Teaching coding across disciplines

open science
open source
data feminism
research computing
literate computing
python
flipped learning
Coding by whom? Coding for whom? Coding with whose interests in mind?
Author

Maeve Murphy Quinlan

Published

January 5, 2025

Coding by whom? Coding for whom? Coding with whose interests in mind?

This is the research, background reading and resources related to the talk I’m giving at Teaching Programming across Disciplines at the University of Edinburgh Winter School 2025. Here’s the pdf of the presentation slides on GitHub.

Read the abstract for this talk below.

Coding by whom? Coding for whom? Coding with whose interests in mind?

Research computing competency and specifically programming skills are becoming ever more important in our data-driven world: Jacobs et al. (2016) argues that “we are rapidly approaching a point where innovations [in research] will primarily come from those who are able to translate an idea into an algorithm, and then into computer code.” With the proliferation of algorithmic approaches in every aspect in our lives (not just in research methods), it is ever more important to strive for justice and equity in programming education. According to the UK Government Digital Strategy policy paper, despite women making up almost half of the workforce, we are under-represented in the tech sector: just 17% of people who work in the tech sector and only 9.5% of students taking computer science A level courses are women (“2. Digital skills and inclusion - giving everyone access to the digital skills they need,” n.d.). This glaring disparity is also apparent for other minoritised groups, and is compounded for women of colour (Cook 2021).

As D’Ignazio and Klein (2020) succinctly state in the opening of their book Data Feminism, “Data science by whom? Data science for whom? Data science with whose interests in mind?”, this inequity has far-reaching impacts on society. In the context of teaching coding, how do currently accepted practices reinforce and uphold unjust power structures? How can we use our varied institutional power to work towards justice in digital skills? Education can both be a mechanism for empowerment and transformation (D’Ignazio and Klein 2020) or can serve to compound existing inequities (Collis 1985). In this talk, I combine reviews of teaching practices (Campbell et al. 2024; Jacobs et al. 2016; Alammary 2019), studies and projects using alternative teaching methods (Fabic, Mitrovic, and Neshatian 2018; Williams 2022; Gan and Ouh 2022; “Code4000” 2022), and personal experience in learning, teaching, and developing educational materials. I hope to prompt an on-going discussion on equitable teaching practices in programming!

Motivation and background

To read about why this topic is important to me, and to learn my personal outlook, please read my blog post.

Texts

Instead of giving my entire bibliography straight off the bat, I’m going to list a few different sources in the order I came to them. Some of these are websites, some are blogs, some are academic articles and textbooks. I will add to this as I continue to build the talk slides; please excuse any unintentional omissions. Also please feel free to continue the discussion/suggested sources here using the Hypothesis commenting platform.

Firstly, it was reading Data Feminism by D’Ignazio and Klein (2020) that solidified a lot of feelings I had that I wasn’t able to express succinctly, and hadn’t fully externalised. This reintroduced concepts to me that I had met before, but put them squarely in the context of data science (and I felt research computing too), specifically data science through the lens of intersectional feminism as coined by Professor Kimberlé W. Crenshaw (Crenshaw 1991), and then the matrix of domination described by Professor Patricia Hill Collins (Collins 2002). I also cannot recommend the book Design Justice by Costanza-Chock (2020) enough.

Texts on feminist teaching, feminist approaches to data science, coding etc.

Academia and experiences within it

Live coding

Cognitive apprenticeship and extreme apprenticeship

Blended learning models

  • Blended learning models for introductory programming courses: A systematic review (Alammary 2019)

Other papers on teaching

  • Cracking the code: An evidence-based approach to teaching Python in an undergraduate earth science setting (Campbell et al. 2024)
  • Experiences with efficient methodologies for teaching computer programming to geoscientists (Jacobs et al. 2016)

Other sources referenced in the talk

Other references include: Brown and Leigh (2020); Dupas et al. (2021); Llorens et al. (2021); Owen, De Bruin, and Wu (2024); Aragón, Pietri, and Powell (2023).

