Exploring Students’ Data Science Learning and Participation through Engagement with Authentic, Messy Data at DataFest

该项目旨在通过支持本科生数据素养技能的发展，促进历史上被边缘化的学生参与数据科学，为国家利益服务。它将通过研究DataFest来实现这一目标。DataFest是一项迅速发展的全国性联合课程数据竞赛，每年有来自100多所院校的2000多名参与者。DataFest是一个让学生在两天紧张的时间里使用真实数据集进行协作的机会。随着技术和计算能力的不断快速发展，确保本科生具备处理和理解大型、混乱数据集的技能至关重要。这个项目的目的是让学生对这些类型的数据有更深入、更基本的理解，以及他们如何一起工作，将他们的其他专业知识运用到调查这些类型的数据集中。确保这些技能得到公平发展也至关重要。虽然传统的竞争性黑客马拉松模式被发现排斥边缘学生，但DataFest的设计有几个方面可以更好地促进包容性，例如更多的合作机会和社会相关任务。因此，本项目还将试图了解谁目前参加和不参加DataFest，以及为什么。这样做是为了理解如何利用DataFest作为一个机会，使数据科学更容易获得、更吸引人、更受欢迎。该项目的范围包括从多个DataFest站点收集数据，以便：(1)更好地了解本科生如何浏览庞大、混乱、真实的数据，特别是他们如何在此过程中利用跨学科资源；(2)研究谁参与了DataFest以及为什么参与，以探索DataFest如何可能成为扩大数据科学作为一门学科的参与和促进跨学科学生数据素养发展的工具。调查人员将使用混合方法和多模式数据流，包括调查、与DataFest团队的访谈、与现场组织者的焦点小组、团队工作的近距离观察和视频记录，以及最终演示的视频记录。本项目将开发(1)丰富、详细的描述(a)团队在DataFest背景下利用跨学科推理的方式，(b)受益于跨学科思维的数据调查过程阶段，以及(c)混乱、真实数据的挑战，这些数据为跨学科思维融入解决方案提供了切入点。该项目还将开发(2)一种调查工具，可以开始评估跨学科思维的开放性；(3)通过DataFest等课外活动扩大STEM参与的初步见解。调查结果将被传播到参与网站、更大的DataFest社区，以及更广泛的STEM和数据科学教育领域。NSF IUSE: EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to serve the national interest by supporting the development of undergraduate students’ data literacy skills and by promoting the inclusion of historically marginalized students in data science. It will do so by studying DataFest, a rapidly growing national co-curricular data competition with over 2000 participants from over 100 institutions annually. DataFest is an opportunity for students to work collaboratively with authentic datasets over two intense days. As technology and computing power continue to rapidly advance, it is critical to ensure undergraduate students have the skills to work with and make sense of large, messy datasets. This project aims to develop a deeper and more fundamental understanding of how students work with and think about these types of data, and how they work together to bring their other expertise to bear in investigating these types of datasets. It is also crucial to ensure that these skills are developed equitably. Although the traditional competitive hackathon model has been found to be exclusionary to marginalized students, there are several aspects of the DataFest design that may better foster inclusion, such as more opportunities for collaboration and socially relevant tasks. Thus, this project will also seek to understand who currently does and does not participate in DataFest and why. This will be done to understand how DataFest can be leveraged as an opportunity to make data science more accessible, engaging, and welcoming to all.The scope of this project includes collecting data from multiple DataFest sites in order to (1) better understand how undergraduate students navigate big, messy, authentic data and, in particular, how they draw on interdisciplinary resources in doing so; and (2) examine who participates in DataFest and why, in order to explore how DataFest can potentially be a vehicle for broadening participation both in data science as a discipline and in fostering the development of data literacy for students across disciplines. The investigators will use mixed-methods and multimodal data streams that include surveys, interviews with DataFest teams, focus groups with site organizers, close observations and video recordings of teams working, and video recordings of final presentations. This project will develop (1) rich, detailed descriptions of (a) ways in which teams draw on interdisciplinary reasoning in the context of DataFest, (b) phases of the Data Investigation Process that benefit from interdisciplinary thinking, and (c) challenges of messy, authentic data that provide entry points for interdisciplinary thinking to become woven into the solution. This project will also develop (2) a survey instrument that can begin to assess openness to interdisciplinary thinking; and (3) initial insights into broadening participation in STEM through co-curricular events like DataFest. Findings will be disseminated to participating sites, the larger DataFest community, as well as to the broader field of STEM and data science education. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.