Data Driven Decision Making
What is Data-Driven Decision-Making?
Data-driven decision-making (DDDM) is a system of teaching and management practices that gets better information about students into the hands of classroom teachers. Many teachers reject the idea of DDDM because of its association with the federal No Child Left Behind Act (NCLB). This is unfortunate, because a multitude of schools and districts across the country are seeing substantial improvements in student learning and achievement as they incorporate data-driven practices. Teachers in these schools are finding that intelligent and pervasive uses of data can improve their instructional interventions for students, re-energize their enthusiasm for teaching, and increase their feelings of professional fulfillment and job satisfaction.
Data-driven decision-making requires an important paradigm shift for teachers – a shift from day-to-day instruction that emphasizes process and delivery in the classroom to pedagogy that is dedicated to the achievement of results. Educational practices are evaluated in light of their direct impacts on student learning. School organizations that are new to the focused, intentional analysis of student and school outcome data quickly find that most teachers and other instructional support staff are unprepared to adopt data-driven approaches without extensive professional development
and training.
Data-driven educators should be able to articulate the essential elements of effective data-driven education outlined in the diagram below. The five major elements of data-driven instruction are:
• good baseline data,
• measurable instructional goals,
• frequent formative assessment,
• professional learning communities, and
• focused instructional interventions.
Dr. Scott McLeod, Director
School Technology Leadership Initiative
University of Minnesota
