the world in a different light
This deck of slides are the ones I will be using for the Symposium on “Leveraging Technology for Engaging and Effective Learning” at the Singapore International Science Teachers’ Conference (SISTC) 2024 on Day 2 of the Conference (20 November). Feel free to download for your reference.
Heating and cooling curves are graphical representations that show how the temperature of a substance changes as heat is added or removed over time. They illustrate the behavior of substances as they go through different states—solid, liquid, and gas.
Heating Curve: This curve shows how the temperature of a substance increases as it absorbs heat. The curve typically rises as the substance heats up, with plateaus indicating phase changes, where the substance absorbs energy but its temperature remains constant. Check out the heating curves for water and nitrogen using the drop-down menu.
Cooling Curve: This curve is the opposite of the heating curve. It shows how the temperature decreases as the substance loses heat. Like the heating curve, it also has plateaus where phase changes occur, but this time, the substance releases energy. In addition to water, you can also see the cooling curve for ethanol.
With these ChatGPT-generated interactive graphs, users can change the rate of heat input or released from the substance. They can also read the descriptions that explain the changes in the average PE and KE of the molecules during each process.
A Geiger-Muller (GM) counter is an instrument for detecting and measuring ionizing radiation. It operates by using a Geiger-Muller tube filled with gas, which becomes ionized when radiation passes through it. This ionization produces an electrical pulse that is counted and displayed, allowing users to determine the presence and intensity of radiation.
Svjo-2, CC BY-SA 3.0, via Wikimedia Commons
This simulation (find it at https://physicstjc.github.io/sls/gm-counter) allows students to explore the random nature of radiation and the significance of accounting for background radiation in experiments. Here’s a guide to help students investigate these concepts using the simulation.
Q1: Set the source to “Background” and start the count. Observe the count for a few minutes. What do you notice about the counts recorded?
Q2: Why is it important to measure background radiation before testing other sources?
Q3: Change the source to “Banana” and reset the data. Start the count and observe the readings. How do the counts from the banana compare to the background radiation?
Q4: How do the counts per minute (CPM) for the banana vary over time? Is there a pattern or do the counts appear random?
Q5: Set the source to “Cesium-137” and reset the data. Start the count and observe the readings. How do the counts from Cesium-137 compare to both the background radiation and the banana?
Q6: What do the counts per minute (CPM) tell you about the intensity of the Cesium-137 source compared to the other sources?
Q7: By looking at the sample counts, can you predict the next count value? Why or why not?
Q8: How can you use the background radiation measurement to correct the readings from the banana and Cesium-137 sources?
Shared during the East Zone Physics EdTech Workshop 6 Aug 2024
The hands-on workshop for Physics teachers will focus on the use of generative AI to create physics simulations without the user having to write code. The collection of the apps made using AI can be accessed here and the github repository here. This deck of slides is made available here for the participants’ reference. The sample prompts that we will be using can be found at the bottom of this page.
The sample app that we hope the participants can produce will look something like this:
For your convenience, you may refer to the steps below.
STEP 1:
Open ChatGPT or any other GAI (e.g. Claude, CoPilot, Gemini)
STEP 2:
Copy these instructions and paste them into the AI.
STEP 3:
Copy the generated code using the button provided.
STEP 4:
Paste into editor here:
“Run in New Tab” to view and test the app. Download the html file once you are happy with it, or if you would like to add media objects such as images and audio.
STEP 5:
Be prepared to generate 10 or more versions! Repeat STEPS 3-4.
Debugging Options:
STEP 6:
Make the app look pretty!
STEP 7:
To embed into SLS, you will need to copy the code libraries that are used (if any) in a new .js file. The code library is a collection of pre-written code that you can use to perform specific tasks, e.g.
To make a copy of the file,
STEP 8:
Download as HTML.
Save file as index.html. This is important for SLS packages as it will automatically load the index.html file by default.
STEP 9:
Zip all the files and upload the zip file into SLS component. Follow the user guide on how to do so: https://www.learning.moe.edu.sg/teacher-user-guide/author/html5-content-development/#uploading-a-html5-zip-file-in-sls
In yet another demonstration of ChatGPT 4o’s capabilities, I generated the above simulation app without writing a single line of code myself: https://physicstjc.github.io/sls/relative-velocity/index.html
The process took 4 prompts.
Prompt 1: Create a javascript simulation in a single html file that demonstrates the addition of two velocity vectors to give a resultant velocity. The velocity of a boat travelling in still water moving across a river is shown as vertically upward. A slider will change the speed and direction of the water flow. The velocity of the river water is also shown. Finally, show the addition of the two vectors to give a third vector: the resultant velocity. Create a button for animating the boat’s movement.
Response: Here’s an HTML file with embedded JavaScript to create a simulation of the addition of two velocity vectors. This simulation includes a slider to adjust the speed and direction of the river flow, and a button to animate the boat’s movement. (Code is not included to avoid making this post too long.)
Prompt 2: Use two horizontal lines to show the shores of the river, also the starting and ending point of the boat’s journey. The boat should be represented with a pencil-like shape, moving upward from one horizontal line to another. The boat should be moving in the direction of the resultant velocity. The vectors should be represented with arrows, not just lines.
Response: Here’s the updated HTML file with the specified features:
Two horizontal lines representing the shores of the river.
A pencil-like shape representing the boat, moving from one shore to the other in the direction of the resultant velocity.
Arrows to represent the vectors.
Prompt 3: Very good! Now invert the shape of the boat vertically. Also, draw some light blue wavy curves between the two horizontal lines that are stationary when the river speed is zero but move at the river velocity otherwise.
Response: Here’s the updated HTML file with the specified changes:
The boat shape is inverted vertically.
Light blue wavy curves are drawn between the two horizontal lines to represent the river water. These waves are stationary when the river speed is zero but move at the river velocity otherwise.
Prompt 4: Keep the canvas to 640 pixels wide and 480 pixels tall. Resize everything else accordingly.
And what you see in the beginning of this post was the result.
Fleming’s Left-Hand Rule is a visual way to remember the direction of force on a current-carrying conductor in a magnetic field. In this rule, the thumb, forefinger, and middle finger of your left hand are held at right angles to each other. The thumb represents the direction of the force, the forefinger the direction of the magnetic field, and the middle finger the direction of the current.
To visualize this with GlowScript’s VPython, I used ChatGPT to generate a 3D scene with arrows representing each of these directions.
The prompts used were
LinkGenerate a glowscript python code for visualising fleming’s left-hand rule.
Use mouse or finger interactions to rotate the scene
Include a toggle to switch from the left-hand rule to the right hand rule.
