Contributor
Marc Tyler Sager
Subject Area
Education
Abstract
This dissertation explores the integration of data science practices with social justice principles, particularly within the context of food justice. The research is driven by two primary questions: (1) What are the differences in data science practices when applied to social good projects? (2) What critical tensions arise when balancing simplicity and complexity in data science for social good?
The findings explore the differences in data science practices and highlights critical tensions such as simplicity versus complexity, balancing constraints, and addressing food justice through data science. These tensions are summarized to provide insights into the stances adopted by participants. Overall, this dissertation contributes to the understanding of how data science can be harnessed to promote social justice, offering valuable insights for educators, practitioners, and researchers in the field.
Degree Date
Summer 2024
Document Type
Dissertation
Degree Name
Ph.D.
Department
Teaching and Learning
Advisor
Anthony Petrosino
Second Advisor
Jeanna Wieselmann
Third Advisor
Candace Walkington
Fourth Advisor
Ryan "Seth" Jones
Acknowledgements
This material is based on work supported by the National Science Foundation under Grant SBE 2150505. The opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Number of Pages
217
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Sager, Marc, "“It’s Hard to Quantify Community Togetherness”: Exploring The Evolution Of Data Science Practices And Uncovering Critical Tensions" (2024). Teaching and Learning Theses and Dissertations. 24.
https://scholar.smu.edu/simmons_dtl_etds/24
Included in
Data Science Commons, Educational Technology Commons, Food Security Commons, Higher Education Commons, Social Justice Commons
