oa Teaching coding inclusively

References

1. Abbiss, J. (2008). Rethinking the ‘problem’ of gender and IT schooling: Discourses in literature. Gender and Education, 20(2), 153–165.
   [Google Scholar]
2. Aelenei, C., Martinot, D., Sicard, A., & Darnon, C. (2019). When an academic culture based on self-enhancement values undermines female students’ sense of belonging, self-efficacy, and academic choices. The Journal of Social Psychology, 160(3), 373–389.
   [Google Scholar]
3. Aiello, L.C. (2016). The multifaceted impact of Ada Lovelace in the digital age. Artificial Intelligence, 235, 58–62.
   [Google Scholar]
4. Anderson, N.D. (2016). A call for computational thinking in undergraduate psychology. Psychology Learning & Teaching, 15(3), 226–234.
   [Google Scholar]
5. Angeli, C. (2022). The effects of scaffolded programming scripts on pre-service teachers’ computational thinking: Developing algorithmic thinking through programming robots. International Journal of Child-Computer Interaction, 31, 100329.
   [Google Scholar]
6. Angeli, C., & Georgiou, K. (2023). Investigating the effects of gender and scaffolding in developing preschool children’s computational thinking during problemsolving with bee-bots. Frontiers in Education, 7.
   [Google Scholar]
7. Angeli, C., & Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105, 106185.
   [Google Scholar]
8. Angeli, C., & Valanides, N. (2020). Developing young children’s computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105, 105954.
   [Google Scholar]
9. Basman, A. (2017). If what we made were real: Against imperialism and cartesianism in computer science, and for a discipline that creates real artifacts for real communities, following the faculties of real cognition. PPIG, 23.
   [Google Scholar]
10. Berry, A., McKeever, S., Murphy, B., & Delany, S.J. (2022). Addressing the ‘leaky pipeline’: A review and categorisation of actions to recruit and retain women in computing education. arXiv preprint arXiv:2206.06113.
    [Google Scholar]
11. Beyer, K. W. (2012). Grace Hopper and the invention of the information age. MIT Press.
    [Google Scholar]
12. Birhane, A., & Guest, O. (2021). Towards decolonising computational sciences. Kvinder, Køn & Forskning, 1, 60-73.
    [Google Scholar]
13. Blum, L., & Frieze, C. (2005). The evolving culture of computing: Similarity is the difference. Frontiers: A Journal of Women Studies, 26(1), 110–125.
    [Google Scholar]
14. Bocconi, S., Dini, S., Ferlino, L., Martinoli, C., & Ott, M. (2007). ICT educational tools and visually impaired students: Different answers to different accessibility needs. Universal Access in Human-Computer Interaction. Applications and Services, 491–500.
    [Google Scholar]
15. Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada, 1, 25.
    [Google Scholar]
16. Burrill, C. (1985). The sensitive period hypothesis: A review of literature regarding acquisition of a native-like pronunciation in a second language. Paper presented at a meeting of the TRI-TESOL conference, Bellevue, WA.
    [Google Scholar]
17. Busjahn, T., & Schulte, C. (2013). The use of code reading in teaching programming. Proceedings of the 13th Koli Calling International Conference on Computing Education Research.
    [Google Scholar]
18. Connell, S.M., & Janssen-Lauret, F. (2022). Lost voices: On counteracting exclusion of women from histories of contemporary philosophy. British Journal for the History of Philosophy, 30(2), 199–210.
    [Google Scholar]
19. De Jonge Akademie. (2021). A smarter academic year. Amsterdam. Retrieved from https://www.dejongeakademie.nl/documenten/handlerdownloadfiles.ashx?idnv=2043173
    [Google Scholar]
20. De Wit, S., Hermans, F., Specht, M., & Aivaloglou, E. (2024). Gender, social interactions and interests of characters illustrated in scratch and python programming books for children. Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1.
    [Google Scholar]
21. De Wit, S., Hermans, F., Specht, M., & Aivaloglou, E. (2023). Exploring the effects of the Hedy user interface on the development of cs interest in girls. 10th ACM Celebration of Women in Computing womENcourage: Computing Connecting Everyone.
    [Google Scholar]
22. Dresden, B.E., Dresden, A.Y., Ridge, R.D., & Yamawaki, N. (2018). No girls allowed: Women in male-dominated majors experience increased gender harassment and bias. Psychological reports, 121(3), 459–474.
    [Google Scholar]
23. Erscoi, L., Kleinherenbrink, A.V., & Guest, O. (2023). Pygmalion displacement: When humanising ai dehumanises women. SocArXiv. February, 11.
