# I’m a screenwriter—not really

This year I’m working with Professor Mike Schatz and the Georgia Tech Physics Education Research Group, to develop a computational modeling curriuclum for high school physics that dovetails nicely with the Modeling Instruction (TM) approach to teaching physics. You can see some of the work I’ve done on this previously this summer here.

Today, during a skype session, Mike challenged me to think carefully about a couple of very interesting ideas. The first is to get students to use computational modeling pretty much right out of the gate. The first lab in most modeling classes involves studying a constant velocity buggy and figuring out the relationship between the buggy’s position and time. Mike proposed that immediately after student distilled the model from this lab that they go about trying to recreate the motion they saw in a vypthon program, which I will attach very soon. The program is fully written and shows a car moving across a screen—the student’s job would be to change around the initial conditions so that the motion matches the motion they measured in lab. Mike and one of his grad students had pretty great success trying this approach with a professional development workshop they ran this Summer at Georgia Tech.

But this immediately brought up a question of why do this? How is this helping my students to develop a deeper understanding of physics? This coupled with a fantastic series of challenging emails from Kelly O’Shea this weekend really got me thinking that I’m not really doing anything to motivate my students understanding of why we’re studying computational modeling. Mike then suggested presenting students a sort of mini-explanation of the big idea of computational modeling down to exactly how you can iteratively predict the future using Newton’s second law, and he pushed me pretty hard to give students this roadmap of the big idea very early on in class, long before they had even been exposed to acceleration, since most students do have an intuitive sense of what force and velocity are.

I wasn’t super jazzed about the idea of giving away the whole N2 synthesis on the first few days of class, when students would be unlikely to really understand it. So I then thought about it, and for some crazy reason, I said to myself, well this might be a useful place to make a video. I totally recognize the irony of this statement with my previous criticism of Khan Academy, but I offer the following two points in my defense:

- My video will focus on the why of computational modeling—it’s going really try to be a short 5 minute presentation to help students to see why VPython is different, but also how it connects to the other physics ideas they’re studying. In my estimation of the dozens of Khan Academy video’s I’ve written, Khan is almost exclusively focused on showing how to follow procedures, which isn’t the intent of this project at all. We’re going to take most of the first semester to unpack some of the ideas I mention in this video.
- And lamer than the first reason: I’ve got a script. Khan is famous for talking off the cuff, and so in an effort to distance myself I decided to write up the following script.

Introduction to Computational Modeling

I’ve made the script editable to the world, and if you are kind enough to take the time to read it, I would appreciate any feedback—it is still quite rough now.

Here’s another push: Why not do the “momentum is king” spiel at this point? Pom swapping and all that would seem to be a great way to start the semester and you can do the more formal N2L spiel later. You can still do all the same computational stuff, you just carry along the mass everywhere. At every point you need to know your current Pom state (mag and direction). Take a step in that direction. Figure out the Pom swap. Repeat. The “move” step is a little harder but it might be worth it.

Andy,

Thanks. I might try to film this—but I’m not so sure my ninth graders are ready for the full magesty of the king.