Tag Archive | Logger Pro

Day 18: Speed of Sound

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College-Prep Physics: Today we did the traditional speed of sound lab using Vernier microphones and Logger Pro. It turns out that our 4-sided meter sticks worked great as tubes. We stood them vertically on the floor so the floor acted as the reflecting surface. We got nice data:

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We used the thermometer in the room and Hyperphysics to determine the actual speed of sound in the room. Most groups got with a few percent!

##CVPM

NGSS Science and Engineering Practice 4: Analyzing and Interpreting Data

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Day 14: To Catch a Speeder

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College-Prep Physics:  Today we went out to the lawn to film cars zooming by in front of the high school. Then we went to the computer lab to learn how to analyze the video in Logger Pro in order to determine the speed of the cars. (We set the video scale using fence posts along the road — they’re 10 feet apart.) We shared out our data to determine if there was a speeding problem in front of the school.

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It’s not even October yet, and students have already had a hand at using each of the different data collection techniques we’ll be using throughout the year: Desmos on Day 9, Logger Pro probeware on Day 13, and Logger Pro video analysis today.

##CVPM
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NGSS Science and Engineering Practice 4: Analyzing and Interpreting Data

 

Day 13: A New Approach to Colliding Buggies

College-Prep Physics: Modeling Instruction’s standard lab practicum for the constant velocity unit is colliding buggies. Lab groups take data to determine the speed of their buggy, then the buggies are quarantined and groups are paired up. Each group pair is then given an initial separation distance for their buggies and are asked to predict the point were the buggies will collide. Once they calculate the answer, they are given their buggies back to test their prediction.

It’s fun, but there are some frustrations. Groups that have poor experimental design or data collection techniques won’t calculate the correct buggy speed, which means they won’t accurately predict the collision point. Also, since only the separation distance is given, there isn’t much focus on the position of the buggy and students are less likely to use a graphical method to find the collision point. They try all sorts of equations instead. In the end, one person in the group typically does the calculations while her partners just copy her work.

This year, I decided to shy away from the calculation aspects of the buggy collision lab and instead use the activity to get students more familiar with some of the digital tools we’ll be using throughout the year.

Logger Pro: Students used a motion detector and Logger Pro to find the speed of their buggies. They learned how to select portions of the graph and how apply a linear fit. This also reinforced the concept that the slope of a position graph represents velocity. They printed a copy of the graph and taped it into their lab notebooks. Then I quarantined the buggies.

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Position, not distance: Pairs of groups were then assigned a starting position relative to an origin (marked on the floor) and a direction of motion. Careful advance planning let us have a variety of collision scenarios — some head on, some where a fast buggy catches up to a slow buggy moving in the same direction.

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Desmos: Groups were then required to model the collision scenario in Desmos in order to determine the collision point. For me, the physics is in formulating the correct models to type into Desmos, not actually solving the set of simultaneous equations or graphing them by hand. Surprisingly, there were some interesting mistakes in this stage: Some groups didn’t use the proper sign for the slope to indicate a buggy heading north/south. Some groups just used the sign from their Logger Pro graph (positive or negative, depending on whether they made their buggy move towards or away from the motion detector). And still some groups used the y-intercept from their Logger Pro graph as their starting point instead of the starting point they were assigned. Once the mistakes were realized, it was a quick fix in Desmos — much less frustrating than reworking a set of simultaneous equations. Then they tested their predictions and included their Desmos graph in their notebooks.

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It went well this way, and took about 40 minutes from start to finish. It was something that even students with weaker math/algebra skills could find accessible. Plus, there was more reasoning and discussion about what the slopes and intercepts mean and how to model the situation rather than a focus on solving equations.

##CVPM

NGSS Science and Engineering Practice 2: Developing and Using Models

 

Day 13: Fan Cart Lab

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AP Physics C: Here’s the velocity-time graph for a fan cart that received a brief push from my hand, then slows down as it rolls away from me, picks up speed as it rolls back to me, then is stopped by my hand. Notice that the acceleration while the cart was rolling away from me is NOT the same as the acceleration when it rolled back. This is because friction is not negligible and changes direction. A free body diagram for the away and return trips will show you how.

It was then put as a challenge to students to use the momentum principle and the data from the Logger Pro file to find the magnitudes of all the forces acting on the cart:

  • the gravitational force from the earth
  • the normal force from the track
  • the thrust from the air
  • the frictional force from the track

Later, we’ll create a model in VPython and check our model data against our laboratory data.

Day 167: Examining Doppler Shift in Logger Pro

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doppler2College-Prep Physics: One student is looking at the relationship between the speed of her car and the apparent shift in the frequency of the horn. She used her iPhone to video her dad driving the car and honking the horn (yay for parental involvement!). Then in school, she played back the video on her phone and held a Vernier microphone to the iPhone speaker. Looking at the FFT graphs created by LoggerPro, she was able to see the shift in pitch at various approaching and receding speeds.

TIP: We used the LoggerPro template file called “Mathematics of Music” found in the “Physics with Vernier” folder. The FFT graph will display the whole spectrum and indicate the peak (loudest) frequency. However, we found that sometimes, depending on the exact moment when we sampled the horn, the different frequencies in the horn’s spectrum were the peak frequencies, even though all the same frequencies were present on repeated samplings.

We discovered this because the student was originally looking solely at peak frequency and was finding an inconsistent pattern between car speed and pitch. So I helped her out and looked at her data. By looking at the whole spectrum (rather than just the peak frequency),  we could see see the “fingerprint” of the horn. Then we could map the shift in the “fingerprint” for different speeds.