Fostering the development of Earth data science skills in a diverse community of online learners: A case study of the Earth Data Science Corps

Quarderer, N. A., Wasser, L., Gold, A. U., Montaño, P. A., Herwehe, L., et al. (2025). Fostering the development of Earth data science skills in a diverse community of online learners: A case study of the Earth Data Science Corps. Journal of Statistics and Data Science Education, doi:https://doi.org/10.1080/26939169.2024.2362886

Title Fostering the development of Earth data science skills in a diverse community of online learners: A case study of the Earth Data Science Corps
Genre Article
Author(s) N. A. Quarderer, L. Wasser, A. U. Gold, Patricia A. Montaño, L. Herwehe, K. Halama, E. Biggane, J. Logan, D. Parr, S. Brady, J. Sanovia, C. J. Tinant, E. Yellow Thunder, J. White Eyes, L. Poor Bear/Bagola, M. Phelps, T. O. Phelps, B. Alberts, M. Johnson, N. Korinek, W. Travis, N. Jacquez, K. Rohlehr, E. Ward, E. Culler, R. C. Nagy, J. Balch
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.
Publication Title Journal of Statistics and Data Science Education
Publication Date Jan 1, 2025
Publisher's Version of Record https://doi.org/10.1080/26939169.2024.2362886
OpenSky Citable URL https://n2t.net/ark:/85065/d7dv1q67
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