It’s most educators’ least favorite four-letter word.
Even accomplished teachers struggle with it. At a recent workshop, teachers were asked to investigate the Ten Roles of Teacher Leaders and then select their “superpowers” and “tragic flaws.” Of 50 teachers, not a single one identified data as a “superpower.” Even more telling, over half of the teachers identified data as their “tragic flaw.”
Building effective long-term habits of mind that will outlast our time with teachers is one of the most important roles of mentors and coaches. A high-leverage habit we can help them develop is how to regularly, sustainably, and effectively use data to make instructional decisions.
Data can be formative or summative. Looking at each serves different purposes in our work.
Highly-effective, ongoing data analysis utilizes formative assessments, including pre-assessments. This information guides us while students are still in the process of developing their knowledge and skill and, therefore, we still have opportunities to create the “just right” instruction for all of our students — to change our plans to meet their needs, create differentiated instruction and take learning beyond our initial plans when students are ready.
Daily formative assessments such as exit tickets, selected math problems or short paragraphs are powerful vehicles in developing sustainable teacher habits of looking regularly at data because they seem easier to tackle than a stack of summative tests. We can look at them quickly and don’t necessarily need to write on them or give them back to kids. They are simply a quick check of where students are in their learning. Helping teachers learn to look that these sets daily is a doable and sustainable practice that has big pay off.
Looking together at student work provides a common third point that the coach and the teacher can investigate. No longer is the mentor telling the teacher what s/he sees in the classroom. Instead, the coach and the teacher look at the data set as partners who are working to understand the data and plan collaboratively. This seemingly subtle shift changes the dynamic of the relationship entirely and makes a positive outcome — a change in teacher practice and student learning — much more likely.
While large scale data may seem formidable, the processes used to analyze “small data” work just as well with it. Although teachers don’t generate or know the items on standardized tests (making highly detailed analyses improbable), the data can still inform practice and provide opportunities for reflection. Large-scale data can provide a snapshot of learning, revealing patterns over time. The cautious maxim of standardized test data is, “It tells us something, but it doesn’t tell us everything.”
So how do we actually look at all this data?
Although there are many different data protocols that can help, a few simple questions can empower teachers to reflect upon exit tickets or standardized test information today.
- What is the data measuring?
- What observations can we make about the data?
- What inferences can we make about the data?
- In which areas do we have influence or control?
- What actions can we take now based upon our analysis of the data?
The following is an example of how these ideas played out in an English language arts (ELA) Professional Learning Community (PLC) in one of the schools where we coach.
- Although there are many standards in ELA, the test they were analyzing together mainly emphasized the reading standards, which narrowed the focus of the discussion.
- The results seemed to indicate that, in general, students underperformed — at first. However, a closer inspection revealed that most girls performed well, but boys underperformed.
- This information lead to a discussion of two issues they saw with many boys in this course: low motivation and frequent absences.
- Although the teachers brainstormed attendance ideas, they decided that they could more likely influence reading motivation.
- This knowledge lead them to research ways to motivate boys in the English classroom, resulting in new strategies such as increased physical movement, student choice in reading selections and more informational texts related to student interests.
With coaching support, they implemented these new strategies and continued monitoring their male students’ motivation through formative assessment and anecdotal observations.The next test showed that the reading achievement gap between boys and girls narrowed greatly. This new data further affirmed the practices they implemented and their success supported the group in continuing to use both formative and summative data to modify instruction to meet student needs.
Data doesn’t have to be scary. Start today. Use it to make incremental changes. Eventually, it can be your favorite new superpower.
Emily Davis and Kenny McKee are each members of the 2014 class of ASCD Emerging Leaders. Davis is a program director for the New Teacher Center and Silicon Valley New Teacher Project and the author of Making Mentoring Work. McKee is a literacy and instructional coach with Buncombe County Schools in Asheville, N.C. To learn more about his work, visit his website kennycmckee.com.