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

Lead Staff:
Jessica Karch
Traci Higgins
Jim Hammerman

Project Summary

Exploring Students’ Data Science Learning at DataFest is a 2 year exploratory IUSE study led by ɫƵ staff of , a two-day long co-curricular data competition sponsored by the American Statistical Association and hosted at more than 40 sites with over 2000 participants and 100 participating institutions annually.

Partnering with 6 DataFest sites, the project aims 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.

Research Activity

This study will use multimodal data streams that include surveys, interviews with DataFest teams, focus groups with organizers, close observations and video recordings of teams working, and video recordings of final presentations.

Impact

This study will generate new knowledge about how students leverage interdisciplinary thinking when working with messy, authentic data, as well as develop a pilot instrument that can be used to assess interdisciplinarity. Additionally, the project team will work directly with DataFest organizers to strategize about how to broaden participation at their sites.