WoW -- A System for Self-Service Collaborative Design Workshops
August 19, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ilyasse Belkacem, Vasile Ciorna, Frank Petry, Mohammad Ghoniem
arXiv ID
2408.09926
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
In many working environments, users have to solve complex problems relying on large and multi-source data. Such problems require several experts to collaborate on solving them, or a single analyst to reconcile multiple complementary standpoints. Previous research has shown that wall-sized displays supports different collaboration styles, based most often on abstract tasks as proxies of real work. We present the design and implementation of WoW, short for ``Workspace on Wall'', a multi-user Web-based portal for collaborative meetings and workshops in multi-surface environments. We report on a two-year effort spanning context inquiry studies, system design iterations, development, and real testing rounds targeting design engineers in the tire industry. The pneumatic tires found on the market result from a highly collaborative and iterative development process that reconciles conflicting constraints through a series of product design workshops. WoW was found to be a flexible solution to build multi-view set-ups in a self-service manner and an effective means to access more content at once. Our users also felt more engaged in their collaborative problem-solving work using WoW than in conventional meeting rooms.
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