Online project-based learning to foster students’ course choices in data science: a longitudinal case study using Sankey visualization

Abstract Career choices are shaped by students’ experiences, knowledge, and skill sets across time, reflecting not only disciplinary interests but also exposure to evolving fields such as data science (DSC). Despite a surge in interest and enrollment in data science degrees, the United States faces a growing demand for data literacy across multiple sectors. Online learning environments have become entry points for students’ initial engagement with DSC, offering accessibility and supporting workforce needs. Nevertheless, the interdisciplinary essence of DSC means that clear career paths remain ambiguous, especially for those applying DSC knowledge within various disciplines. While national data sources provide valuable overviews of degree distributions, more granular analysis at the course level is warranted to understand nuanced student trajectories. Project-based online learning, though proven valuable in in-person settings, remains underexplored in online DSC education. This study employs curriculum analytics and Sankey diagram visualizations to investigate course enrollment patterns and career trajectories among students after enrolling in an introductory online project-based DSC course. We built a longitudinal dataset by following 35 students between Fall 2022 and Spring 2024, tracking their subsequent course enrollments over time. Demographic and academic data were sourced from institutional enrollment records, allowing subgroup analysis based on major, gender, race, first-generation status, and achievement. Our exploratory analysis reveals patterns indicating that continued DSC course enrollment appears prevalent among nonwhite, male, STEM-major, and academically proficient students, whereas first-generation students exhibit no persistence. We illustrate how Sankey diagrams, though not establishing causality, provide actionable insights for program and curriculum development in DSC education.

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Publication Details

Journal
International Journal of Educational Technology in Higher Education
Published
2025-10-19
DOI
https://doi.org/10.1186/s41239-025-00560-y
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00

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article

Online project-based learning to foster students’ course choices in data science: a longitudinal case study using Sankey visualization

Tom R. Leppard, Daniela Castellanos-Reyes
International Journal of Educational Technology in Higher Education
Online Learning and Analytics
article

Online project-based learning to foster students’ course choices in data science: a longitudinal case study using Sankey visualization

Tom R. Leppard, Daniela Castellanos-Reyes
article en

Abstract

Abstract Career choices are shaped by students’ experiences, knowledge, and skill sets across time, reflecting not only disciplinary interests but also exposure to evolving fields such as data science (DSC). Despite a surge in interest and enrollment in data science degrees, the United States faces a growing demand for data literacy across multiple sectors. Online learning environments have become entry points for students’ initial engagement with DSC, offering accessibility and supporting workforce needs. Nevertheless, the interdisciplinary essence of DSC means that clear career paths remain ambiguous, especially for those applying DSC knowledge within various disciplines. While national data sources provide valuable overviews of degree distributions, more granular analysis at the course level is warranted to understand nuanced student trajectories. Project-based online learning, though proven valuable in in-person settings, remains underexplored in online DSC education. This study employs curriculum analytics and Sankey diagram visualizations to investigate course enrollment patterns and career trajectories among students after enrolling in an introductory online project-based DSC course. We built a longitudinal dataset by following 35 students between Fall 2022 and Spring 2024, tracking their subsequent course enrollments over time. Demographic and academic data were sourced from institutional enrollment records, allowing subgroup analysis based on major, gender, race, first-generation status, and achievement. Our exploratory analysis reveals patterns indicating that continued DSC course enrollment appears prevalent among nonwhite, male, STEM-major, and academically proficient students, whereas first-generation students exhibit no persistence. We illustrate how Sankey diagrams, though not establishing causality, provide actionable insights for program and curriculum development in DSC education.

International Journal of Educational Technology in Higher EducationVol. 22(1)
North Carolina State University (US)
National Science Foundation
Openalex Percentile: Top 67%
Online Learning and Analytics
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