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Investigating the Role Choice of Female Students in a Software Engineering Team Project

Published:09 January 2019Publication History

ABSTRACT

In 2017 the number of individuals who identify as female in the UK studying a STEM subject (Science, Technology, Engineering and Maths) in further education is 35% where as 94% of their peers who identify as male choose a STEM subject to study. This decreases in higher education with 9% of females compared to 29% of males choosing a STEM subject in 2017 [1]. To address this issue there are many initiatives being set up to recruit females into STEM higher education courses. However there is a lack of research into the experiences of female students once they have started their degree programme in Computer Science.

In this study we investigate the roles which undergraduate female students choose in a large software engineering team project to find out if there are barriers that prevent them from taking on technical or programming roles in these projects. We analysed assessment data to determine their previous programming experience and then performed a content analysis of team project deliverables and peer assessment scores. The results show that our female students, despite their strong academic background, tend to pick less technical roles in these projects than male students and are subsequently awarded lower peer review scores by their teammates for their contribution to the group work. These results indicate that teaching interventions may need to take place to make the role choice and peer review processes fairer in such projects. Further investigations are needed to see if there is some form of unconscious bias or imposter syndrome occurring in our software engineering team project module and if this is a common phenomenon in these projects in other HE institutions.

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    • Published in

      cover image ACM Other conferences
      CEP '19: Proceedings of the 3rd Conference on Computing Education Practice
      January 2019
      68 pages
      ISBN:9781450366311
      DOI:10.1145/3294016

      Copyright © 2019 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 9 January 2019

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      • short-paper
      • Research
      • Refereed limited

      Acceptance Rates

      CEP '19 Paper Acceptance Rate16of33submissions,48%Overall Acceptance Rate32of71submissions,45%

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