Overcome the gender gap: analyzing massive open online courses through the lens of stereotype threat theory

Abstract Despite ongoing progress towards gender equality in education, women remain significantly underrepresented in computer science—a field still shaped by stereotypical expectations in our society. This gap is evident in both traditional classroom settings and online learning platforms such as MOOCs, where women face psychological barriers that hinder their learning success. As MOOCs increase accessibility and democratize education, it’s particularly important to address the barriers women face in these platforms. By analyzing 338,459 negative reviews from 8,067 IT and software courses offered by the MOOC provider Udemy, we explored the differences in how men and women experience these online learning environments. Our analysis was complemented by ten expert interviews, which helped us develop key propositions to explain these gender-based differences and derive guidelines to overcome them. Our results reveal that men and women criticize similar topics in IT courses, demonstrating that they do not belong to different user groups. However, differences between male and female reviews emerge within each topic. These differences are reflected in different communication styles, demands and areas of emphasis, shaped by gender-specific backgrounds, socialization processes and stereotypes. To overcome these differences, we propose seven guidelines drawing form Stereotype Threat Theory for designing gender-inclusive online courses that focus on inclusive communication and representation, creating supportive learning environments, and implementing high-quality, bias-aware educational practices. Aiming to foster greater participation and success for women in computer science.