Hacking gender in computer‐supported collaborative learning: The experience of being in mixed‐gender teams at a computer science hackathon

Abstract

AbstractBackgroundGender stereotypes about women and men are prevalent in computer science (CS). The study's goal was to investigate the role of gender bias in computer‐supported collaborative learning (CSCL) in a CS context by elaborating on gendered experiences in the perception of individual and team performance in mixed‐gender teams in a hackathon.DatasetThe dataset of this study was collected at a 3‐day CSCL hackathon aimed at gaining knowledge on designing educational games. We assigned the 28 participants of the hackathon to mixed‐gender groups and asked them to fill out a questionnaire, including collective self‐esteem scales, before the start. During the hackathon, we again asked the participants to complete team progress evaluation surveys individually after each workday. Lastly, we interviewed 11 participants to elaborate on the quantitative findings with qualitative data.MethodologyWe applied an exploratory mixed‐method approach using quantitative survey data at several time points during the hackathon, which was analysed with clustering and descriptive statistics and complemented with qualitative coding of interviews with participants.ResultsThe results demonstrate that social and psychological aspects of gender are important for understanding the outcomes and perceptions of gender in a CS hackathon. The analysis further suggests that collective self‐esteem can be used as a key variable to assess gender differences in CSCL studies, providing explanatory benefits. More broadly, results gave reason to believe that CSCL in the CS domain currently severely fails to account for gender representation. Interviewed participants raised substantial concerns about the underlying gender stereotypes prevalent in communication, team roles, and work division. We provide recommendations for practitioners seeking to create gender‐inclusive and counter‐stereotypical CSCL and wider, critical proposals for how we, as researchers, can assess gender with appropriate methodologies and interventions in computer science education.