organize, synthesize and train teachers to utilize the data within their classrooms. Without proper training in these areas, data will go to waste. Curriculum guides pressure teachers to keep to a rigorous pace in their instruction. Teachers become concerned with instructional time used to administer assessments and then re-teaching, if the data so indicates it is needed. In addition, there is so much time needed to sift through data collected, it is possible that by the time that data is analyzed students have moved on to other grade levels. Even if there is a leadership team in place all members of the team have other duties and expectations within their jobs.

DDDM demands money. DDDM requires school districts to purchase testing systems, for example. Then consultants from the testing company come in to train administration and teachers to administer and then decipher the data that is reported. Often technical support is needed and a team is formed to maintain a culture which data can be worked through constructively. Time out of the classroom must then be granted to the team members that require substitutes teachers and training for them as well.

In conclusion, our culture supports the use of DDDM, not only in education but also in politics and business. When DDDM is implemented in an educational environment the purpose for data must be clearly stated, and there must be a trained team that can teach and be advocates for the data collected. The team then must revise the data and the system by which it was collected regularly, to keep the development of the school at best. Communication is a necessary piece to DDDM to assure that data is collected, organized, and synthesized on a regular basis and in a timely manner. Then, data can ideally be used to inform and guide decisions made to improve teaching and student learning.