Thinking back on the teacher education courses I took in college and graduate school, I don’t recall a single professor discussing the importance of data-driven instruction or what implementing it looked like in a classroom setting. I can’t recall reading any research-based or pragmatic approaches for what a teacher should do after developing and administering a test, multiple-choice or not.
Analyzing data is certainly not the easiest, most glamorous, or quick endeavor for a teacher. However, it’s the only way to ensure that instruction is relevant and skills that need remediation are retaught in a different way.
I first became familiar with data directing what a teacher teaches when reading excerpts of Driven By Data by Paul Bambrick Santoyo, a text devoted to detailing, explaining, and providing examples of teachers making data central to their daily practice.
I worked at a charter school where we had weekly data meeting that required teachers to prepare a data sheet analyzing questions and skills students struggled with most, so we could prepare a re-teach plan with our coaches. We epitomized data-driven. During my first semester teaching there, this was a new challenge for me and the school because I taught a writing composition class, which meant I had to develop a system for identifying student strengths and weaknesses without compromising the organic nature of a writing workshop. I used rubrics provided by the state and developed by the school I worked at to grade student work within the four traditional categories off which we assess writing: development, focus/organization, conventions, and language. The problem was that each category was so vast that there was no easy way to understand what students struggled with specifically in each assignment.