Front Big Data. 2021 Jan 27;3:599016. doi: 10.3389/fdata.2020.599016. eCollection 2020.
The current study uses a network analysis approach to explore the STEM pathways that students take through their final year of high school in Aotearoa New Zealand. By accessing individual-level microdata from New Zealand’s Integrated Data Infrastructure, we are able to create a co-enrolment network comprised of all STEM assessment standards taken by students in New Zealand between 2010 and 2016. We explore the structure of this co-enrolment network though use of community detection and a novel measure of entropy. We then investigate how network structure differs across sub-populations based on students’ sex, ethnicity, and the socio-economic-status (SES) of the high school they attended. Results show the structure of the STEM co-enrolment network differs across these sub-populations, and also changes over time. We find that, while female students were more likely to have been enrolled in life science standards, they were less well represented in physics, calculus, and vocational (e.g., agriculture, practical technology) standards. Our results also show that the enrollment patterns of Asian students had lower entropy, an observation that may be explained by increased enrolments in key science and mathematics standards. Through further investigation of differences in entropy across ethnic group and high school SES, we find that ethnic group differences in entropy are moderated by high school SES, such that sub-populations at higher SES schools had lower entropy. We also discuss these findings in the context of the New Zealand education system and policy changes that occurred between 2010 and 2016.