A Statistical Study of Female Students in a Software Engineering Class: Preparedness, Performance, and Contribution
Jialin Cui, Runqiu Zhang, Qinjin Jia, Fangtong Zhou, Ruochi Li, Edward F. Gehringer · 2024
This is a research-to-practice full paper. Several research studies indicate that women who have opted into a computing career path must regularly contend with negative stereotypes about their technical abilities. These stereotypes are often cited as contributing factors to the underrepresentation of women in computing. To counter these stereotypes and enhance female participation in computer science, numerous interventions have been designed. However, most existing research tends to rely on anecdotal evidence and questionnaires to study these stereotypes. In contrast, our study collected data from over 900 students over a span of eight years and adopted a comprehensive quantitative approach to examine these stereotypes about female students. We utilized pre-class GitHub contribution metrics to evaluate students' programming experience and an array of in-class grading items to measure students' performance. Additionally, we mined the project repositories' git logs to gain insights into students' contributions to team projects. Our investigation began by probing whether there was a notable difference in the technical backgrounds or preparedness between female and male students. The results indicated that males tended to be better prepared. Next, we explored potential disparities in class performance between the two genders. Our findings revealed that males and females each excelled in different areas. We were also interested in discerning if female and male students contributed equally to team projects; our analysis affirmed that the contributions were comparable between the two groups. If allowed to choose their teammates, we examined whether they showed a preference for single-gender teams or mixed-gender teams. Our conclusions indicated no marked preference. This paper aims to augment the body of research on computing education by assisting educators in gaining a better understanding of female students in the class. Moreover, it tests the stereotypes by comparing them with empirical results.