This is part of a blog series about why data matters in education. Why it matters to students, to parents, to teachers and to administrators and how each group finds value from it in different ways. You can find the whole series here.
There is no shortage of data points provided to teachers about the students in their classrooms. On any given day, large numbers of teachers have access to basic demographic information, student schedules, current grades, interim and/or formative assessment scores, attendance history, etc. The list goes on, across many screens of a wide variety of student information systems and other teacher focused data tools.
When we talk about teacher data use, then, a common underlying presumption is that teachers sit down at regular intervals with the data at hand, figure out how best to make sense of what they see, and then go about the business of adjusting instruction accordingly. The data is driving the decision–making. Which is to say, that the particular data points available as answers define the universe of questions that teachers might ask and the range of instructional adjustments they might therefore make.
What if this is backwards?
Dylan Wiliam, a noted expert in formative assessment, argues instead for “decision-driven data-collection.” Rather than gathering data first to make an unknown set of decisions later, we should start with the key list of decisions to be made in a particular classroom with particular students and then collect data specifically for these decisions. Although Professor Wiliam speaks specifically about formative assessment practice, I believe this insight has value to the use of education data in general.
How might a teacher’s data analysis be enriched if given the opportunity to bring their own research questions to the table when making instructional decisions based on data? Let’s play this out ….
Data Driven Decision Making: Ms. Smith keeps a copy of her distractor analysis from the most recent district interim assessment on screen as she creates her weekly lesson plans. She is always considering how to build in appropriate reteach time for the standards where this data set shows her students struggle even as she works to move them ahead in the curriculum.
Teachers are both more inspiring and more inspired when they are empowered as researchers, as entrepreneurs, and as co-creators of learning experiences.
Decision Driven Data Collection: As she creates weekly lesson plans, Ms. Smith identifies the key learning objective for each day and embeds data collection activities into each lesson to help her understand whether students have mastered the concept at hand. She then considers how she will modify instruction in the moment based on students’ responses to these activities, and how her lesson plans may evolve over the course of the week as she learns more about how students are responding to her approach.
Although a subtle one, from my perspective, the distinction is critical. In the first scenario, Ms. Smith reacts to external data generated after instruction had taken place. In the second, Ms. Smith proactively identifies her measures of success, plans to gather appropriate evidence and process for adjusting instruction in response.
Teachers as the drivers
I believe that teachers are both more inspiring and more inspired when they are empowered as researchers, as entrepreneurs, and as co-creators of learning experiences with the students in their classrooms.
In other words, when it comes to making decisions about what is best for students, teachers (with their students), must be in the driver’s seat.
Read other blogs in the series: