Despite great efforts, women are underrepresented in computer science and other science, technology, engineering, and mathematics disciplines. To understand the root of this we studied 112 high school students (51 female, 61 male) aged between 10 to 13 years. The questionnaire-based survey revealed a significant effect of gender on technical self-efficacy and on interest in computer science. Furthermore, we extracted the students’ mental models by letting them draw computer scientists. A rich picture analysis revealed significant effects of gender on the stereotypicality of the images. The gender gap revealed by this multi-method approach influences students’ career decisions and yields in declining participation of women in STEM. Consequently, future measures must focus on school students at an earlier age. These should be offering a variety of male and female role models and should strengthen the individual’s technical self-efficacy, as it profoundly impacts later career decisions.
Brauner, P., Leonhardt, T., Schroeder, U., Ziefle, M., Bergner, N., Ziegler, B.: Gender Influences On School Students’ Mental Models of Computer Science – A Quantitative Rich Picture Analysis with Sixth Graders. In: GenderIT ’18 Proceedings of the 4th Conference on Gender & IT. pp. 113–122. ACM New York, NY, USA (2018).