I have served my current school district as an Instructional Data Coach for the past two school years. Recently, I was lucky enough to chat with a director of instructional technology from a large, elite university. She started our conversation with the following question: “So, what is a Data Coach?” I had the thought that if someone of her background and experience doesn’t know, perhaps a more thorough explanation might be in order of this emerging role.
The Instructional Data Coach
An educator and/or instructional designer who helps other educators improve instructional outcomes by collecting, aggregating, analyzing and visualizing student data.
The above definition provides us a jumping-off point to understand this role and perhaps sounds a lot like a traditional instructional coach. In fact, this role was created at my district after a conversation with administrators about the instructional coaching cycle.
I contend that the coaching cycle as an offshoot of the ADDIE model for instructional design. It’s pretty simple at its heart: identify a problem, design a solution (using quick iterations), implement the solution, measure to see if the desired result was achieved. This is an effective strategy and is held up by considerable educational research. Awesome. But consider the following questions before you start a cycle:
- How do you know there’s a “problem”?
- How do you know when it’s “solved”?
I feel that these questions have to be answered with data. So before we engage in 6-9 weeks of coaching, we need to answer the questions above. What data shows us there’s a problem? What data will tell us when it is solved?
But shouldn’t all coaching cycles be centered around data anyway? What’s the difference?
Maybe you’re right; maybe “data coaching” is no different. Maybe I’m, say, a data-flavored instructional coach. That is totally fine. Given the emergent nature of instructional coaching in general, this is all in a state of flux. As you consider this distinction though, I ask you to check out some of the lessons I’ve learned from my time in this role. Maybe data isn’t as “integrated” and understood in the traditional coaching model as it needs to be.
Teachers and Their Data: The Warm and the Cold
If you’ve been in education for more than 1 day, you know that education is an extremely human endeavor. It’s messy, it’s emotional. It’s draining, it’s rewarding. It’s non-linear, it’s essential for success. Above all it’s complex! Being an educator requires compassion, empathy, and warmth. Data, by contrast, are objective, simple, and cold. I argue that this is why standardized tests are often villified.
“A test score doesn’t really communicate the whole learning process; the whole child.” -Us, when when viewing an undesired test result.
The objective, unfeeling numbers don’t sit right with many of us educators. Numbers don’t include the context of the students that we have as their teachers. I mean, how do we model persistence mathematically? How do we quantify Adversity? It’s just not as simple as A,B,C,D,F. The complexity of learning leads many teachers to eschew the data all together and just focus on what they feel is “best” for students.
“At the end of the day, we all just want what is best for kids.”
Right. Doesn’t that sound good? It gives you a fuzzy feeling. This statement the epitome of that warmth that is a hallmark of a good educator. I feel, however, that an instructional coach’s job (and by extension that of a data coach) is to help teachers process the cold data to help their warmth go further; to be more effective. Without this help from a coach, a teacher might wonder why “doing what’s best for kids” keeps ending in undesirable results.
Furthermore, I feel the statement above carries with it a danger that is currently coming to light. Without data, how do we decide what is “best” for kids? How do we know that we’re doing it? Is it a gut feeling? Yikes. My gut is attached a cis-gendered, white male with a moderately positive view of public education. My “gut” might not be the best consultant for a significant portion of my classroom, who exhibit a myriad gender, cultural, sexual, and academic identities. The “what’s best for kids” myth causes many of us to get stuck in a routine, often based on our own experience as students. Has the world changed since then? If only there were numerical information to put this difference in perspective.
This emotional investment I am calling “warmth” is essential to a good educator. It’s why teachers buy supplies from their own pockets, give kids second (third, and fourth) chances, stay up late grading/planning, and ensure kids in crisis get the help they need. Coaches need to foster this warmth and help teachers work through the emotions related to it as a matter of course. (This sentiment has been echoed by many of the instructional coaches whom I profoundly respect!) But educators can also benefit from a coach who can help make the complex, emotional, draining, rewarding task of teaching a little simpler with data.
The Coach We Need
It’s likely that someone in your district or building is playing this data-centered role. Maybe it’s an assistant principal, maybe its a curriculum coordinator, or maybe its a particularly tenacious statistics teacher. Someone is trying to look at this cold, hard, data and make actionable insights from it. Regardless of who it is, someone has to be trying to look and answer the simple questions: what are our problems? and how will we know when they’re fixed? Someone has to be sure that all this teacher warmth and investment is pointing in the correct direction. Your district needs at least one person (usually a bunch!) to knows what is looks like when we’re doing “what’s best” for kids and can tell you when you get there. That’s what a Data Coach does, and that’s why you need one.
Listen at the next staff meeting: who is scoffing about “statistical signifance” and “correlation vs. causation” and “sample sizes”? Who is getting flack for being too “numbers-focused” and “data-driven”? Who can believe trying a new intervention without a written hypothesis of what it will change!? When you overhear this person, approach them happily, shake their hand and say “My observations objectively indicate that you’re a data coach.”
If they are one, they’ll respond: “Well, maybe.”