Assessing gender bias in the software used in computer science and software engineering education

Abstract

Women are underrepresented in Computer Science (CS)/ Software Engineering (SE) and other technology related degrees. As undergraduates, they are also less likely to persist with CS/SE studies than men enrolled in those same courses. Gender correlated differences in personal characteristics, behaviour, and preferences mean that course design decisions may introduce unintended bias. To address this issue, we drew inspiration from the GenderMag method. GenderMag uses personas with evidence-based gender differences in problem-solving traits to detect usability issues in software. In this paper we investigate the personal qualities of CS and SE students, and how these influence their CS/SE learning journey. A series of persona development workshops were held to gather an extensive and unique qualitative dataset capturing the prior experiences, preferences, learning styles, motivations, goals, frustrations, and constraints of CS/SE students. Gender differences were used to construct preliminary male and female student personas. These personas were used in cognitive walkthroughs of software applications commonly used in education, and their performance compared to GenderMag’s Tim and Abi. While the student personas were less effective and lacked specificity compared to Abi, they were able to identify issues not detectable with GenderMag. Furthermore, the findings show the utility of persona development workshops as a data collection method and introduce a comprehensive list of CS/SE student qualities that may inspire future investigations.