    [Google Scholar]
24. Essanhaji, Z. (2023). The (im)possibility of complaint: On efforts of inverting and (en)countering the university. Gender and Education, 1–16.
    [Google Scholar]
25. Evans, C.L. (2020). Broad band: The untold story of the women who made the internet. New York: Penguin.
    [Google Scholar]
26. Fine, C. (2010). Delusions of gender: How our minds, society, and neurosexism create difference. New York: WW Norton & Company.
    [Google Scholar]
27. Forbes, S.H., Aneja, P., & Guest, O. (2022). The myth of normative development. Infant and Child Development, 33(1), e2393.
    [Google Scholar]
28. Ford, P. (2015). What is code? if you don’t know, you need to read this. Retrieved from https://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/
    [Google Scholar]
29. Gage, M.J. (1883). Woman as an inventor. The North American Review, 136(318), 478–489.
    [Google Scholar]
30. Gilsing, M., Pelay, J., & Hermans, F. (2022). Design, implementation and evaluation of the Hedy programming language. Journal of Computer Languages, 73, 101158.
    [Google Scholar]
31. Goffman, E. (1977). The arrangement between the sexes. Theory and society, 4(3), 301–331.
    [Google Scholar]
32. Gould, S. J. (1981). The mismeasure of man. New York: Norton.
    [Google Scholar]
33. Guest, O. (2018). Why women in psychology can’t program. Retrieved from http://neuroplausible.com/programming
    [Google Scholar]
34. Hamilton, M.H., & Hackler, W.R. (2008). Universal systems language: Lessons learned from apollo. Computer, 41(12), 34–43.
    [Google Scholar]
35. Hampshire, A., Highfield, R.R., Parkin, B.L., & Owen, A.M. (2012). Fractionating human intelligence. Neuron, 76(6), 1225–1237.
    [Google Scholar]
36. Harlizius-Klück, E. (2017). Weaving as binary art and the algebra of patterns. Textile, 15(2), 176–197.
    [Google Scholar]
37. Hermans, F. (2021). The programmer’s brain: What every programmer needs to know about cognition. Shelter Island, NY: Manning Publications.
    [Google Scholar]
38. Hermans, F. (2024). [onward23] creating a learnable and inclusive programming language. Retrieved from https://www.youtube.com/watch?v=VzXiup5Gm7Y
    [Google Scholar]
39. Hermans, F., & Aldewereld, M. (2017). Programming is writing is programming. Companion Proceedings of the 1st International Conference on the Art, Science, and Engineering of Programming, 1–8.
    [Google Scholar]
40. Hicks, M. (2017). Programmed inequality: How Britain discarded women technologists and lost its edge in computing. Cambridge, MA: MIT press.
    [Google Scholar]
41. Johnson, J., Madill, A., Koutsopoulou, G.Z., Brown, C., & Harris, R. (2020). Tackling gender imbalance in psychology. Psychologist, 33, 5–6.
    [Google Scholar]
42. Kelly, P., Wang, Y., & Mizunoya, S. (2022). How do the educational experiences of girls and boys differ? Retrieved from https://data.unicef.org/data-for-action/how-do-educational-experiences-ofgirls-and-boys-differ/
    [Google Scholar]
43. Kleiman, K. (2022). Proving ground: The untold story of the six women who programmed the world’s first modern computer. Hurst Publishers.
    [Google Scholar]
44. Kramarae, C. (1988). Technology and women’s voices: Keeping in touch. Routledge.
    [Google Scholar]
45. Kyndt, E., Berghmans, I., Dochy, F., & Bulckens, L. (2013). ‘Time is not enough’: Workload in higher education: A student perspective. Higher Education Research & Development, 33(4), 684–698.
    [Google Scholar]
46. Kyndt, E., Dochy, F., Struyven, K., & Cascallar, E. (2010). The perception of workload and task complexity and its influence on students’ approaches to learning: A study in higher education. European Journal of Psychology of Education, 26(3), 393–415.
    [Google Scholar]
47. Lee Shetterly, M. (2016). Hidden figures: The American dream and the untold story of the Black women mathematicians who helped win the space race. New York: William Morrow.
    [Google Scholar]
48. Lehtinen, T., Lukkarinen, A., & Haaranen, L. (2021). Students struggle to explain their own program code. Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1.
    [Google Scholar]
49. Lewis, C.M., Anderson, R.E., & Yasuhara, K. (2016). ‘I don’t code all day’: Fitting in computer science when the stereotypes don’t fit. Proceedings of the 2016 ACM conference on international computing education research, 23–32.
