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和促进历史上被边缘化的学生参与数据科学来服务于国家利益。它将通过研究DataFest来做到这一点，DataFest是一个快速发展的全国性课外数据竞赛，每年有来自100多个机构的2000多名参与者。DataFest是学生在两天紧张的时间里与真实数据集协作的机会。随着技术和计算能力的不断快速发展，确保本科生拥有处理和理解大型混乱数据集的技能至关重要。该项目旨在更深入、更根本地了解学生如何处理和思考这些类型的数据，以及他们如何共同努力，利用他们的其他专业知识来调查这些类型的数据集。同样至关重要的是，要确保公平地发展这些技能。虽然传统的竞争性黑客模式被发现对边缘化学生具有排斥性，但DataFest设计的几个方面可能会更好地促进包容性，例如更多的合作机会和社会相关任务。因此，该项目还将试图了解目前谁参加和不参加DataFest以及为什么参加。 这个项目的范围包括从多个DataFest网站收集数据，以便（1）更好地了解本科生如何浏览大的，混乱的，真实的数据，特别是，他们如何利用跨学科资源这样做;以及（2）研究谁参与了DataFest以及为什么参与，以探索DataFest如何可能成为一种工具，既可以扩大数据科学作为一门学科的参与，也可以促进跨学科学生的数据素养的发展。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，近距离观察和团队工作的视频记录，以及最终演示的视频记录。该项目将开发（1）丰富，详细的描述（a）团队在DataFest的背景下利用跨学科推理的方式，（B）数据调查过程的各个阶段受益于跨学科思维，以及（c）杂乱，真实数据的挑战，为跨学科思维提供切入点，融入解决方案。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。该奖项反映了NSF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.