Teacher Data Literacy Week is an opportunity to elevate why data-literate teachers are so important to student success and the actions that can be taken to support teachers in building these skills. This blog is by Andrew Knips, Teach Plus Teacher Leadership Coach in Philadelphia, and reflects on what teacher data literacy looks like in practice.
This month, Teacher Data Literacy Week reminds us of the high correlation between teachers’ data literacy and their students’ academic achievement. We know that we can’t just throw a pile of data at teachers and expect improved outcomes for students; teachers need time, training, and support from administration. They also need to know that data analysis is a priority in their school.
In the last two years as a Teach Plus teacher leadership coach in Philadelphia, I’ve sat in on over 200 data analysis meetings run by teacher leaders with their grade-level colleagues. These meetings took place as part of our work to foster a culture of shared leadership in five elementary schools. Our approach aims to create more intentional, data-driven professional learning communities (PLC) at each grade-level, all facilitated by teacher leaders.
Sometimes the facilitator of data analysis PLCs led the meeting beautifully, and sometimes the meeting crashed and burned. As one teacher leader put it, “If you want the meeting to be productive, as the leader you have to very intentionally create a format for your team. Otherwise, you’ll get bogged down in the minutiae and you won’t get anything done.”
Here are some of the do’s and don’ts of data analysis I’ve learned and can recommend to others who undertake this work:
DON’T overassess. Remember that the primary purpose of assessments is to give teachers information that leads to adjusting their instruction. With teachers’ packed schedules and infinite to-do lists, there’s usually not much time to critically examine piles of student work, so be careful how many big tests you administer. DO purposefully collect student data. Consider which quizzes and short assessments will give you the best possible information to gauge student learning and give you feedback on the effectiveness of your instruction.
DON’T make up “the bar.” I often see major differences across classrooms around what teachers expect of students on a given common assessment. It’s easy to assume that we all interpret standards the same way, but it takes a lot of time and energy for teachers to internalize “the bar” and get on the same page. DO discuss norms for what outcomes you’d like to see from the data. Some of the strongest meetings I’ve been in include a round of norming, where teachers identify the upcoming assessment they will all use and then each write an exemplar student response. This process quickly stimulates conversations about any disconnect in their responses.
DON’T jump to conclusions. Possibly the most concerning form of data analysis I’ve witnessed sounds like, “Yes, we’ve brought a bunch of student work, but let’s just talk about student deficits without looking at it.” It’s not data-driven if you skip the “data” part. DO actually look at student work. To avoid talking about kids extemporaneously, structure in silent, independent time to review data. Creating chunks of independent work time is already a best practice for facilitators, and it’s particularly important here. And because it’s easy to focus exclusively on where students are struggling, also push the team to highlight students’ strengths.
DON’T stop with analysis. Without follow-up and follow-through, there’s little point to looking at student work. I’ve seen far too many meetings end before next steps are identified. And often the next steps are too vague. DO push for specific, actionable follow-up. Push for specific, actionable follow up. Stop with 5-10 minutes remaining in the meeting and give everyone a chance to share what they are going to do differently as a result of what they learned in the data. In order to move vague next steps to strong ones, facilitators may need to ask follow up questions like, “When?” “Who?” and “What specifically will that look like?”
Data literacy means committing to adjusting teacher practice. Teams that watch out for common pitfalls and implement best practices will have more productive meetings and see better outcomes for students. And if you find yourself falling into some of the “don’ts” along the way, take a deep breath and remind yourself that it takes time to fine-tune data literacy skills. Keep practicing; the more you refine your process, the more you, your colleagues, and students will benefit.
This blog is the second in a four-part series published in support of Teacher Data Literacy Week. See our first blog for more on the value of teacher data literacy to student success and look for our third blog coming tomorrow about how one parent partnered with a data-literate teacher to support learning at home.
This blog is also available as a story on Medium.