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Coding with Blockly: Displaying a Smart Cart’s Velocity Vector

Today I got to work through an experiment using PASCO’s Wireless Smart Cart and Blockly code on SPARKvue.  I followed the Blockly Extension: Vector Display lab from the PASCO Experiment Library. This lab guides you to use Blockly code to display text on the screen depending on the speed of the Smart Cart.

I connected the Smart Cart through Bluetooth to SPARKvue and read through the lab procedure. I started off by slowly moving the Smart cart along my desk while observing the velocity graph. I conducted three runs, one to determine a low velocity, a medium velocity, and a high velocity. I took note of these three velocities, as shown in the image on the right, so that they could be included within the code. After getting familiar with the lab, I copied the example code, adjusting the velocity values to the ones I recorded, as shown in the image on the left. I tested my code by clicking start and moving the Smart Cart. At first, I was not sure where to look for the displayed text. I realized I had to change my display from a graph to digits. Then, by clicking the variable being displayed, I switched from Sensors to User-entered and chose Velocity Vector (the variable I created in the Blockly code). This time when I pressed start, the vectors I assigned to each velocity displayed on the screen depending on the Smart Cart’s speed. I decided to change the text displayed from vectors to words. As shown in the video below, I used simple terms such as slow, medium, and fast to describe the carts’ velocities.

I found this lab super cool! It was my first time experimenting with the Wireless Smart Cart using Blockly code and I am looking forward to coding more products.


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  • During distance learning due to COVID-19 school shut down, I was given a short window to collect what I could from my classroom to teach online. The PASCO wireless sensors and Smart Carts were my top priority to collect to implement distance learning. By sharing experimental data with students via SPARKVue, the sensors were pivotal in creating an online experience that still allowed students to grow with their lab skills. It was easy to record videos of the data collection and share the data with my students. They did a phenomenal job examining and interpreting the data.


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