Modeling Women's Elective Choices in Computing

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationITiCSE-WGR '23: Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education
PublisherACM Association for Computing Machinery
Pages196-226
Number of pages31
ISBN (Electronic)9798400704055
DOIs
Publication statusPublished - 28 Dec 2023
Event28th ACM Conference on Innovation and Technology in Computer Science Education - Turku, Finland
Duration: 10 Jul 202312 Jul 2023
Conference number: 28
https://iticse.acm.org/2023/

Conference

Conference28th ACM Conference on Innovation and Technology in Computer Science Education
Abbreviated titleITiCSE 2023
Country/TerritoryFinland
CityTurku
Period10/07/2312/07/23
Internet address

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