Research documents significant differences in the representation of women in various STEM fields (Cheryan, Ziegler, Montoya & Jiang, 2017; Faber et al. 2020). Women are well-represented in fields such as medicine and biology, while extraordinarily few women pursue mathematics and technology-intensive programs, such as computer science and engineering programs (Bøe, Henriksen, Lyons & Schreiner, 2011; Faber et al., 2020; McNally, 2020).

Research indicates that teachers often rely on stereotypes in assessing students, particularly in STEM. Notably, pervasive stereotypes perpetuate the notion that girls lack interest in mathematics, while boys excel in science and technology (Riegle-Crumb & Humphries, 2012; Steffens & Jelenec, 2011). The far-reaching consequences of such stereotypes are evident in teachers' expectations (Muntoni & Retelsdorf, 2018), interactions with students (Lavy, 2008), and students' achievements, confidence, and educational choices (Carlana, 2019; Retelsdorf, Schwarts and Asbrock, 2015).

The purpose of this paper is to investigate pre-service teachers' gender stereotypical beliefs and whether these beliefs result in bias in the recommendation of technological study tracks for elementary school students. The study also explores the causal mechanisms behind gender stereotypes and biases, considering the influence of teachers' background characteristics.

We focus on pre-service teachers rather than practicing teachers for three reasons. Firstly, pre-service teachers are more accessible than experienced educators, providing an opportunity to ensure higher data quality, such as achieving a higher response rate. Secondly, research indicates that pre-service teachers also hold stereotypical beliefs about students, and these beliefs exist even before they begin on their teaching careers (Holder & Kessels, 2017). Thirdly, pre-service teachers serve as crucial norm-setters for new generations of children and adolescents. Examining gender stereotypes among pre-service teachers creates an opportunity to integrate the knowledge generated by the project into elements of teacher education.

To this aim, we employ an embedded experimental mixed methods design using vignettes. The content of the vignettes is varied to discern the influence of gender on pre-service teachers' assessments of young students' educational choices. The quantitative part of the study incorporates a factorial survey (FS), a common method in research on discrimination and social judgments (Jasso, 2006; Wallander, 2009). Respondents are presented with a series of vignettes describing a hypothetical elementary school student with variations in gender, ethnicity, parents' occupations, favorite subjects, grades in Danish and mathematics, belief in their own abilities in mathematics, and social profile. The characteristics of the vignettes are experimentally varied, allowing for an examination of the significance of different attributes on the respondents' evaluations (Auspurg & Hinz, 2015). Based on the information presented, respondents are asked to recommend a study track for the student, choosing from natural science, technology, linguistics, or social science. A total of 441 students completed the questionnaire, resulting in 1764 vignette responses. The qualitative part of the study consists of a qualitative vignette experiment embedded in semi-structured interviews. This approach retains interpretative elements while introducing a quantitative, experimental logic using vignettes. Thus, participants are presented with identical vignettes that only vary on the independent variable (gender). This allows for both the introduction of controlled variation in information about the independent variable and in-depth interpretation of how this information is received and interpreted by the interviewees (Harrits & Møller, 2020). A total of 30 students have been interviewed. The data is analyzed using multinomial logistic regression models to estimate the effect of gender on pre-service teachers’ track recommendation as well as qualitative content analysis of interviews to shed light on the causal mechanisms underlying gender stereotypes in technology.

Preliminary results show a notable gender difference in the recommendation of study tracks in general, particularly in technology. Results from the multinomial logit model reveal a 9.7 percentage point lower probability for girls to be recommended a technological study track compared to boys. Simultaneously, the study identifies an inverse gender difference in recommending a natural science study track, where girls have a 4.6 percentage point higher probability than boys. While various student characteristics influence the recommendation of study tracks—such as parents' occupations, favorite subjects, grades, confidence in mathematics, and social profile—these characteristics only marginally reduce gender differences and thus fail to provide a comprehensive explanation of the gender gap. The qualitative analyses offer deeper insights into the reasons behind these gender biases. Technology is strongly associated with boys, computers, and gaming, leading to automatic exclusion of recommending a technological study track for girls who are not perceived as interested in technology. Furthermore, the qualitative analyses underscore the presence of socially conditioned gender considerations, particularly among female students who caution against choosing a technological study track due to perceived challenges in integrating into the male-dominated social community. In conclusion, this research unveils gender bias in pre-service teachers' assessments, contributing valuable insights for addressing and mitigating gender stereotypes in educational settings. Awareness of these biases is crucial for addressing gender inequality in educational settings and fostering an environment that encourages all students to pursue STEM fields based on their interests and capabilities.

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