    [Google Scholar]
50. Lien, T. (2013). No girls allowed. Retrieved from https://www.polygon.com/features/2013/12/2/5143856/nogirls-allowed
    [Google Scholar]
51. Light, T. P., Nicholas, J., & Bondy, R. (2015). Feminist pedagogy in higher education: Critical theory and practice. Waterloo, Ontario: Wilfrid Laurier University Press.
    [Google Scholar]
52. Lind-Guzik, A. (2023). You should give a sh*t about: Gender apartheid. Retrieved from https://conversationalist.org/2023/05/18/end-gender-apartheid-today-open-letter-interviewgissou-nia-human-rights-lawyer/
    [Google Scholar]
53. Little, B., & Winch, A. (2020). Patriarchy in the digital conjuncture: An analysis of Google’s James Damore. New Formations, 102(102), 44–63.
    [Google Scholar]
54. Long, K. (2018). Why don’t women code? a UW lecturer’s answer draws heat. Retrieved from https://www.seattletimes.com/seattle-news/education/why-dont-women-code-a-uw-lecturersanswer-draws-heat/
    [Google Scholar]
55. Macrides, E., Miliou, O., & Angeli, C. (2022). Programming in early childhood education: A systematic review. International Journal of Child-Computer Interaction, 32, 100396.
    [Google Scholar]
56. Margolis, J., & Fisher, A. (2001). Unlocking the clubhouse: Women in computing. Cambridge, MA: MIT Press.
    [Google Scholar]
57. Mayer, R.E. (1981). The psychology of how novices learn computer programming. ACM Computing Surveys, 13(1), 121–141.
    [Google Scholar]
58. McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Kolikant, Y.B.-D., Laxer, C., Thomas, L., Utting, I., & Wilusz, T. (2001). A multi-national, multi-institutional study of assessment of programming skills of first-year CS students. ACM SIGCSE Bulletin, 33(4), 125–180.
    [Google Scholar]
59. McKinley, K. S. (2018). What happens to us does not happen to most of you. Retrieved from https://www.sigarch.org/what-happens-to-us-does-not-happen-to-most-of-you/
    [Google Scholar]
60. Mitcho, S.R. (2016). Feminist pedagogy. Encyclopedia of Educational Philosophy and Theory, 1–5.
    [Google Scholar]
61. Morrison, B.B., Margulieux, L. E., & Guzdial, M. (2015). Subgoals, context, and worked examples in learning computing problem solving. Proceedings of the eleventh annual International Conference on International Computing Education Research.
    [Google Scholar]
62. Navarro, D. (2013). Learning statistics with R. Lulu.com. Retrieved from http://compcogscisydney.org/learning-statistics-with-r
    [Google Scholar]
63. O’Dea, R.E., Lagisz, M., Jennions, M.D., & Nakagawa, S. (2018). Gender differences in individual variation in academic grades fail to fit expected patterns for stem. Nature communications, 9(1), 3777.
    [Google Scholar]
64. Okun, T. (2021). Retrieved from https://www.whitesupremacyculture.info/
65. O’Mara, M. (2022). Why can’t tech fix its gender problem? Retrieved from https://www.technologyreview.com/2022/08/11/1056917/tech-fix-gender-problem/
    [Google Scholar]
66. Phillips-Jones, L. (2003). Skills for successful mentoring: Competencies of outstandingmentors and mentees. CCC/The Mentoring Group.
    [Google Scholar]
67. Pozo, B., & Padilla, C. (2019). Criptogıń ia: Una paraula nova per a un fenomen antic. eldiario. es, 5, 2019.
    [Google Scholar]
68. Pozo-Sánchez, B., & Padilla-Carmona, C. (2021). Criptoginia: Una palabra nueva, un concepto para investigar. Quaderns de Filologia-Estudis Lingüístics, 26, 175–192.
    [Google Scholar]
69. QANU. (2020). Report on the bachelor’s and the master’s programmes artificial intelligence of Radboud University. Retrieved from https://publicaties.nvao.net/ACCR_009468_21PM-56945_Artificial_Intelligence_Rapport_2020.pdf
70. Raytheon. (1969). Apollo 11 Press Kit Raytheon. Retrieved from https://www.apollopresskits.com/apollopresskit-directory
71. Rosner, D.K., Shorey, S., Craft, B.R., & Remick, H. (2018). Making core memory: Design inquiry into gendered legacies of engineering and craftwork. Proceedings of the 2018 CHI conference on human factors in computing systems, 1–13.
