Data-driven instruction may be the popular catchphrase in education, but in a recent edWebinar, the speakers advocated for data-driven learning. The student, they said, should be at the center of all educational efforts, especially when the goal is to improve outcomes. “Using Student Learning Data to Foster a Growth Culture,” featuring Amy Trees Dodson, M.Ed., director of instruction, Cisco (TX) Independent School District (CISD); David Woods, director of curriculum and reporting, DreamBox Learning; and Robyn Sturgeon, professional learning consultant, NWEA, focused not just on the idea of collecting data, but on collecting only the data that is actionable. Instead of teaching to the middle, they said, educators and students can use data to attack learning.
Using the CISD framework as an example, the presenters discussed six key phases for developing buy-in and creating a successful data-oriented learning system.
Phase 1 – Think big
First, do an honest evaluation of your programs. What assessments do you use now? What data do you have, and what are you missing? What problems exist where more specific data would be helpful? This is the opportunity for stakeholders to brainstorm about how they can use data to make a difference.
6 steps for creating a successful data-oriented learning system #edtech
Phase 2 – Train well…and then train again
While professional development seems like an obvious step, the speakers emphasized that this is a never-ending process. Although some training can be done in large groups, most should be small group work, possibly even one on one. Here, teachers need to understand that what gets assessed gets discussed and learn how to incorporate the information into their lesson plans.
Phase 3 – Action
The point of using data to drive learning is to change what is happening in the classroom. Without action, it’s meaningless. In addition to the teachers looking at the data, students benefit from understanding their own performance. When developing student improvement plans, however, don’t make time the constant and limit how long students have to learn a concept or make progress, commented Woods. In data-driven learning, time is the variable and achievement is the constant, which keeps the focus on individual student success.