Abstract
Gender bias is a key factor inhibiting the representation of women in the IT workforce, yet it remains a complex issue with mixed findings in prior literature. In this paper, we used data from RateMyProfessor.com (RMP) to explore potential patterns of gender bias in academia. Specifically, we examined how students evaluated their instructors in IT-related fields. Through multivariate analysis of numerical ratings and written comments, we revealed that students tend to give high star ratings and positive comments for female academics’ outstanding performance but are also more critical of their underperformance. Interestingly, this pattern is inverted for male academics. By revealing the multi-dimensional nature of gender bias, we expect to provide insights for research and practice, particularly informing future efforts to promote gender diversity, equity, and inclusion in the IT workforce.
Recommended Citation
Gao, Grace Yuekun; So, Bobby T.K.; and Cheung, Christy M.K., "Gender Bias: Student Evaluations of Female Academics in IT-Related Fields" (2026). ICEB 2026 Proceedings (Beijing, China). 43.
https://aisel.aisnet.org/iceb2026/43