Although computing occupations have some of the greatest projected growth rates, there remains a deficit of graduates in these fields. The struggle to engage enough students to meet demands is particularly pronounced for groups already underrepresented in computing, specifically, individuals that self-identify as a woman, or as Black, Hispanic/Latinx, or Native American. Prior studies have begun to examine issues surrounding engagement and retention, but more understanding is needed to close the gap, and to broaden participation. In this research, we provide quantitative evidence from the Multiple-Institution Database for Investigating Engineering Longitudinal Development—a longitudinal, multi-institutional database to describe participation trends of marginalized groups in computer science. Using descriptive statistics, we present the enrollment and graduation rates for those situated at the intersection of race/ethnicity and gender between 1987 and 2018. In this work, we observed periods of significant flux for Black men and women, and White women in particular, and consistently low participation of Hispanic/Latinx and Native American men and women, and Asian women. To provide framing for the evident peaks and valleys in participation, we applied historical context analysis to describe the political, economic, and social factors and events that may have impacted each group. These results put a spotlight on populations largely overlooked in statistical work and have the potential to inform educators, administrators, and researchers about how enrollments and graduation rates have changed over time in computing fields. In addition, they offer insight into potential causes for the vicissitudes, to encourage more equal access for all students going forward.

中文翻译：

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交叉性和计算机科学人口统计的探索

尽管计算机职业的预计增长率最高，但这些领域的毕业生仍然短缺。对于已经在计算机领域代表性不足的群体，特别是自我认同为女性、黑人、西班牙裔/拉丁裔或美洲原住民的个人而言，让足够多的学生参与以满足需求的斗争尤其明显。先前的研究已经开始研究围绕参与和保留的问题，但需要更多的了解来缩小差距并扩大参与。在这项研究中，我们从用于调查工程纵向发展的多机构数据库中提供定量证据，该数据库是一个纵向的多机构数据库，用于描述计算机科学中边缘化群体的参与趋势。使用描述性统计，我们展示了 1987 年至 2018 年间处于种族/民族和性别交汇点的人的入学率和毕业率。在这项工作中，我们观察到黑人男性和女性，尤其是白人女性的显着变化时期，并且参与度一直很低西班牙裔/拉丁裔和美洲原住民男性和女性以及亚洲女性。为了为参与的明显高峰和低谷提供框架，我们应用历史背景分析来描述可能影响每个群体的政治、经济和社会因素和事件。这些结果突出了在统计工作中很大程度上被忽视的人群，并有可能让教育工作者、管理人员和研究人员了解计算机领域的入学率和毕业率如何随时间发生变化。此外，