We integrated Visual Python (VPython) into University Physics courses several years ago as part of adopting the Matter & Interactions curriculum. However, students have consistently struggled in translating physics principles into functional VPython code. New curricular materials for the laboratory component were developed and evaluated in the Fall of 2022 to provide students with more familiarity and support through prelab questions and worksheet guides. However, evaluation of student work and surveys showed that previous coding experience was the biggest factor in student success with and perception of VPython assignments. In response, we developed additional in-class assignments for use in the lecture component. These assignments scaffold student learning as they first write out physics principles algebraically, next translate to code for implementation, and finally use their simulations and graphs to answer physics questions. Preliminary results from piloting these materials in one lecture section indicate that these new classroom activities improve student learning.

For several years, I have assigned a project where students analyze the stresses within a (model) space elevator.  This is a sophomore-level course in computational methods for physics and engineering, and the model space elevator provides a very nice example of where a computer model of a complex system can be built by starting with a simple system, and iteratively stepping through a simple analysis.  Specifically, we treat the space elevator as a stack of blocks, and analyze the 1D free body diagram for a single block, and then repeat this process within a loop.  The results of this analysis can be compared with the results in Ref. 1, and agree perfectly.  The only difference is that Ref. 1 uses symbolic integrals to analyze the space elevator, whereas my students analyze the space elevator using a discrete summation.  This assignment will be submitted to the PICUP Collection for you to access the complete details.[2]

At the University of British Colubmia, Vancouver, our second-year Introduction to Computational Physics course has been awarded an internal Students as Partners in Course Design grant to make this course more equitable and engaging with respect to previous coding experience. The core partnership is made up of the co-authors: three undergraduate students, one graduate student and one faculty member. Based on self-reported student data and course grades, we see that a lack of previous coding experience is proving to be a barrier to student success in this course. We will present on our partnership processes as well as details of the redesign of our course structure, instructional materials and assessments to meet the dual challenges of providing appropriate support to novice coders while also providing an engaging course for all learners.

Modern computational methods are increasingly becoming an essential tool throughout physics. However, their practical use within Physics is often built upon combined knowledge of computational methods and physics that are taught separately. We present a course that provides realistic, contemporary examples of how computational methods apply to physics research, and deliver this content via interactive Jupyter notebooks. This course, titled "Computational Data Science in Physics," was delivered in several different modalities from 2021 to 2023, ranging from online modules on the MITx Online platform (using Open edX), to a full semester, graduate-level course at MIT. For the online modules, we developed interactive problem graders for coding problems and organized content to promote active learning (e.g., lecture videos intermixed with exercises). Each module culminated in a Final Project, where students applied what they had practiced in previous lessons towards a recent (Nobel prize winning) data set (e.g., LIGO and LHC data). Importantly, we ensured that notebooks were accessible to learners in several formats in order to broaden the modes with which learners could engage with the content.