Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/135383
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Type: Conference paper
Title: Gender Influence on Communication Initiated within Student Teams
Author: Garcia, R.
Liao, C.-J.
Pearce, A.
Treude, C.
Citation: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (SIGCSE, 2022), 2022, vol.1, pp.1-7
Publisher: Association for Computing Machinery
Publisher Place: New York, NY, United States
Issue Date: 2022
ISBN: 9781450390705
Conference Name: Computer Science Education (SIGCSE) (3 Mar 2022 - 5 Mar 2022 : Providence, RI, USA)
Statement of
Responsibility: 
Rita Garcia, Chieh-Ju Liao, Ariane Pearce, Christoph Treude
Abstract: Collaboration is important during software development, but related work has found gender differences can influence the collaboration process, creating inequality in the team’s dynamics. In this paper, we present a gender analysis study that involved 39 students, examining their teams’ online collaborations while contributing to a large open-source software project. Eight teams of 4-6 Software Engineering (SE) students communicated over an online messaging platform, Slack, to complete an eight-week project. The goal of this study is to identify gender differences emerging from team collaboration. A mixed-methods approach was used to collect students’ teamwork experiences and analyse their collaboration. Our research shows statistically significant results in female students’ leadership, coordination, and project-monitoring behaviours used to complete the project. The results also showed a higher rate of help seeking within the all-female team, an infrequent behaviour observed in the all-male and mixed-gender teams. Our findings raise future research opportunities to further investigate the gender differences emerging from team collaboration.
Keywords: Gender Analysis; Teamwork; Collaboration
Rights: © 2022 Association for Computing Machinery Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org
DOI: 10.1145/3478431.3499279
Published version: https://www.acm.org/
Appears in Collections:Computer Science publications

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