Exploring the Impact of Gender Identity and Stereotypes on Secondary Pupils’ Computer Science Enrolment Interest

Despite recent government initiatives, there continues to be a shortage of individuals working in Science, Technology, Engineering and Mathematics (STEM) industries.

There is a particular under-representation of female STEM workers, with females opting out of STEM fields at each step of the 'STEM pipeline', from classroom to boardroom.

This thesis identifies and explores the impact of different factors on interest in choosing STEM subjects at post-16 level and how gender identity and stereotypes impact upon computer science enrolment interest.

A systematic review of the literature that explores influences on STEM subject choice at post-16 level highlighted thirteen key factors that predict STEM subject choice; these factors could be categorised as either intrinsic or extrinsic to the individual.

A fourteenth factor, an individual's sex, interacted with the majority of these identified factors.

This systematic literature review highlights the insufficiency of theories of decision-making in explaining the decision-making that occurs during STEM subject choice, since an individual's biological sex appears so influential.

The empirical study investigates whether gender identity and other well-evidenced influences predict enrolment interest in computer science. It aims to explore whether stereotypical cues in a learning environment affect students' interest.

Year 9 students (n= 168) completed measures assessing gender identity.

They were shown either a stereotypical or a non-stereotypical computer science classroom and completed measures assessing their enrolment interest in computer science, belonging, stereotype threat, self-efficacy and utility value.

Femininity significantly predicted enrolment interest, and this relationship was mediated by stereotype threat.

The stereotypicality of the classroom did not moderate the mediation of stereotype threat on femininity and enrolment interest.

This empirical study extends previous research by showing that it is one's gender identity, rather than simply their sex, that predicts enrolment interest.

We highlight the need to consider and challenge stereotypes that continue to exist in relation to subjects such as computer science, in order for all students to feel included.