Designing Doable and Locally-adapted Action Cards for an Interactive Tabletop Game To Support Bottom-Up Flood Resilience
August 22, 2025 Β· Declared Dead Β· π ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Linda Hirsch, James Fey, Katherine Isbister
arXiv ID
2508.16480
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play
Last Checked
4 months ago
Abstract
Serious games can support communities in becoming more flood resilient. However, the process of identifying and integrating locally relevant and doable actions into gameplay is complex and underresearched. We approached the challenge by collaborating with a community-led education center and applying an iterative and participatory design process of identifying and defining actions that may increase local applicability and relevance. The process comprised a field observation, two expert focus groups (n=4), and an online survey (n=13). Our findings identified 27 actions related to increasing or maintaining individuals' and communities' flood resilience, which we turned into 20 playing cards. These action cards are a part of a larger interactive tabletop game, which we are currently developing. Our work discusses the potential of card games to educate non-experts to increase flood resilience, and contributes to our process of identifying local needs and conditions, and turning them into engaging game artifacts for bottom-up empowerment.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted