Stanford University

Stereotype Threat in Virtual Learning Environments: Effects of Avatar Gender and Sexist Behavior on Women’s Math Learning Outcomes

Chang, F., Luo, M., Walton, G. M., Aguilar, L., & Bailenson, J. N. (2019). Stereotype threat in virtual learning environments: Effects of avatar gender and sexist behavior on women’s math learning outcomes. Cyberpsychology, Behavior, and Social Networking.

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Abstract

Women in math, science, and engineering (MSE) often face stereotype threat: they fear that their performance in MSE will confirm an existing negative stereotype – that women are bad at math – which in turn may impair their learning and performance in math. The current research investigated if sexist non-verbal behavior of a male instructor that is indicative of negative stereotypes about women could activate stereotype threat among women in a virtual classroom. Additionally, the research examined if learners’ avatar representation in VR altered this non-verbal process. Specifically, a 2 (avatar gender: female vs. male) x 2 (instructor behavior: dominant-sexist vs. non-dominant or non-sexist) between-subjects experiment was employed. Data from 76 female college students demonstrated that participants learned less and performed worse when interacting with a sexist male instructor compared to a non-sexist instructor in a virtual classroom. Participants learned and performed equally well when represented by female and male avatars. Our findings extend previous research in physical learning settings, suggesting that dominant-sexist behaviors may give rise to stereotype threat and undermine women’s learning outcomes in virtual classrooms. Implications for gender achievement gaps and stereotype threat are discussed.

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