From Classroom to Community: Evaluating Data Science Practices in Education and Social Justice Projects

Critical data literacy (CDL) has emerged as a crucial component in data science education, transcending traditional disciplinary boundaries. Promoting CDL requires collaborative approaches to enhance learners’ skills in data science, going beyond mere quantitative reasoning to encompass a comprehens...

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Main Authors: Marc T. Sager, Jeanna R. Wieselmann, Anthony J. Petrosino
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Education Sciences
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Online Access:https://www.mdpi.com/2227-7102/15/7/878
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author Marc T. Sager
Jeanna R. Wieselmann
Anthony J. Petrosino
author_facet Marc T. Sager
Jeanna R. Wieselmann
Anthony J. Petrosino
author_sort Marc T. Sager
collection DOAJ
description Critical data literacy (CDL) has emerged as a crucial component in data science education, transcending traditional disciplinary boundaries. Promoting CDL requires collaborative approaches to enhance learners’ skills in data science, going beyond mere quantitative reasoning to encompass a comprehensive understanding of data workflows and tools. Despite the growing literature on CDL, there is still a need to explore how students use data science practices for supporting the learning of CDL throughout a summer-long data science program. Drawing on situative perspectives of learning, we utilize a descriptive case study to address our research question: How do data science practices taught in a classroom setting differ from those enacted in real-world social justice projects? Key findings reveal that while the course focused on abstract principles and basic technical skills, the Food Justice Project provided students with a more applied understanding of data tools, ethics, and exploration. Through the project, students demonstrated a deeper engagement with CDL, addressing real-world issues through detailed data analysis and ethical considerations. This manuscript adds to the literature within data science education and has the potential to bridge the gap between theoretical knowledge and practical application, preparing students to address real-world data science challenges through their coursework.
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spelling doaj-art-5e69afc919f94d4ea4ee0b2dfcce794a2025-08-20T03:58:31ZengMDPI AGEducation Sciences2227-71022025-07-0115787810.3390/educsci15070878From Classroom to Community: Evaluating Data Science Practices in Education and Social Justice ProjectsMarc T. Sager0Jeanna R. Wieselmann1Anthony J. Petrosino2The Budd Center, Southern Methodist University, Dallas, TX 75275-0509, USADepartment of Teaching and Learning, Southern Methodist University, Dallas, TX 75275-0455, USADepartment of Teaching and Learning, Southern Methodist University, Dallas, TX 75275-0455, USACritical data literacy (CDL) has emerged as a crucial component in data science education, transcending traditional disciplinary boundaries. Promoting CDL requires collaborative approaches to enhance learners’ skills in data science, going beyond mere quantitative reasoning to encompass a comprehensive understanding of data workflows and tools. Despite the growing literature on CDL, there is still a need to explore how students use data science practices for supporting the learning of CDL throughout a summer-long data science program. Drawing on situative perspectives of learning, we utilize a descriptive case study to address our research question: How do data science practices taught in a classroom setting differ from those enacted in real-world social justice projects? Key findings reveal that while the course focused on abstract principles and basic technical skills, the Food Justice Project provided students with a more applied understanding of data tools, ethics, and exploration. Through the project, students demonstrated a deeper engagement with CDL, addressing real-world issues through detailed data analysis and ethical considerations. This manuscript adds to the literature within data science education and has the potential to bridge the gap between theoretical knowledge and practical application, preparing students to address real-world data science challenges through their coursework.https://www.mdpi.com/2227-7102/15/7/878data science educationdata science practicescritical data literacyfood justicesituated learninghigher education
spellingShingle Marc T. Sager
Jeanna R. Wieselmann
Anthony J. Petrosino
From Classroom to Community: Evaluating Data Science Practices in Education and Social Justice Projects
Education Sciences
data science education
data science practices
critical data literacy
food justice
situated learning
higher education
title From Classroom to Community: Evaluating Data Science Practices in Education and Social Justice Projects
title_full From Classroom to Community: Evaluating Data Science Practices in Education and Social Justice Projects
title_fullStr From Classroom to Community: Evaluating Data Science Practices in Education and Social Justice Projects
title_full_unstemmed From Classroom to Community: Evaluating Data Science Practices in Education and Social Justice Projects
title_short From Classroom to Community: Evaluating Data Science Practices in Education and Social Justice Projects
title_sort from classroom to community evaluating data science practices in education and social justice projects
topic data science education
data science practices
critical data literacy
food justice
situated learning
higher education
url https://www.mdpi.com/2227-7102/15/7/878
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AT anthonyjpetrosino fromclassroomtocommunityevaluatingdatasciencepracticesineducationandsocialjusticeprojects