Experiences of Learning to Code

Perspectives of Undergraduate Physics Students in 2024

Authors
Affiliations

Joe Marsh Rossney

UK Centre for Ecology & Hydrology

University of Edinburgh

Sarah Hogarth

University of Edinburgh

Polux Gabriel Garcia Elizondo

University of Edinburgh

Ross Galloway

University of Edinburgh

Britton Smith

University of Edinburgh

Published

August 29, 2025

Modified

August 29, 2025

About this site

This site provides access to research materials and outputs produced during the “Experiences of Learning to Code” project, which was run by a staff-student collaboration in the School of Physics & Astronomy at the University of Edinburgh from June–December 2024.

The site contains the following contents, navigable via the top panel.

  • Project Overview: a concise overview of the aims, methods and key results of the project.

  • Resources for Researchers:

    • Project Proposal: the original proposal submitted to the Principle’s Teaching Award Scheme (PTAS) in March 2024.

    • Interview Sign-up Survey: the Jisc survey disseminated in September 2024, which enabled undergraduate physics students to put themselves forward for interview.

    • Participant Information & Informed Consent: the combined Participant Information and Informed Consent form, which students were required to have completed prior to their interview.

    • Instructions for Interviewers: step-by-step instructions for conducting 1-1 interviews with students over Microsoft Teams, starting from the point of first contact with the selected student, and ending with instructions on how to redact and format the Microsoft Teams transcript, ready for analysis.

    • Interview Guide: the interview guide used by interviewers during interviews with students.

    • Reading List: a list of references which we found useful during this work.

  • Publications & Media: a work in progress!

  • Code & Data: this page describes and locates the various code and data artifacts produced during the project.

Authors

During the relevant time period (2024), all authors were affiliated with the School of Physics & Astronomy at the University of Edinburgh. Joe Marsh Rossney had recently completed a PhD in theoretical physics, during which time they were a teaching assistant on several different programming courses. Sarah Hogarth had recently completed a Bachelors degree in physics, where their dissertation focused on the impact of Generative AI on physics education. Polux Gabriel Garcia Elizonda was a Master’s student in physics, having also completed a dissertation on Generative AI in physics education. Ross Galloway was a Senior Lecturer and leader of the Physics Education Research Group. Britton Smith was a Reader in the Institute for Astronomy and Course Organiser for an introductory Python course taken by physics undergraduates.

Author contributions

CRediT: JMR: Conceptualisation (lead), Data curation (lead), Formal analysis (equal), Funding acquisition (lead), Investigation (lead), Methodology, Project administration (equal), Software, Supervision (of SH & PGGE), Writing - original draft. SH: Data curation (supporting), Formal analysis (equal), Investigation (supporting). PGGE: Data curation (supporting), Formal analysis (supporting), Investigation (supporting). RG: Conceptualisation (supporting), Funding acquisition (supporting), Project administration (equal), Supervision (of JMR), Writing - review & editing. BS: Conceptualisation (supporting), Funding acquisition (supporting).

Acknowledgements

The authors would like to thank Kristel Torokoff for playing an instrumental role in securing financial support for this project via the School of Physics and Astronomy. We would also like to thank Kristel Torokoff and Joe Zuntz for conversations that helped to shape this project.

Financial support

We gratefully acknowledge that funding for this Principle’s Teaching Award Scholarship (PTAS) project was provided by the University of Edinburgh Development Trust.

JMR was directly supported by both PTAS and the School of Physics & Astronomy at the University of Edinburgh. SH was supported by PTAS. PGGE was supported by the School of Physics & Astronomy through the Career Development Summer Scholarship programme.

Correspondence

  • joemar@ceh.ac.uk for enquiries related to the project, website, code and data.

Reuse

Citation

BibTeX citation:
@online{MarshRossney2025,
  author = {Marsh Rossney, Joe and Hogarth, Sarah and Gabriel Garcia
    Elizondo, Polux and Galloway, Ross and Smith, Britton},
  title = {Experiences of {Learning} to {Code:} {Perspectives} of
    {Undergraduate} {Physics} {Students} in 2024},
  date = {2025-08},
  url = {https://ExpLrnCode-2024.github.io/},
  langid = {en},
  abstract = {This site provides access to research materials and
    outputs produced during the \_“Experiences of Learning to Code”\_
    project, which was run by a staff-student collaboration in the
    School of Physics \& Astronomy at the University of Edinburgh from
    June-\/-December 2024. The study sought to understand how the
    experiences of undergraduate physics students taking programming
    courses have been changing due to the sudden availability of
    Generative Artificial Intelligence (GenAI) systems. The main inquiry
    took the form of a series of semi-structured interviews with 24
    student participants, whose experiences span the periods before and
    after the advent of GenAI.}
}
For attribution, please cite this work as:
Marsh Rossney, J., Hogarth, S., Gabriel Garcia Elizondo, P., Galloway, R., & Smith, B. (2025, August). Experiences of Learning to Code: Perspectives of Undergraduate Physics Students in 2024. https://ExpLrnCode-2024.github.io/