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References

“2. Digital skills and inclusion - giving everyone access to the digital skills they need.” n.d. https://www.gov.uk/government/publications/uk-digital-strategy/2-digital-skills-and-inclusion-giving-everyone-access-to-the-digital-skills-they-need.
“Addressing sexual misconduct in higher education, part one: prevention.” 2024. https://www.timeshighereducation.com/campus/addressing-sexual-misconduct-higher-education-part-one-prevention.
Alammary, Ali. 2019. “Blended learning models for introductory programming courses: A systematic review.” PLoS One 14 (9): e0221765. https://doi.org/10.1371/journal.pone.0221765.
Aragón, Oriana R, Evava S Pietri, and Brian A Powell. 2023. “Gender bias in teaching evaluations: the causal role of department gender composition.” Proc. Natl. Acad. Sci. U. S. A. 120 (4): e2118466120. https://doi.org/10.1073/pnas.2118466120.
Brown, Nicole, and Jennifer Leigh, eds. 2020. Ableism in academia: Theorising experiences of disabilities and chronic illnesses in higher education. London, England: UCL Press. https://doi.org/10.14324/111.9781787354975.
Campbell, Ethan C, Katy M Christensen, Mikelle Nuwer, Amrita Ahuja, Owen Boram, Junzhe Liu, Reese Miller, Isabelle Osuna, and Stephen C Riser. 2024. “Cracking the code: An evidence-based approach to teaching Python in an undergraduate earth science setting.” J. Geosci. Educ., August, 1–20. https://doi.org/10.1080/10899995.2024.2384338.
“Code4000.” 2022. https://www.catch-22.org.uk/find-services/code4000/.
Collins, Patricia Hill. 2002. Black feminist thought: Knowledge, consciousness, and the politics of empowerment. 10th ed. Perspectives on Gender. London, England: Routledge. https://doi.org/10.4324/9780203900055.
Collis, Betty. 1985. “Reflections on inequities in computer education: Do the rich get richer?” Educ. Comput. 1 (3): 179–86. https://doi.org/10.1016/s0167-9287(85)91519-5.
Cook, Emilie. 2021. “Promoting Equity in the Classroom with Intersectional Pedagogy.” https://www.everylearnereverywhere.org/blog/promoting-equity-in-the-classroom-with-intersectional-pedagogy/.
Costanza-Chock, Sasha. 2020. Design Justice. Cambridge, Mass.: MIT Press. https://doi.org/10.7551/mitpress/12255.001.0001.
Crenshaw, Kimberle. 1991. “Mapping the margins: Intersectionality, identity politics, and violence against women of color.” Stanford Law Rev. 43 (6): 1241. https://doi.org/10.2307/1229039.
D’Ignazio, Catherine, and Lauren F Klein. 2020. Data Feminism. London, England: MIT Press.
Del Fatto, Vincenzo, Gabriella Dodero, and Rosella Gennari. 2016. “How measuring student performances allows for measuring blended extreme apprenticeship for learning Bash programming.” Comput. Human Behav. 55 (February): 1231–40. https://doi.org/10.1016/j.chb.2015.04.007.
Dupas, Pascaline, Alicia Sasser Modestino, Muriel Niederle, Justin Wolfers, and The Seminar Dynamics Collective. 2021. “Gender and the dynamics of economics seminars.” w28494. Cambridge, MA: National Bureau of Economic Research; National Bureau of Economic Research. https://doi.org/10.3386/w28494.
Fabic, Geela Venise Firmalo, Antonija Mitrovic, and Kourosh Neshatian. 2018. “Investigating the effects of learning activities in a mobile Python tutor for targeting multiple coding skills.” Res. Pract. Technol. Enhanc. Learn. 13 (1): 23. https://doi.org/10.1186/s41039-018-0092-x.
Gan, Benjamin, and Eng Lieh Ouh. 2022. “Designing flipped learning activities for beginner programming course.” In Proceedings of 28th AMCIS: Innovative Research Informing Practice, Minneapolis, 2022 August 10-14. AIE eLibrary.
Jacobs, Christian T, Gerard J Gorman, Huw E Rees, and Lorraine E Craig. 2016. “Experiences with efficient methodologies for teaching computer programming to geoscientists.” J. Geosci. Educ. 64 (3): 183–98. https://doi.org/10.5408/15-101.1.
Lee, Meggan J, Jasmine D Collins, Stacy Anne Harwood, Ruby Mendenhall, and Margaret Browne Huntt. 2020. “‘If you aren’t White, Asian or Indian, you aren’t an engineer’: racial microaggressions in STEM education.” Int. J. STEM Educ. 7 (1). https://doi.org/10.1186/s40594-020-00241-4.
Lindsay, Sally, and Kristina Fuentes. 2022. “It is time to address ableism in academia: A systematic review of the experiences and impact of ableism among faculty and staff.” Disabilities (Basel) 2 (2): 178–203. https://doi.org/10.3390/disabilities2020014.
Llorens, Anaïs, Athina Tzovara, Ludovic Bellier, Ilina Bhaya-Grossman, Aurélie Bidet-Caulet, William K Chang, Zachariah R Cross, et al. 2021. “Gender bias in academia: A lifetime problem that needs solutions.” Neuron 109 (13): 2047–74. https://doi.org/10.1016/j.neuron.2021.06.002.
Nederbragt, Alexander, Rayna Michelle Harris, Alison Presmanes Hill, and Greg Wilson. 2020. “Ten quick tips for teaching with participatory live coding.” PLoS Comput. Biol. 16 (9): e1008090. https://doi.org/10.1371/journal.pcbi.1008090.
Owen, Ann L, Erica De Bruin, and Stephen Wu. 2024. “Can you mitigate gender bias in student evaluations of teaching? Evaluating alternative methods of soliciting feedback.” Assess. Eval. High. Educ., October, 1–16. https://doi.org/10.1080/02602938.2024.2407927.
Rubin, Marc J. 2013. “The effectiveness of live-coding to teach introductory programming.” In Proceeding of the 44th ACM technical symposium on Computer science education. New York, NY, USA: ACM. https://doi.org/10.1145/2445196.2445388.
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Citation

BibTeX citation:
@online{murphy_quinlan2025,
  author = {Murphy Quinlan, Maeve},
  title = {Teaching Coding Across Disciplines},
  date = {2025-01-05},
  url = {https://murphyqm.github.io/docs/talks/code-for-whom},
  langid = {en}
}
For attribution, please cite this work as:
Murphy Quinlan, Maeve. 2025. “Teaching Coding Across Disciplines.” January 5, 2025. https://murphyqm.github.io/docs/talks/code-for-whom.