Today, some districts across the country are practicing data-driven decision making techniques not only to analyze test scores and student achievement, but also to narrow achievement gaps between student subgroups in order to improve teacher quality, to improve curriculum and to promote parental involvement and parental collaboration in the education. Data-driven decision-making (DDDM) is a system of teaching and management practices that enables classroom teachers to obtain more accurate information about their students. In its most basic form, data-driven decision making is about collecting appropriate data, analyzing that data in a meaningful method, getting the data into the hands of the people who need it. Using the data is important to increase school efficiencies and improve student achievement and communicating data-driven decisions to the correct professionals.
The author states that data can be a powerful tool for districts because knowledge is power, and there’s nothing more powerful than data to help district and school leaders develop a solid blueprint with measurable results for continuous improvement. Through the proper use of data, districts can:
Do an effective analysis of student achievement data can help Superintendents understand which instructional strategies are creating the best results and see where additional training might be needed.
Narrow achievement gaps. Data provides quantifiable evidence, taking the emotion out of what can be tough calls for superintendents and school boards.
Improve teacher quality. Districts can employ data-driven decision making systems to bring to light specific and targeted professional development needs of district staff
Analyze performance data with an effective data-driven decision making system
Data helps districts and administrators see things they might not
When is examined from all angles, it may highlight a program that, although popular, is not helping students learn.
Data can help drill down to the root causes of a problem opportunity for staff to learn from each other.
A data-driven decision making system allows administrators and teachers to adopt a proactive approach to curriculum design and development
At present, diverse districts across the country are employing data-driven decision making techniques not only to analyze test scores and student achievement, but also to:
• Narrow achievement gaps between student subgroups
• Improve teacher quality
• Improve curriculum
• Share best practices among schools and districts
• Communicate education issues more effectively with key stakeholders
• Promote parental involvement in the education process
• Increase dialogue within the educational community
Good intentions are no longer enough to direct the curricula of today’s schools.
Data Driven Decision Making is diverse, 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 types to be used by the central office, administrators and teachers, all with different roles and perspectives in education. According to the author, the process data may provide reports such as financial operations or quality of teacher instruction within the schools. The outcome data, which is usually used to drive curriculum design, classroom instruction and test structure, may be data that reports dropout rates or student test scores. These types of data are methodically collected throughout the year to make decisions within a school district. (Source www.erdc.k12.mn.us/promo/sage/images/Analytics_WhitePaper.pdf)
After the data is collected, it will then be used to make a variety of decisions. Data can be used to identify, clarify, inform, or it can be used to act in educational situations. Frequently, DDDM is being used to determine the student’s level today and what it takes to get them to reach the curriculum standards. DDDM can be helpful when identifying patterns of outcomes within a school district. DDDM provides accountability for schools, which is an advantage to implementing data driven decisions in a district.
Lately, DDDM helps teachers to collaborate and work together when discussing the data collected within the classroom. This type of collaboration is what builds empowered teachers. Certainly, the teacher may generate more interest to study within the students. Students are given appropriate opportunities to learn because differentiation of instruction and learning styles are identified by the data collected. “School reform is the ultimate goal of school reform laws and the rules, policies, and procedures for implementing them. Federal and many state laws require schools to have school improvement plans and to set goals to improve student achievement of standards. Goals for improvement are based on state and local assessment results and the indicator systems of which they are a part. These results reveal overall learning, conditions that affect information; the school determines what needs to be improved, who needs to improve, and how that improvement might be accomplished.” (ael.org/dbdm)
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 not be worthy. 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. Data-driven decision-making (DDDM) is a system of teaching and management practices that enables classroom teachers “Access to the raw data is crucial, because educators invariably want more detailed data, or want data presented in different ways, than paper reports typically provide.” (McLeod, S., 2007)
In regard to the cons some educators dislike the idea of DDDM because of its connection with the federal No Child Left Behind Act (NCLB). However, there are is plenty of research that supports the powerful tool that DDDM can be in the educational field. Also there is are numerous school districts across the country that are seeing substantial improvements in student achievement and learning as they incorporate data-driven practices. “Most importantly, building-level administrators must actively help teachers identify key indicators of classroom success, appropriately analyze their data, and then turn those data into strategic pedagogical interventions.” (McLeod, S., 2007)
Data Driven Decision Making is a process that consists of collecting data and using that data to inform and guide decisions. Those decisions help to improve teaching and student learning. Data can provide useful information within and across classes and schools in formats that educators at all levels can quickly use to determine best practices. In its most basic form, data-driven decision making is about collecting appropriate data, analyzing that data in a meaningful fashion, getting the data into the hands of the people who need it, using the data to increase school efficiencies and improve student achievement. A data-driven decision making system allows administrators and teachers to adopt a proactive approach to curriculum design and development. Certainly, there is plenty research that support the powerful tool that DDDM can be in the educational field. Also there is a variety of school districts across the country are seeing considerable improvements in student achievement and learning as they incorporate data-driven practices. It is important to notice that professional’s who use DDDM should be trained to acquire important data and use it in an accurate way with the objective to make the student succeed in the present and in the future.
References & Resources
Kadel, R. (2010). Data-Driven Decision Making--Not Just a Buzz Word. Learning & Leading with Technology, 37(7), 18-21. Retrieved from EBSCOhost.
Means, B., Chen, E., DeBarger, A., Padilla, C., Department of Education (ED), O., & SRI, I. (2011). Teachers' Ability to Use Data to Inform Instruction: Challenges and Supports. Office of Planning, Evaluation and Policy Development, US Department of Education, Retrieved from EBSCOhost.
Davis Bianco, S. (2010). Improving Student Outcomes: Data-driven Instruction and Fidelity of Implementation in a Response to Intervention (RTI) Model. TEACHING Exceptional Children Plus, 6(5) Article 1. Retrieved [date] from http://escholarship.bc.edu/education/tecplus/vol6/iss5/art1.