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Jupyter Notebooks gaining popularity in Universities for scientific computation and programming. Jupyter Notebooks utilize Python programming and are primarily used to handle large data sets. They are also used in scientific computational projects, such as machine learning. Advantages for the educational setting include: it is free to use (with a google account), its enabled to be highly collaborative (like google docs), and has the ability to allow advanced code markup including writing equations, inserting hyperlinks, youtube videos and other media. I developed six Jupyter Notebook lessons introducing high school physics students to computational physics, aimed at teaching them to graph data and solve problems. The large data sets used included the open data repository from the CMS experiment at CERN, where students can access real data to discover a particle.

AAPT SM 21

Integrating Computational Jupyter Notebook Lessons into High School Physics

jupyter notebook lessons

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technical paper

Computational modeling and video analysis are two powerful tools in the physics classroom for helping students analyze and model real-world phenomena. When we pair the two of these together in one web-based platform, students can use video analysis to construct a computational model that reproduces the data they measure from the video and gain new physical insights and understandings. Pivot interactives is one platform that allows for the integration of video analysis side by side with computational tools like Tychos, Glowscript, and Pyret.


Computational Modeling & Video Analysis in One Easy Environment

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Fun before physics

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One challenge (of many) I faced in moving my ~100-student in-person introductory physics course online was how to effectively integrate a group project into the curriculum. I needed a project that would serve as a collaborative stepping-stone to a broader view of physics, as well as a project that would be manageable to execute and grade in an online setting. Enter the team anthology, a team-based learning technique described by Elizabeth F. Barkley, Claire H. Major, and K. Patricia Cross in their book on collaborative learning techniques. I will share the constraints I faced with my particular class, as well as the adjustments to the idea I made in implementing it in the virtual Spring 2021 semester.

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Integrating Computational Jupyter Notebook Lessons into High School Physics

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