Using Passive Sensing to Assess the Impact of Real-Time Discrimination against Women and Underrepresented Minorities in Engineering

增加工程和计算机科学的多样性一直是一个遥不可及的目标。尽管做出了重大努力，但在2018年，代表不足的少数族裔只获得了16.1%的工程学学位，女性只获得了21.9%的工程学学位。这些低人数的原因是复杂和多方面的，歧视是这些群体的学生离开工程学的一个重要因素。这项研究的目的是对歧视对历史上代表性不足的工科学生的影响进行全面的理解。在这个大数据和手机、可穿戴设备等现成技术的时代，全面改变大学生体验数据的收集和评估方式是可能的。人们现在可以从一个实验室移动到另一个领域，将行动与行为联系起来，并收集纵向数据。这反过来又使我们有可能以新的方式理解偏见及其对工程教育的影响：通过用被动的数据收集来补充自我报告，大数据可以用来创建行为图像，同时了解代表不足的少数族裔和女性工程专业学生面临的具体挑战。该项目将产生一个独特的强大的纵向数据集，该数据集捕捉到学生经历的实时变化，并允许研究各种背景下大规模歧视的影响。该项目将量化范围、方向和对行为的纵向影响，并将其与GPA和保留率等长期结果联系起来。在过去的歧视研究中，这种将行为与现场经验联系起来的能力是缺乏的。由于样本较大(N=200/年)和变量数量较多，需要能够捕捉个体方差和观察不相等数量的观测的分析技术，例如分层线性建模。这些数据是在一所大型公立大学收集的，最适用于类似机构的类似项目。这项研究将为工程项目的政策制定和干预设计提供支持。最终目标是使工程学学生群体多样化，这将通过增加参与工程和计算机科学工作的代表性和视角范围而直接造福社会。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Increasing diversity in engineering and computer science has been a goal that remains elusive. Despite significant efforts, underrepresented minorities received only 16.1% and women received only 21.9% of engineering degrees in 2018. The reasons for these low numbers are complex and multifaceted and discrimination is an important factor in why students from these groups leave engineering. The goal of this research is to develop a holistic understanding of the impact of discrimination on historically underrepresented engineering students. In this era of big data and readily available technology such as mobile phones and wearables, a comprehensive change in how data about the college student experience are collected and assessed is possible. One can now move from lab to field, connect action to behavior, and collect longitudinal data. This, in turn, makes it possible to understand bias and its impact on engineering education in new ways: By complementing self-reports with passive data collection, big data can be used to create an image of behavior while learning about specific challenges underrepresented minority and female engineering students face. The project will result in a uniquely powerful longitudinal data set, which captures real-time changes in student experiences and allows study of the impact of discrimination at scale across a variety of contexts. The project will quantify the scope, direction, and longitudinal impact on behavior and link this to long-term outcomes such as GPA and retention. This ability to connect behavior to experience in the field was lacking in past studies of discrimination. Analytic techniques capable of capturing both individual variance and looking at unequal numbers of observations, such as hierarchical linear modeling, are required due to the large sample (N=200/year) and number of variables. The data are collected at a large public university and will be most applicable to similar programs at similar institutions. The research will support policy making and intervention design in engineering programs. The ultimate goal is to diversify the pool of engineering students, which will be of direct benefit to society by increasing representation and the range of perspectives engaged in the engineering and computer science workforce.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.