Modeling Women's Elective Choices in Computing

Bradley, Steven; Parker, Miranda C.; Altin, Rukiye; Barker, Lecia; Hooshangi, Sara; Kunkeler, Thom; Lennon, Ruth G.; McNeill, Fiona; Minguillón, Julià; Parkinson, Jack; Peltsverger, Svetlana; Sibia, Naaz

Abstract

Evidence-based strategies suggest ways to reduce the gender gap in computing. For example, elective classes are valuable in enabling students to choose in which directions to expand their computing knowledge in areas aligned with their interests. The availability of electives of interest may also make computing programs of study more meaningful to women. However, research on which elective computing topics are more appealing to women is often class or institution specific. In this study, we investigate differences in enrollment within undergraduate-level elective classes in computing to study differences between women and men. The study combined data from nine institutions from both Western Europe and North America and included 272 different classes with 49,710 student enrollments. These classes were encoded using ACM curriculum guidelines and combined with the enrollment data to build a hierarchical statistical model of factors affecting student choice. Our model shows which elective topics are less popular with all students (including fundamentals of programming languages and parallel and distributed computing), and which elective topics are more popular with women students (including mathematical and statistical foundations, human computer interaction and society, ethics, and professionalism). Understanding which classes appeal to different students can help departments gain insight of student choices and develop programs accordingly. Additionally, these choices can also help departments explore whether some students are less likely to choose certain classes than others, indicating potential barriers to participation in computing.

Citation

Bradley, S., Parker, M. C., Altin, R., Barker, L., Hooshangi, S., Kunkeler, T., Lennon, R. G., McNeill, F., Minguillón, J., Parkinson, J., Peltsverger, S., & Sibia, N. (2023, July). Modeling Women's Elective Choices in Computing. Presented at ITiCSE 2023: Innovation and Technology in Computer Science Education, Turku Finland

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ITiCSE 2023: Innovation and Technology in Computer Science Education
• Start Date: Jul 10, 2023
• End Date: Jul 12, 2023
• Acceptance Date: Nov 27, 2023
• Online Publication Date: Dec 28, 2023
• Publication Date: Dec 22, 2023
• Deposit Date: Jan 15, 2024
• Publicly Available Date: Jan 17, 2024
• Publisher: Association for Computing Machinery (ACM)
• Pages: 196-226
• DOI: https://doi.org/10.1145/3623762.3633497
• Public URL: https://durham-repository.worktribe.com/output/2146643