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

使用被动传感评估工程领域对女性和代表性不足的少数群体的实时歧视的影响

• 批准号: 2009977
• 负责人: Jennifer Mankoff
• 金额: $ 63.8万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2009977&HistoricalAwards=false
• 关键词: Using; Passive; Sensing; Assess; Impact

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.

工程和计算机科学的多样性增加一直是难以捉摸的目标。尽管做出了巨大的努力，但代表性不足的少数民族仅获得16.1％，而女性在2018年仅获得了工程学位的21.9％。这些少数人数的原因是复杂且多方面的，歧视是这些小组学生为什么离开工程学的重要因素。这项研究的目的是对歧视对历史上代表性不足的工程专业学生的影响进行整体理解。在这个大数据和随时可用的技术（例如手机和可穿戴设备）的时代，可以对大学生体验的数据收集和评估的全面变化。现在，一个人可以从实验室转移到字段，将动作连接到行为并收集纵向数据。反过来，这使得以新的方式了解偏见及其对工程教育的影响：通过通过被动数据收集来补充自我报告，大数据可用于创建行为形象，同时了解特定代表性不足的少数民族和女工程专业的学生面对。该项目将导致一个独特的强大纵向数据集，该数据集捕获了学生体验的实时变化，并允许研究各种环境中歧视的影响。该项目将量化对行为的范围，方向和纵向影响，并将其与GPA和保留等长期结局联系起来。在过去的歧视研究中，缺乏将行为连接到该领域体验的能力。由于样本较大（n = 200/年）和变量数量，需要捕获单个方差和查看不相等的观测值的分析技术，例如层次线性建模。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计划。该研究将支持工程计划中的政策制定和干预设计。最终的目标是使工程专业的学生库多样化，这将通过增加代表性和参与工程和计算机科学劳动力的观点范围来直接利益。该奖项反映了NSF的法定任务，并被认为值得支持。通过使用基金会的智力优点和更广泛影响的评论标准进行评估。

项目成果

2021, April. Understanding health and behavioral trends of successful students through machine learning models. In International Conference on Human Interaction and Emerging Technologies (pp. 516-525). Springer, Cham.

2021 年，四月。

• DOI: 10.1007/978-3-030-74009
• 发表时间: 2021
• 期刊: International Conference on Human Interaction and Emerging Technologies
• 作者: Kim, A.;Nikseresht, F.;Dutcher, J.M.;Tumminia, M.;Villalba, D.;Cohen, S.;Creswell, K.;Creswell, D.;Dey, A.K.;Mankoff, J.
• 通讯作者: Mankoff, J.

Distress among undergraduates: Marginality, stressors and resilience resources

本科生的困境：边缘性、压力源和复原力资源

• 发表时间: 2021
• 期刊: Journal of American college health
• 影响因子: 2.4
• 作者: Nurius, P. S.

A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams

用于根据移动和可穿戴数据流对生物行为节律进行建模的计算框架

• DOI: 10.1101/2020.08.10.244020
• 发表时间: 2021
• 期刊: ACM transactions on intelligent systems and technology
• 影响因子: 5
• 作者: Yan, Runze

TypeOut: Leveraging Just-in-Time Self-Affirmation for Smartphone Overuse Reduction

TypeOut：利用即时自我肯定减少智能手机过度使用

• DOI: 10.1145/3491102.3517476
• 发表时间: 2022
• 期刊: CHI Conference on Human Factors in Computing Systems
• 作者: Xu, Xuhai;Zou, Tianyuan;Xiao, Han;Li, Yanzhang;Wang, Ruolin;Yuan, Tianyi;Wang, Yuntao;Shi, Yuanchun;Mankoff, Jennifer;Dey, Anind K
• 通讯作者: Dey, Anind K

GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization

• DOI: 10.48550/arxiv.2211.02733
• 发表时间: 2022-11
• 期刊: ArXiv
• 作者: Xuhai Xu;Han Zhang;Yasaman S. Sefidgar;Yiyi Ren;Xin Liu;Woosuk Seo;Jennifer Brown;K. Kuehn;Michael Merrill;P. Nurius;Shwetak N. Patel;Tim Althoff;Margaret E. Morris;E. Riskin;Jennifer Mankoff;A. Dey