It’s nearing the beginning of another academic year when the educational accountability machine starts cranking out new sets of data—data to be sorted and scrubbed by so-called professional learning communities who will disaggregate the information to identify deficits and plan interventions to be responded to and evaluated in the continuous improvement cycle of teaching and learning.

On the first day of school, those of us who teach know these data by name, not just by number or item analysis or proficiency status. In our classrooms, data are students: real children with questions and longings. Until we know not only their names, but the stories behind their shy smiles and shiny, new, first-day-back clothes, we cannot truly assess their learning strengths and needs. That’s why we take the time to develop new relationships every year with twenty-something young souls to whom we give our undivided attention until we know them as fully as possible. We watch them write, listen to them read, observe them interacting with each other and solving their math problems and their friendship problems. We meld them into a childhood learning community, insisting on and creating the kind of collaboration and cooperation that we also long for in our teacher teams as we adults try to learn from each other.

Our data are three dimensional. They sometimes get sick. Some move from apartment to apartment and town to town. Their parents work two jobs and don’t see the data as much as they wish. Some data’s parents get divorced; some shout at their data to do their homework, asking, “Why are you so stupid?” Some data’s parents forget to pack a lunch or to send in a permission slip so the data can go on a field trip. Some skip data conferences or ignore the report cards the data brings home.

Other data sleep in the same bed every night and are tucked in and read to as often as possible. They get up and have breakfast and watch cartoons and talk on Skype and do homework on their own computers and go on vacation and always have a snack every day. They get tired and bored and expect learning to be easy and cry when they don’t get it the first time.

People who design high-stakes tests and common standards for academic performance know a lot about one-dimensional data that they want all on the same page: numbers on spread sheets attached to cognitive learning targets that must be met so annual yearly progress toward these targets can be made. Decision making about academic interventions that can improve the rate of growth toward these targets and that are based on the assessments these experts have designed is called “data-driven decision making” and given vaunted status by state and federal educational policy makers.

Perhaps we should invite those driving the testing and policy buses to actually ride the school bus in September. We should invite them to come to class to see who these data are and what these data are being asked to learn and master and what it feels like these days to be in school. Meantime, most teachers will continue to see their professional obligations in 3-D.