By Deb Dunbar, Elizabeth Soggs, Lisa Zettle


Data-Driven Decision Making:


5 BASIC CONCEPTS FOR SCHOOL IMPROVEMENT AND TECHNOLOGY PLANNING


1. DATA-DRIVEN DECISION MAKING You may have heard the term “data-driven decision making” a thousand times, but what does it really mean in practice? A Google search of the phrase yielded 1,910,000 results within a fraction of a second! Generally speaking, data-driven decision making is a system of beliefs, actions and processes that infuses organizational culture and regularly organizes and transforms data to wisdom for the purpose of making organizational decisions (Preuss, 2007).


In school improvement, data-driven decision making uses student assessment data and relevant background information, to inform decisions related to planning and implementing instructional strategies at the district, school, classroom, and individual student levels. “Data literacy” means that a person possesses a basic understanding of how data can be used to inform instruction.


2. WHAT KIND OF DATA?


Dr. Victoria Bernhardt has written numerous books cited at the end of this article to assist us in understanding “multiple measures of data.” Her framework gives us the ability to organize the data and make it both manageable and useable. For school improvement and technology planning, it is helpful to think of data in four distinct categories:


STUDENT LEARNING DATA Teacher-created assessments Standardized assessments State assessments Grades


ACT/SAT scores PERCEPTION DATA Parents – thoughts, opinions


Students (before & after graduation)


Teachers and staff Employers


Citizens/community members


Technology customer service surveys


Technology training needs assessments


Technology utilization and needs assessments


22


DEMOGRAPHIC DATA Age of students/staff Gender


Ethnicity


Socioeconomic level Location of home


Access to technology outside of school


SCHOOL PROCESS DATA Retention rates


Attendance rates Truancy rates


Graduation/dropout rates


Use of computer labs/ classroom technology Network speed


Network usage reports Work order reports


3. CONTINUOUS IMPROVEMENT


Data are the key to continuous improvement. When we “Gather and Get Ready,” we organize data to provide insight and focus for our goals. When we “Study & Analyze,” we are seeking data patterns that reveal strengths and challenges in our schools. When we “Plan,” we base our plans on information we’ve learned. When we “Do,” we implement activities and collect data that will tell us the impact of our strategies. Eventually the whole cycle begins again.


4. DATA TOOLS CAN HELP


The National Education Technology Plan calls upon states, districts and schools to establish a plan to integrate data systems; use data from both administrative and instructional systems to understand relationships; ensure interoperability; and use assessment results to inform instruction (www.NationalEdTechPlan.org). In this report, school improvement and technology are interwoven and interdependent upon one another. For example:


Data systems are about Data quality is aboutv


Aligned assessment measures are about Automated data systems help in Network connectivity helps in


getting the right data getting the data right


getting the data the right way getting the data the right way getting the data right away


In Michigan the Title II, Part D Regional Data Initiatives grant has assisted many districts across the state in making data systems such as Data Director, Pinnacle Insight, Pearson Inform and others available. These tools help to break down the walls of isolated data sets which can then be queried for the purpose of reporting out answers to specific questions, which, in turn, will inform the instructional decision- making process thereby impacting student learning (Preuss 2007).


5. RESOURCES YOU CAN USE


Never in the history of education have we had the resources and tools to engage in high quality data-driven decision making like we do today. The following resources are especially helpful in both school improvement and technology planning:


| Spring/Summer 2011


“Data Driven Decision Making” continued on page 33 |


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