Future delta 2.0 an experiential learning context for a serious game about local climate change
Abstract
In this paper we discuss the theoretical, design and evaluative underpinnings of the experiential learning context central to the design processes of the Future Delta 2.0 serious game. The game is aimed at facilitating understanding and action on local climate change. We begin with a discussion of play as it relates to designing serious games. Then we articulate the experiential learning context revealed through three interconnected design strands: meaningful learning objectives -- how the learning is structured; situatedness -- where the learning takes place, geographically and culturally; learning through action -- how learning happens through play. We introduce the experiential learning context of Future Delta 2.0, a virtual 3D game. The game reaches across art, science and technology to communicate a community-based local vision of climate change challenges and solutions in Delta, British Columbia. Finally, we discuss the design, evaluation methods and analysis of the Future Delta 2.0 experiential learning context. Our conclusion is that the experiential learning context may contribute theoretically and practically to the research and design of 3D serious games.
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ML can help create personalized educational tools for climate learning, for instance by generating images of potential future impacts of extreme weather events based on a learner's address or by anchoring learning in a digital replica of their real-life location.
“In these contexts, ML can help create personalized educational tools, for instance by generating images of potential future impacts of extreme weather events based on a learner's address [716] or by anchoring an individual's learning experience in a digital replica of their real-life location and allowing them to explore the way that climate change will impact a specific location [28].”
From Tackling climate change with machine learning (2023) · cites this paper as [28]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Angel, J., LaValle, A., Iype, D. M., Sheppard, S., & Dulic, A.
- Affiliations
- University of British Columbia (CA)
- Venue
- SIGGRAPH Asia 2015 Symposium on Education
- Publisher
- ACM
- Published
- 2015-11-02
- Pages
- 1-10
- OpenAlex
- W2243100874
- Cited by
- 22
- References
- 35
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 6 · 2021: 3 · 2020: 1 · 2019: 1
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- SIGGRAPH Asia Symposium on Education (publishedVersion)
- Funders
- Ministry of Environment
- University of British Columbia
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Social Sciences › Sociology and Political Science › Digital Games and Media
- Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
- Keywords
- serious games, experiential learning, local climate change, game design, play, situated learning, learning through action, game evaluation
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