A Network Connecting School Leaders From Around The Globe
Data should be in the passenger seat, it is not the driver!
By: Katie Zahedi
When we hear the call for “data-driven instruction,” visions of more scientifically appropriate and precise methods of teaching are evoked. Data-driven instruction suggests a more systematic and informed program for students, embellished by charts depicting growth and success that will just happen because we studied the relevant data. Viewing results of teaching and assessment, and then using the “data” reflectively to improve the preparation and delivery of our lessons is good practice. That said, anyone in educational leadership who calls for data-driven instruction or data-driven decision making should think twice!
Data is a record of the past, even if the past constitutes the quiz that was given ten minutes ago. I am not suggesting that we ignore data, whether based on individual or collective results. And while those who don’t know history may be doomed to repeat it, history cannot decisively tell us what to do today and neither should student data be the main driver of our educational practice. Sometimes the best decisions involve ignoring the past by moving forward with a new goal that introduces new expectations and possibilities.
Data is not a good judge of character. Hope in improved student motivation leading to success is all but drowned out by a growing infatuation with data as the key to academic success. While data makes claims seem more credible, it doesn’t often inspire people. As such, how can passionless data lead the charge of our nation in RTTT’s desperate hustle to bring test scores up to par with Finland and Singapore? It cannot; simply gaining more precise information on performance will not propel us toward our goals. Sorry for the let-down, but data does not cause growth.
In addition to the characteristic weaknesses of data in understanding the most vital human variables, it is specifically detrimental in application to outliers. Educators are charged to use data to improve performance of underachieving students, but struggling students are outliers. They are represented by data points that are not explicated by trends and expectations that cluster around means and modes. Often not understood in relation to others, students who struggle need individually driven not data-driven methods of teaching. Data cannot provide insight or wise counsel because it simply benchmarks the levels to which students worked in the past. Knowing where a student has scored in knowledge or skills assists us in finding the proximal zone of learning, but does not say anything about how to teach the child.
The other day, I noticed myself taking very different lines of action in regard to the data on two students. The cases were descriptively similar, however my processing of qualitative information about these students, constituted the reasons for different decisions on similar cases. Professional judgment is part and parcel of the skill repertoire of good teachers and administrators and we often deliberately depart from knowledge based on data in order to help students learn. Stepping away from my subjective judgment and using the data to drive me in one direction or the other would have meant that I made a poor decision for one of the two students with similar data points.
I know something about Johnny that data doesn’t know. If my instruction or my decisions are data-driven, I will often be precise, but still incorrect. Upon reviewing 88h grade Johnny’s file all the way back to kindergarten and looking at all of the charted progress on every test he ever took, it is not reasonable to put him in honors English in 9th grade. Hope isn’t reasonable. In fact, there is no data-driven reason for me to expect much from Johnny. Specifically, he doesn’t do well on tests or do his homework. He doesn’t have much support at home and he lacks confidence and teacher pleasing behaviors. As I said, I know something about Johnny that data doesn’t. Sent out of class for not doing his work, I found out that he likes poetry. After a few conversations and reading John’s poetry, I’ve decided to disregard the data on my placement decision.