Seven years ago in EL's theme issue on "Data: Now What?" (December 2008/January 2009), Rick Hess wrote a funny, but deadly serious, article called "The New Stupid" in which he warned that educators had just about come full circle in their attitudes toward data. No longer summarily dismissing data as a means for improving schools (the old stupid), many were embracing data to the extreme, citing statistics in half-baked ways to prove points they wanted to make and ignoring significant kinds of data in favor of focusing on one or two measures such as cut scores (the new stupid).
As we completed editing this issue of Educational Leadership on "Doing Data Right," I wondered how most of you readers would assess the situation today. Now that we have ever more digitized information at our fingertips, have we moved to an even higher level of stupidity, or have we learned our lessons about data? Are we, in fact, doing data right? Let's hear what some EL authors have to say about just how that is done.
Are you using data to guide improvement? Many educators are, according to a Gates Foundation study that reports that are using digital tools to gather data and guide instruction. Sixty-one percent say data use is making them better teachers. At many schools, data teams and professional learning communities meet regularly. As Amanda Datnow and Vicki Park tell us, sharing responsibility, allowing healthy disagreement, and having a solutions-oriented approach can make all the difference in how effective these data meetings are. To get the most benefit from data, Richard DuFour adds, the uppermost questions a professional learning community must ask are, Why are we gathering data in the first place? and What actions are we taking as a result?
Are you looking at the right kinds of data? Hawaii is, as Victoria L. Bernhardt reports. She describes a school that has achieved impressive results by initiating a continuous improvement cycle involving the regular examination of school data made accessible by the state. All the educators, first in small groups and then collectively, looked at data on demographics, perceptions, student learning, and school processes (practices and structures). As a result of looking at what all four types of data were telling them, and studying the links among them, educators identified a shared vision for the school and made some important changes in programs and strategies.
Still, when it comes to school performance, too many states are still operating with the equivalent of an inadequate dashboard, Robert Rothman reports. They are either measuring school performance by a single indicator—namely, state test scores—or they are combining multiple indicators into an index that provides little information about how a school can improve. He reports on some districts that measure school performance on many dimensions—for example, student achievement, student engagement, college and career readiness, school climate, parent involvement, access to basic services, implementation of state standards, and access to rigorous coursework. Choosing the crucial indicators for your school or district can transform an accountability system from one used for finger-pointing to one designed for improvement (pp. 22, 68).
Are you using data to motivate students? Caitlin C. Farrell and colleagues add another way to assess whether you are doing data right. What interactions do students have with data? Are your data walls, color-coded charts, and data challenges motivating kids or just scaring them? Is offering awards for movement up the chart helpful, or is it making kids more status-conscious? These authors provide a number of ways to make sure data help students develop a learning perspective and a mastery focus. See also pp. 34, 62, and 68 for more articles that show how data can engage students and teachers alike in personal improvement.
And, finally, are you keeping data in perspective? Both over-trusting and under-trusting data can be harmful. One is the equivalent of living in a cloud, and the other is as pointless as kicking a cloud. To use data wisely, we need to exercise human judgment. Deciding to look again at processes and structures—and ourselves—is where the workable solutions lie.
