This is a selection of recorded workshops on getting started with Jupyter, Ocatve/Matlab, spreadsheets, Glowscript, and p5.js.
Introductory physics lectures are generally short on time and finding time to integrate computation isn't trivial. One alternative is to move the computational exercises to the laboratory portion of the course and replace a few traditional labs with computational labs covering the same material. Here, we describe our computational laboratory implementation and present two complete exercises.
Integrating computation into first year physics courses can be a challenge, especially given the students' comfort with programming. One solution is to integrate spreadsheet computation in your labs for data reduction and analysis. You can connect the language of computation with common spreadsheet terminology. Computation with spreadsheets is advantageous because Excel is widely available.
"Contemporary research in physics and related sciences almost always involves the use of computers. [...] Computational physics has become a third way of doing physics and complements traditional modes of theoretical and experimental physics. [...] almost all undergraduate students who take physics courses will use computational tools in their future careers even if they do…
Capture, Code, Compare activities combine hand-on labs with computer modeling. Students use video tracking to capture the motion of an object, code a model that reproduces the behavior of the physical system, then compare the quantitative results of the video analysis with the results from the computer model. I'll go through an example of one of my activities in an introductory mechanics course.
Once you've made the choice to use Python as the programming language in your course, you still have a lot of decisions ahead of you. I'll lay out the choices along with what I chose I why I chose it.