Many kids like playing sports more than they like learning math, and I’m starting to understand why.
1. The learning environment invites infinite expressions of thought.
Consider basketball. There are functionally infinite ways to shoot a basketball.
Choose your spot on the court. Choose your velocity. Choose your angle. Choose your spin. Choose a bank shot or net. Choose choose choose.
2. The learning environment returns useful information to the learner for each of those expressions.
Whatever shot you take—literally any shot—the world around you immediately returns volumes and volumes of useful information. Whether or not the ball goes in is one of the most important and interesting pieces of information, sure, but there are others before and beyond that one.
Does the ball travel in line to the hoop? Does it peak where you wanted it to? Does it hit the backboard where you wanted? Does it hit the rim? Does it bounce away left or right? At what speed does it bounce away? Did your spin have its intended effect?
There are infinite ways to shoot a basketball, infinite ways to shoot it successfully, and even unsuccessful shots are interesting.
3. The learning environment lets you try again.
The fact that you can try your shot again, often from the same exact spot, adds enormous incentive to learn from your missed shots. You can put your new learning to immediate use.
By contrast: a lot of math software.
In a lot of math software, you’re allowed four possible thoughts. If none of them reflects your thoughts, you’re unlikely to feel the same agency someone feels when they play a sport.
And after you’ve constrained your thinking to one of the four options, what information does the environment return in response to your thinking? You’re either right or you’re wrong.
It doesn’t matter if we dress the “wrong” feedback up with a growth mindset message or the “right” feedback up with computer confetti. It isn’t useful feedback. It’s low information. It doesn’t help me understand “which parts of my thinking were right?” or “what should I try next time?”
The work of teaching is to attach meaning to student thinking.
I’m critiquing math software here because math software is what I think about every day. But it’s worth reflecting on curriculum and pedagogy as well.
If I’m evaluating any learning experience lately—math software, curriculum, or classrooms—I’m asking myself two questions:
How many different students get to express how many different kinds of thinking here?
How many of those students then get something interesting to think about in response to their thinking?
That’s it. The work of teaching is to attach meaning to as many kinds of student thinking as possible. That seems harder in some disciplines than in others and I can’t stop wondering why.
2021 Oct 6. Joe Matuch posts a video where he automates IXL exercises and asks, “Is our curriculum automatable?”
🎁 What else?
Check out the Bite-Sized Data Science Lesson Plan Competition. Submissions are due October 29 and I'm very excited to see the finalists here.
My favorite tweet thread of the last month dives deep into the One Laptop Per Child Project, an initiative that took the hype-per-unit-of-actual-impact ratio to heights previously unseen.
Berkeley's got shirts. I think I need convincing that the trapezoid debate is any more interesting than those deliberately ambiguous arithmetic problems that float around the internet every week. I'm open, but I don't know that debates over definitions are a facet of math I'm excited to foreground.
Math Teacher Lounge, my video series with Bethany Lockhart Johnson, is releasing new episodes—including some brief back-to-school interviews with some of our favorite educators.
Desmos cracked the EdTech Top 40. Encyclopedia Britannica is in our rear-view mirror. Watch out, Wikipedia.