A member of one school’s data team observed, “a lot of becoming a better data-facile teacher and improving your student’s learning results hinges on owning problems and following up on changes that you identify within the team model.”
I had the opportunity to travel to Duval County in Jacksonville, Florida this past summer and sit in on a two-day professional development institute scheduled for supporting data teamwork and analyzing 2012 FCAT results going into year two of the four year implementation process. Over the course of several institutes and site visits in year one, facilitators coached teachers and principals from 30 Duval elementary schools in strand and item data analysis; student work analysis; and dialogue practices. Data teams delved into school-specific FCAT results and district benchmarks from the previous five years and started enacting action plans to shift their school cultures from “data-shy” to “data-driven.”
As I talked to the teachers and administrators from the data teams about their experiences following the first year of implementation, I realized that the teams who were effectively using data all shared a few common strengths — and they were not at all the strengths I had originally anticipated — those being inherent teacher number sense; statistical facility across a team; and isolated, grade-specific pedagogical interventions. Success in using data requires investment in team crowd sourcing, ownership and cultural continuity. And while these factors may seem self-evident, in actuality, they are too often undervalued, inadequately supported, or even absent from data-driven initiatives.
What are the particular approaches that these Duval data teams are continuously using to encourage knowledge sharing? What are some of those effective, data-driven decisions team members have owned and monitored in their classrooms to improve student learning? And how does the mechanism of the data team facilitate lasting changes in school culture? Namely, how do the Duval teachers and principals tell us they’re effectively using data now that it’s back in their own hands?
The Power of Crowd Sourcing in a Bias-Free Zone
The fact that Wikipedia rivals Encyclopedia Britannica in accuracy of content indicates that crowd sourcing is very powerful indeed. When I talked to members of the Duval school teams, I quickly learned that any individual’s expertise in data was not nearly as important as a team’s ability to talk about their ideas and best practices around data. In the case of using data for better student learning, the process of making collective predictions, observations, inferences, and hypotheses in a judgment-free team environment was far more sustainable and information-rich than any single educator saying to himself or herself, “My kids aren’t understanding fractions based on my disaggregated FCAT results. I need to do something different NOW to improve my scores.”
A team from a Duval elementary school that had gone from a Florida Department of Education “C” status in 2010 to “B” in 2011 and to an “A” in 2012 shared their best data team practices from the first implementation year that led to their improved school rating. “Unquestionably, the structure and scheduled meetings of our data team motivated us to engage in constant, formative and vertical discussions which led to better accountability on our parts.” Another member of the team elaborated, “as a team, we dug into our FCAT results from the previous year and discovered that our middle grade students were encountering issues around geometry and fraction test items. We openly shared our best practices for targeting these comprehension issues. But more than that, we made the collective commitment to check in daily across grade levels to ask each other:
- “What does the state want our students to know about fractions and geometry?
- “How can we change our teaching strategies to address the content standards?
- “How can we create continuity of good teaching practices for these topics across grades three, four, and five?”
When the team’s 2012 FCAT scores arrived, the student results showed improvement on geometry and fraction test items. The first year also marked a significant morale shift among teachers.
“We’ve gotten feedback from our colleagues that the professional development that we as a team led at our school in using data was the best training our peers had ever had,” added another team member. “In year one, we created opportunities to share our knowledge of the Using Data process with our colleagues. It was not only a comfortable way to deliver professional development, but it also completely opened up the lines of meaningful communication between teachers and administrators in our school.”
With Changes Big or Small, Accountability Benefits All
Why do some changes we decide to make stick with us through a resolution and others fail before we’ve gotten them out of the starting gate? As the over 88 percent of us carrying around at least one failed New Years’ resolution know, change of any magnitude is difficult. But as I talked to Duval’s teachers and administrators about the successful pedagogical changes they saw through to completion after examining their data — I realized that making changes work — both big and small — almost unilaterally required two things: ownership and accountability.
A member of one school’s data team observed, “a lot of becoming a better data-facile teacher and improving your student’s learning results hinges on owning problems and following up on changes that you identify within the team model.” She described how her team met every other week on early release days, during which team members brought examples of student work to the team meeting and compared teaching strategies, engaged in task deconstruction of student results, and brainstormed ideas for small changes to help close the gap vertically.
In another elementary school that progressed from a “D” status in 2011 to an “A” in 2012, a member of the small data team remarked, “It is the structure of a data-friendly team that supports us as we try to implement changes to improve student learning. Before I had never felt comfortable digging into my own student data to such a degree of focus because there was no support or expertise from a group of invested colleagues to help me see a problem through.” After this team member analyzed strand data on her own student assessments, she realized her students were having issues with word problem interpretation. With the ongoing feedback of her data team colleagues, she designed a highlighting exercise to help her students thrive in high pressure testing environments when confronted with dense word problems, and wrote a selection of targeted word problems designed to illuminate an array of comprehension and linguistic challenges. And those monitored changes led to measurable results — when she shared her students’ 2012 FCAT results with her data team, her students’ problem solving had improved.
Cultivating Culture and Fostering Support
What does supporting a data-driven school culture look like for an administrator? On the final day of Duval professional development institute, principals and data coaches from every participating school met to talk about what the data process meant to them — and what they were doing to contribute to data inquiry in their schools. As these leaders contemplated their improved teacher relationships and articulated stories as to how the Using Data process positively impacted their environments, the manifestation of a data driven school culture started to reveal itself with a greater clarity — administrators getting on-board with data inquiry and allowing time, resources, and positive encouragement for their teacher-led data teams to take the reins.
One principal remarked, “Even working with a budget and district assessed needs, I was completely on board to facilitate monthly data meetings, and I worked with my teachers and allocated them time, supplies, and resources to create graphs and visual representations of their students’ performance by classroom.” She noted that she had then scheduled portfolio nights during which her teachers were able to discuss their interpretations of performance data from their visualizations. “Our teachers shared that our administrative support of the data visualization process was so important in helping them determine objective data and become comfortable in what we defined as our own immersed, data-driven school culture.”
Another principal added, “I helped my teachers design and evaluate a peer-coaching cycle to complement the Using Data cycle of inquiry. Building off of the process of collaborative inquiry into our student data, we implemented a cycle that entailed determining a specific area of concern, crafting a lesson and a rubric, gathering our student work and analyzing it together as a data team.” She elaborated that she and her team extended the peer-coaching model to all teachers at the fourth and fifth grade levels, which resulted in significant improvements in fifth graders’ math performance in 2012.
She continued, “Ultimately, success in using data requires administrative encouragement and support. When you support your teachers talking, sharing, and enacting their hypotheses and strategies for improving elementary math learning, you can’t help but see the results in your school culture.”