## Day 19: Science Under the Sea!

This is from Friday, but I didn’t download the photos from the camera until today. Last Friday, after school, Physics Club welcomed 42 junior high students for a ten-week Science under the Sea project. Over the next ten weeks groups of junior high students with high school mentors will design, construct, and test underwater remote-operated vehicles (ROVs). The Naperville Education Foundation (NEF) awarded us a grant that made this opportunity possible. It is going to be an amazing ten weeks for all involved!

## Day 18: JUnit in BlueJ

Today, in AP Computer Science, I introduced JUnit-based unit tests in the BlueJ IDE. BlueJ makes it very easy to create unit tests and interactively generate test methods. I’m trying to emphasize test-driven development more this year, and BlueJ’s integration with JUnit will definitely help!

## Day 17: CVPM Lab Whiteboard

Today groups presented their whiteboards for the constant-velocity paradigm lab. Due to some guidance from me, each group had a different graph to share. By the end of class, we had six whiteboards on display that we had compared and contrasted.

## Day 16: Propagating Uncertainty for Linear Regressions

I know I’ve been focused a lot on uncertainty lately, but I wanted to share a breakthrough that I shared with my students today. At this point in the year, students are comfortable with estimating measurement uncertainty and calculating it in a couple of ways. They are also comfortable with using the crank-three-times method to propagate uncertainty through a calculation. In previous years, this thread has unwoven once we started determining velocity from a position vs. time graph. What is the uncertainty of the slope? However, this year, I found a way to have LoggerPro calculate the uncertainty of the coefficients of the linear regression! While I had hoped this could be done from the specified error bars, I instead had to enter all trials into LoggerPro and then edit the Linear Fit Options to show the uncertainty. Now we can continue to focus on measurement uncertainty as we build our various models throughout the semester! I created some sample data to illustrate this technique with my class today:

##expdesign

## Day 15: Projectile Lab Practicum

My AP Physics B class had their first lab practicum today. They had to hit a constant-velocity buggy on the floor with a projectile fired from a launcher on the counter . They characterized their system but were not provided the target distance at which to hit the buggy until today. We explored computational modeling as a tool to solve for the angle. Most groups were successful in that the projectile hit the ground at the proper distance, but they were off a little left or right of the buggy. One group, however, had a direct hit! (Playback is sped up at the beginning and filmed at 120 fps at the end.)

##cvpm ##capmĀ ##practicumlab

## Day 14: Exploring GridWorld

The first summative programming lab in AP Computer Science is to create objects within GridWorld. Students add Actor objects to the world and once they have the basics down, the are free to take their lab in a creative direction. Here’s what one student had by the end of class (he figured out loops).

## Day 12: The Paper Challenge

At the first meeting for new members, the Huskie Robotics team split into groups of returning and new members and competed in the Paper Challenge. The Paper Challenge is an activity some of our team members learned from another team, Winnovation (Team 1625) at one of their workshops. The basic idea is to build a structure to support an egg as far from the floor as possible and build another structure which will allow the egg to roll as far a distance as possible. Tape is allowed, but if tape is not used points for the first structure are multiplied by four. It was a lot of fun and since we had 45 potential new members at the meeting, we had a lot of groups! Here is one of my favorite structures:

## Day 11: More Measurement Uncertainty

I realize that yesterday’s entry was about measurement uncertainty and was a histogram, but this is too cool not to share. I didn’t take a look at the data for the measurement uncertainty activity involving the elapsed time of a cart on a ramp. When we setup this activity, we gathered all of the data in a single spreadsheet, but we setup two stations (two ramps, two cars, two set of photogates) in a similar manner (same starting position of cart, same position of photogates, similar angle of inclination). The angle of inclination wasn’t set as precisely as I could have set it. When I saw the histogram this morning and when I shared it with the students during class today, we all had the same insight: each of the two peaks corresponds to the two setups! I thought this was an awesome example of the insight large sets of data can provide when analyzed.

##expdesign

## Day 10: Measurement Uncertainty

Today, Honors Physics explored measurement uncertainty through a series of activities. Tomorrow, I’ll share this aggregated data from all six sections and we’ll discuss how a large data set can be used to specify the uncertainty. This histogram shows the measurements of how long a “light” in a computer program is lighted: 5.418 +/- 0.064 s

##expdesign