Turnbull Steven Martin, O'Neale Dion R J
School of Critical Studies in Education, Faculty of Education and Social Work, University of Auckland, Auckland, New Zealand.
Te Pũnaha Matatini, University of Auckland, Auckland, New Zealand.
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.
本研究采用网络分析方法，探究新西兰奥塔哥地区学生在高中最后一年所走的STEM路径。通过获取新西兰综合数据基础设施中的个人层面微观数据，我们得以创建一个共同入学网络，该网络由2010年至2016年间新西兰学生参加的所有STEM评估标准组成。我们通过使用社区检测和一种新的熵度量方法来探索这个共同入学网络的结构。然后，我们根据学生的性别、种族以及他们所就读高中的社会经济地位（SES），研究网络结构在不同亚群体之间的差异。结果表明，STEM共同入学网络的结构在这些亚群体之间存在差异，并且随时间变化。我们发现，虽然女生更有可能选修生命科学标准课程，但在物理、微积分和职业（如农业、实用技术）标准课程中，她们的代表性较低。我们的研究结果还表明，亚洲学生的入学模式熵较低，这一观察结果可能是由于关键科学和数学标准课程的入学人数增加所致。通过进一步调查不同种族群体和高中SES之间的熵差异，我们发现种族群体之间的熵差异受到高中SES的调节，即SES较高学校的亚群体熵较低。我们还将在2010年至2016年间新西兰教育系统和政策变化的背景下讨论这些发现。
