In a recent post on leadership I noted that research has identified five principal practices as having a significant effect on student achievement. Among the behaviors “promoting and participating in teacher learning and development” has the greatest effect size. So, what does that mean for a school working on becoming a “data-driven school”?
First, the principal is involved. They attend the professional development and the engage in the collaborative inquiry.
Second, they should frame the problems for inquiry, help search for evidence, invite alternative viewpoints, help make collective sense, ensure action, and generate feedback
Finally, the principal should bring some expertise to the discussion and be willing to engage in the day to day work of looking at student data. This last action will take time, but have a lasting impact on the skills and abilities of the staff.
In grad school I would work feverishly to collect data, scrub the data, run statistical tests (e.g. t-tests, ANOVAs, etc.), and generate as many possible scatter plots as my Pentium I computer could handle. I would print my “results” and stare with black pen in hand to mark the compelling patterns. Without fail my advisor would come and join me for these analytic sessions. The story was the same every time: he would cast aside the statistical tests saying “there is nothing to see in those numbers” and then meticulously he would draw circles around relationships on the scatter plots that he thought looked interesting. Sometimes these circles looked like amoebas and sometimes they looked like obvious patterns I had missed. This was not the way I had learned to approach data in statistics class. However, the man I was working with had a lifetime of experience that trumped a statistics textbook (he also had more honorary Ph.D.s than I could count and had been inducted to the National Academy of Science on three continents). We would end these sessions with him saying, “I want to know more about what is going on with these cases and why variance exists.”
My advisor’s expertise and inquisitiveness always opened doors to new knowledge. Beyond that his tactic of learning with me bred skill and confidence. My advisor never taught me to analyze data. He engaged me and encouraged me to be curious. I learned by watching and practicing.
A culture of high-quality data analysis depends on leadership. It depends on high expectations for all members of the culture (teachers and administrators). Administrators must accept the mantle to be data analysis leaders. To be more prepared than their staff. Administrators need not come with the answers, but with the questions, ideas, and the willingness to engage with the information. Administrators need to nurture teachers and challenge them to be curious.