The aim of inclusive education is to provide a supportive space for students from every background. The theory of intersectionality suggests that multiple identities intersect within social spaces to construct specific positionalities. To support the heterogeneity of all students, there is a need to understand who is in our Science, Technology, Engineering and Mathematics (STEM) courses and how we would go about assessing this. This article problematizes the traditional approach to demographic data collection and presents the beginnings of an alternative approach. The study utilized qualitative and quantitative data in order to examine the way students self-describe within a large multi-institutional program. There were 2,082 students presented with 12 identity categories and asked to specify which of these identities were important to them for their own self-definition and then write an open self-description. The data was analyzed using descriptive statistics, comparative proportional usage analyses of identity categories by traditional demographic groupings, and hierarchical cluster analysis of identity variables. The results showed that the majority of students use multiple categories of identity in combination, that these identity preferences differ in relation to traditional demographic categories, and that there were four underpinning identity orientations consisting of a focus on heritage, health, self-expression, and career.