A Framework for Exploring and Evaluating Mechanics in Human Computation Games
June 11, 2017 Β· Declared Dead Β· π International Conference on Foundations of Digital Games
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kristin Siu, Alexander Zook, Mark O. Riedl
arXiv ID
1706.03311
Category
cs.HC: Human-Computer Interaction
Citations
11
Venue
International Conference on Foundations of Digital Games
Last Checked
4 months ago
Abstract
Human computation games (HCGs) are a crowdsourcing approach to solving computationally-intractable tasks using games. In this paper, we describe the need for generalizable HCG design knowledge that accommodates the needs of both players and tasks. We propose a formal representation of the mechanics in HCGs, providing a structural breakdown to visualize, compare, and explore the space of HCG mechanics. We present a methodology based on small-scale design experiments using fixed tasks while varying game elements to observe effects on both the player experience and the human computation task completion. Finally we discuss applications of our framework using comparisons of prior HCGs and recent design experiments. Ultimately, we wish to enable easier exploration and development of HCGs, helping these games provide meaningful player experiences while solving difficult problems.
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