This was written by Maja Wilson, who taught high school English, adult basic education, ESL, and alternative middle and high school in Michigan’s public schools for 10 years. She is currently a teacher educator at the University of Maine while finishing her doctorate in composition studies at the University of New Hampshire. She is the author of Rethinking Rubrics in Writing Assessment (Heinemann, 2006).
By Maja Wilson
I would like to create my own language. I did actually, when I was 10, during my hour-long bus rides to and from school with Sarah. We created elaborate code books for translating the cryptic notes we sent flying back and forth over rows of green, vinyl bus seats.
You had to be in the know, to know what we were writing. And Lorraine couldn’t ever know. We were writing about her most of the time, how she’d pushed me on the soccer field, or how she’d slapped Sarah at the foot of the slide. So, in an act of semantic warfare, Lorraine slipped her own Top Secret! code book to Heather and Nicole and all the girls with long hair and soap opera names who would always be cooler than thou.
I no longer ride the school bus, but I still spend my days in classrooms, where I’ve worked as a teacher for almost 13 years. If I were to create my own language now, “data” would be my all-purpose curse word. It has all the characteristics of a good swear: four letters, the central harshness of the letter “t,” the power to condemn.
Of course, I wouldn’t be inventing the word myself, but would be stealing it from the 21st century educational codebook. When I started teaching in 1998, “data” was not part of the classroom teacher’s lexicon. But when the No Child Left Behind Act
was passed in 2001, it became a key term in the rhetoric that would both dominate and define an entire era of educational history.
Now, teaching itself has become redefined as generating, collecting, and using data, and learning has become redefined as the curve connecting data points. This is a fundamental
shift in how educators think, talk, and go about educating our children. Unfortunately, it is not a shift that serves anyone but the data-collectors very well.
To illustrate what this redefinition of teaching and learning looks like in practice and why we should be disturbed, let’s take a run-of-the-mill classroom situation—one of a hundred a teacher might confront on a given day. We’ll play it out first in the increasingly common data-driven classroom and then in the classroom governed by professional observation and judgment. Here’s the scenario: Sam, our hypothetical sixth grader, is trying to divide decimals. He gets six of ten decimal problems wrong.
The data-driven teacher in a data-driven school brings her class’ scores on this decimal assessment to her Professional Learning Community
(PLC), which consists of all the school’s sixth grade teachers. (Incidentally, “learning” and “community” are not terms in the 21st Century rhetoric of data, but are used strategically to lull data-leery teachers into submission.)
The teacher whose class has the highest average on the decimal assessment shares her lessons on dividing decimals with the members of the PLC. All sixth grade teachers implement those lessons, and the worksheet is given again the following week.
To make sure PLC members take their work seriously, a Data Board is posted in the teachers’ lounge: teachers’ names are listed with their students’ scores in line or bar graph form underneath. Despite the re-teaching and re-assessment, Sam’s chart is still distressingly low.
Anxious about how her curve compares to the teacher’s next door, each math teacher implements daily timed decimal dividing drills, called Mad Minutes!
right after the morning’s Pledge of Allegiance. Children who don’t pass the morning’s Mad Minutes!
are kept in from lunch recess to practice decimals lest they be left behind. (Apparently, it is acceptable to be “kept in” but not “left behind.”)
Now, if the PLC and the Data Board don’t lead to continually improving scores on state math assessments, the school is labeled a School In Need of Improvement,
and a range of corrective measures are taken, including (but not limited to): additional training for teachers in standardized testing procedures; increased standardization of math curriculum; and increased common math assessments which generate more data points for the Data Board, which has now displaced the “Reach for the Stars” poster that had been hot-glue-gunned to the cinder block wall since 1987.
Now, we must ask: Where is Sam in all of this, besides pinned to the bottom of the Data Board in perpetual anxiety? It is hard to say. No one has bothered to talk to Sam, since everyone has been so busy creating, administering, scoring, posting, and comparing all the new decimal assessments.
However—and here’s what matters to consultants, politicians, and the media—there is the appearance of progress, of a school system really taking education and continuous improvement seriously. At least something systematic and data-driven is being done! What dedicated and collaborative teachers!
Now, let’s consider Sam in a classroom where the teacher doesn’t play the data game. Her observations aren’t formed through the use of standardized tools, but she has spent years studying teaching, math, and children, and she’s met students like Sam before. She’s going to be working through dynamics that are difficult to quantify. But that’s okay, because she isn’t going to try to quantify them. Instead, she’s going to thoughtfully observe, examine, and interpret what she sees. Then she’ll figure out what to do.
Our observant teacher has already noticed that the normally gregarious Sam freezes up in class any time he’s asked to solve a math problem. She sees that when he begins his problems, he becomes quite anxious, scratching deep grooves into his desktop with his pencil instead of showing his work on the page. When she asks him to talk through his thinking, he can’t formulate an entire sentence without his voice shaking in frustration.
She wonders why he is so anxious. When she asks him how he feels about math, he says he’s awful at it and talks about last year’s math teacher, who used to yell at him when he got questions wrong. He is angry and embarrassed about how he always had to stay indoors during lunch recess because he could never finish his Mad Minutes!.
Anxiety-induced math withdrawal, the teacher knows, is more dangerous in the long run than a student who works a bit more slowly and methodically than the rest of the class. She decides that the last thing that Sam needs is the anxiety that trickles down from Data Boards, Mad Minutes!, and more frequent assessments. She encourages Sam to slow down; there will be no stopwatches in her classroom. She cuts his daily problems in half and arranges for him talk through each problem to his seatmate. She will keep an eye on him, and once she sees that he can do these few problems without freezing up, she’ll add problems back to his daily work.
She introduces Sam to a second grader down the hall who is having trouble with addition. He spends some time each day helping the second grader talk through his work, and he starts to feel like maybe he does know something about math. By the end of the year, he isn’t dividing decimals quite “on grade level” yet, but he isn’t afraid to work hard with numbers anymore.
Now, is it possible that, in a different classroom, the teacher will observe Sam carelessly, or worse, with prejudice? Yes. But let’s not pretend that carelessness and prejudice don’t exist in equal amounts in data-driven classrooms. And let’s not pretend that Sam is always (or even often!) given what he needs in data-driven classrooms as a result of the teacher’s focus on data.
But wait, can’t teachers’ observations, interpretations, and knowledge of Sam co-exist with the focus on data? Many teachers are heroically trying to preserve a balance. But they can’t co-exist in the long run. Both approaches are not only time consuming, but they require completely different and ultimately antithetical mindsets: The first is based in a distrust and dismissal of the teacher’s subjectivity and experience, and the latter based in an acknowledgment and development of it.
To pursue the first approach wholeheartedly, in other words, a teacher needs to abandon the second.
A new teacher in the data-driven system will spend so much time being trained to administer the assessments that she’ won’t have time or guidance to develop the observational, descriptive, and interpretive skills that our second teacher has worked so hard at. And these skills do require hard work and mentoring. Unfortunately, mentors who value and develop their professional judgment are being pushed out of the profession, and any time for this mentoring to take place outside the classroom is being sucked up by the focus on data in PLC’s.
I don’t expect that, anytime soon, educators will be piously reprimanding consultants and politicians who accidentally let the word “data” slip in polite company.
In the meantime, I’ll amuse myself during in-services and accreditation meetings by imagining that these consultants—modern day versions of Lorraine with their talk about data-driven instruction and what’s the data telling us and becoming consumers and producers of data—actually suffer from an uncontrollable—Data!—urge to—Data!—curse.