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

Terc Inc, Cambridge MA

Investigators

Abstract

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.