Wednesday, Oct 24 and Thursday, Oct 25, 2018
Today, students tested their buggy collision predictions. For the last week, they’ve had access to the buggies to measure speeds. Since Monday, they’ve had their buggy starting positions.
The course was about 6m long:
Students developed a variety of models to predict the collision location. We put an emphasis on the models agreeing:
Students walked into class with a prediction for a collision time and location. We had them put down a target on the floor and coordinate with their project partner to launch the cars at the same time (harder than it sounds). Here a few collision videos:
In retrospect, the release timing is so important I have a few suggestions:
- When assigning start positions for the buggies, try to set all with approximately the same ∆x. This will make it more fair for everyone.
- Allow do-overs if kids obviously release their cars at different times. I hate that all that good physics work can get erased because the cars get released at slightly different times.
Kids left class with instructions to produce a video to present their models of the collision. These videos are due on Monday.
Tuesday, October 2, 2018
So after running the buggy lab yesterday, I knew we needed to check in on what the students understood. The following warm up question worked great:
Note: tick refers to a metronome.
I’d guess 80% of the students got the 9th position of the buggy correct and were able to reasonably describe how they found an average ∆x from the provided data.
Below are a couple of the more elegant solutions. Here’s a pretty textbook solution:
Smart approach to find x9 from x6:
Monday, October 1, 2018
Today, students ran the constant velocity buggies across the classroom to collect position and time data.
- The metronome in the background is running at 40 ticks per minute. It was our intent to run the metronome at two different rates but we ran out of time in the 45 minute class period.
- Our giant tape measures were clutch — automatically earning us non-zero starting positions AND half the cars in the room ran in the negative direction.
Friday, September 29, 2018
This year, my team and I are teaching Physics 1 (9th grade) through Computational Modeling. This curriculum uses Pyret, a language developed in part to help students learn math and science.
Today, we asked the students to think concretely about a square’s perimeter.
One kid asked why we’re learning to code. Clearly I hadn’t answered this question adequately the last few times, so I showed a computational model from next week that models a buggy moving at constant velocity. It’s familiar and they can see where their work is headed.
So, back to the square, I asked:
Then we wrote a Pyret function to find the perimeter of a square. Students worked off a Design Recipe on paper before going for the computer.
With everyone computing some solid perimeters, we turned our attention to finding the area of a square. I assigned writing functions to find the perimeter of a rectangle and the area of a rectangle for homework.