Project-Based Learning with a Social Impact: Connecting Data Science Movements, Civic Statistics, and Service-Learning

Leid Zejnilović () and Pedro Campos ()
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Leid Zejnilović: Nova School of Business and Economics, Nova University of Lisbon
Pedro Campos: LIAAD-INESC TEC and University of Porto

Chapter Chapter 21 in Statistics for Empowerment and Social Engagement, 2022, pp 537-562 from Springer

Abstract: Abstract Ever since there has been an organized collection and use of data for informing decision making, there has been a debate about the extent to which these data have been put to the best use for improving social welfare in terms of general well-being of a community or an entire society. This chapter offers a contribution to that debate, showing how different facets of civic statistics can be translated into action that delivers social impact. We first introduce data movements and how they emerged as a response to the unmet need for data science services to scale social impact of nonprofit and governmental organizations. These movements focused on feasible hands-on projects which are simultaneously educational, impactful, and scalable. Their success is notable, and their operational model applicable in the context of formal educational organizations, as we show using two exemplary cases. The cases offer insights about how organizations can engage with society through civic action and applied data science to create new academic and training programs. Our intention is to share the lessons learned from the data movements and their interactions with educational institutions, also in the context of service-learning, to inspire others to create exciting, engaging educational programs with lasting social impact.

Keywords: Project based learning; Data science for social good; DSSG; Data science; Machine learning; Data movements (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-20748-8_21

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DOI: 10.1007/978-3-031-20748-8_21

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