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Fostering the Development of Earth Data Science Skills in a Diverse Community of Online Learners: A Case Study of the Earth Data Science Corps

Authors :
Nathan A. Quarderer
Leah Wasser
Anne U. Gold
Patricia Montaño
Lauren Herwehe
Katherine Halama
Emily Biggane
Jessica Logan
David Parr
Sylvia Brady
James Sanovia
Charles Jason Tinant
Elisha Yellow Thunder
Justina White Eyes
LaShell Poor Bear/Bagola
Madison Phelps
Trey Orion Phelps
Brett Alberts
Michela Johnson
Nathan Korinek
William Travis
Naomi Jacquez
Kaiea Rohlehr
Emily Ward
Elsa Culler
R. Chelsea Nagy
Jennifer Balch
Source :
Journal of Statistics and Data Science Education, Pp 1-13 (2024)
Publication Year :
2024
Publisher :
Taylor & Francis Group, 2024.

Abstract

Today’s data-driven world requires earth and environmental scientists to have skills at the intersection of domain and data science. These skills are imperative to harness information contained in a growing volume of complex data to solve the world’s most pressing environmental challenges. Despite the importance of these skills, Earth and Environmental Data Science (EDS) training is not equally accessible, contributing to a lack of diversity in the field. This creates a critical need for EDS training opportunities designed specifically for underrepresented groups. In response, we developed the Earth Data Science Corps (EDSC) which couples a paid internship for undergraduate students with faculty training to build capacity to teach and learn EDS using Python at smaller Minority Serving Institutions. EDSC faculty participants are further empowered to teach these skills at their home institutions which scales the program beyond the training lead by our team. Using a Rasch modeling approach, we found that participating in the EDSC program had a significant impact on undergraduate learners’ comfort and confidence with technical and nontechnical data science skills, as well as their science identity and sense of belonging in science, two critical aspects of recruiting and retaining members of underrepresented groups in STEM. Supplementary materials for this article are available online.

Details

Language :
English
ISSN :
26939169
Database :
Directory of Open Access Journals
Journal :
Journal of Statistics and Data Science Education
Publication Type :
Academic Journal
Accession number :
edsdoj.246017268854422a48500d6816de659
Document Type :
article
Full Text :
https://doi.org/10.1080/26939169.2024.2362886