Introducing Women to Data Science: Investigating the Gender Gap in a Learning Initiative on Kaggle Twyman, Marlon Majchrzak, Ann 2023-12-26T18:52:51Z 2023-12-26T18:52:51Z 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other ec092780-a2ef-4800-9d10-d53b4b58ff35
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject STEM Education and Workforce Development: Addressing Equity and Inclusion for Underserved Populations
dc.subject data science
dc.subject gender gaps
dc.subject informal learning
dc.subject online communities
dc.subject stem
dc.title Introducing Women to Data Science: Investigating the Gender Gap in a Learning Initiative on Kaggle
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract Unlike many STEM fields, data science has emerged with online communities serving as prominent spaces for professional development and learning. This paper explores factors that contribute to gender differences regarding perceptions of satisfaction and difficulty in a learning initiative for data science hosted by the Kaggle community. We investigate multiple factors: prior experience and skills, professional role, and communication within a learning community. Our results, based on a survey of 2,707 aspiring data scientists, suggest that learners who identify as women do not perceive assignments to be more difficult than men, but complete fewer assignments. The increasing difficulty of the learning experience affected all learners, but men were still able to complete the hardest assignments at a higher rate than women despite experiencing similar barriers. Overall, the findings demonstrate how learning initiatives in technically intensive domains contribute to different outcomes between groups.
dcterms.extent 10 pages
prism.startingpage 7184
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