    [Google Scholar]
72. Rossiter, M.W. (1993). The Matthew Matilda effect in science. Social studies of science, 23(2), 325–341.
    [Google Scholar]
73. Salter, A., & Blodgett, B. (2017). Toxic geek masculinity in media: Sexism, trolling, and identity policing. Cham: Palgrave Macmillan.
    [Google Scholar]
74. Scheerens, J., Timmermans, A., & Van der Werf, G. (2019). Socioeconomic inequality and student outcomes in the Netherlands. In Volante, L., Schnepfe, S., Jerrim, J., & Klinger, D. (Eds.), Socioeconomic inequality and student outcomes: Cross-national trends, policies, and practices (pp. 111–132). Singapore: Springer.
    [Google Scholar]
75. Scott, S. (2019). Fake geek girls. New York: New York University Press.
    [Google Scholar]
76. Sheard, J., Simon, J., Dermoudy, J., D’Souza, D., Hu, M., & Parsons, D. (2014). Benchmarking a set of exam questions for introductory programming. Proceedings of the Sixteenth Australasian Computing Education Conference (ACE2014), Auckland, New Zealand.
    [Google Scholar]
77. Shore, J. (1985). The sachertorte algorithm and other antidotes to computer anxiety (Vol.17). New York: ACM New York.
    [Google Scholar]
78. Shrewsbury, C.M. (1987). What is feminist pedagogy?Women’s Studies Quarterly, 15(3/4), 6–14.
    [Google Scholar]
79. Swidan, A., & Hermans, F. (2023). A framework for the localization of programming languages. Proceedings of the 2023 ACM SIGPLAN International Symposium on SPLASH-E.
    [Google Scholar]
80. Terrell, J., Kofink, A., Middleton, J., Rainear, C., Murphy-Hill, E., Parnin, C., & Stallings, J. (2016). Gender differences and bias in open source: Pull request acceptance of women versus men. PeerJ Computer Science.
    [Google Scholar]
81. The British Psychological Society. (2017). Supplementary guidance for research and research methods on Society accredited undergraduate and conversion programmes. Retrieved from https://cms.bps.org.uk/sites/default/files/202207/Research%20Methods%20-%20Undergraduate%20Programmes%20WEB.pdf
    [Google Scholar]
82. Tupas, R., & Tarrayo, V.N. (2024). The violence of literature review and the imperative to ask new questions. Applied Linguistics Review, (0).
    [Google Scholar]
83. Turkle, S. (2005). The second self. Cambridge, MA: MIT Press.
    [Google Scholar]
84. Van den Brink, M., & Benschop, Y. (2012). Gender practices in the construction of academic excellence: Sheep with five legs. Organization, 19(4), 507–524.
    [Google Scholar]
85. Van der Meulen, A., Hartendorp, M., Voorn, W., & Hermans, F. (2023). Observing the computational concept of abstraction in blind and low vision learners using the bee-bot and blue-bot. Computer Science Education, 1–23.
    [Google Scholar]
86. Van der Werf, V., Aivaloglou, E., Hermans, F., & Specht, M. (2022). (how) should variables and their naming be taught in novice programming education? Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume2, 53–54.
    [Google Scholar]
87. Voyer, D., & Voyer, S.D. (2014). Gender differences in scholastic achievement: A meta-analysis. Psychological bulletin, 140(4), 1174.
    [Google Scholar]
88. Vygotsky, L. S., & Cole, M. (1978). Mind in society: Development of higher psychological processes. Cambridge, MA: Harvard University Press.
    [Google Scholar]
89. Wagenmakers, E.-J. (2018). Agreed, but I am not sure that combining these in a single course is wise, and I worry about students without coding experience feeling that they start with a handicap (because they do). In their first stats course, I like students to grasp the concepts, not “tapply”. Retrieved from https://twitter.com/EJWagenmakers/status/1066680953534328832
    [Google Scholar]
90. Webb, L.M., Allen, M.W., & Walker, K.L. (2002). Feminist pedagogy: Identifying basic principles. Academic Exchange, 6(1), 67–72.
    [Google Scholar]
91. White, S.K. (2020). Women in tech statistics: The hard truths of an uphill battle. CIO online.
    [Google Scholar]
92. Wing, J.M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.
    [Google Scholar]
93. Yates, J., & Plagnol, A.C. (2022). Female computer science students: A qualitative exploration of women’s experiences studying computer science at university in the UK. Education and Information Technologies, 27(3), 3079–3105.
    [Google Scholar]