If you think prediction is only for weather forecasters and Nostradamus, you may be leaving out a key part of the process of digging into your data. Predicting what we think we’ll find turns us from passive recipients of information into agents of change by getting us into a critical thinking head space. It forces us to reflect and consider what we think was successful and what wasn’t as successful about teaching and learning. Whether our predictions hold true or not, by taking the time to predict, we create an active intellectual scaffold that will support learning what there is to learn from our data. Prediction prepares us to think about not just what, but the far more important, why.
Some of you may have recently received last year’s state test results. Spend some time brainstorming what you expect to find before you examine (or re-examine) them. What kind of questions do you think tripped up students the most? What should have been a cinch? Who may have struggled most with which questions? Keep track of all the questions that arise when you compare actual results to your predictions. You may have predicted strong performance on comprehension questions based on your end of year formative assessment results, yet students systematically missed comprehension questions on the state test. This can lead to a number of new and important questions. Have others in your building seen the same pattern? Does the assessment treat comprehension differently than the state test? Is there an underlying skill required by state test assessment questions that students didn’t have? These follow-up questions may be answered by digging into other data sources, and often start meaningful dialogue among colleagues. Perhaps you suspect the students who struggled most were those with the most absences during the year. If that is your suspicion, then your data team’s next agenda item might be to look at attendance data to find out.
The process of prediction uncovers assumptions about teaching and learning that merit more exploration. Our assumptions can be challenged or validated, but first they must be uncovered. Ensure that you get the most out of your data by engaging in a real process of prediction with your colleagues. The process may sound tedious, but will pay off in the end.
Wendy Vaulton is a researcher for the Lesley University Center for Reading Recovery and Literacy Collaborative.