Data-Driven Differentiation (AKA Targeted Teaching)
Teresa Taylor-Ware, a second grade teacher at Canterbury, tells her students, “I’m going to give you a ‘tiny test’ of four to ten questions that will take five minutes and will tell me how to teach you better.”
Students know that the “tiny tests” don’t count for a grade, so they aren’t nervous about the outcome. Unlike a test that comes after completing a unit, these assessments are timed to help Taylor-Ware see more clearly how much of the material each student has mastered. The information allows her to target lessons to specific groups, either re-teaching or introducing new material where appropriate.
Scores are entered on a chart in a binder and highlighted with markers: pink indicates a serious need for intervention from a tutor, yellow indicates struggling students who will work with peer tutors, and green indicates a student who should continue to make improvements. Students whose scores are not highlighted are performing well enough to serve as peer tutors.
Ms. Taylor-Ware devotes Monday afternoons to math intervention. She preps volunteer tutors who will work with two to three students in the hallway. In the classroom, she gathers a small group of students to work on a skill they haven’t mastered. Peer tutors help another group of classmates.
“I know from the assessments what students are struggling with and group students with similar weaknesses,” explained Taylor-Ware. “That way they get the instruction they need.”
In educators’ jargon, Taylor-Ware is using a formative assessment to inform and differentiate her instruction. End-of-Unit tests then confirm what students have learned.