Designing a Co-located Collaborative Cross-device Game for Ad Hoc Social Settings
Bibliographic record
Abstract
Collaborative games, played across multiple co-located mobile devices are gaining popularity because of their location flexibility, low hardware requirements, players’ close proximity, and their promise of generating excitement and collaborative social experience. Even though the integration of spatial device rearrangement (i.e., physically picking up a device and positioning it in another location) in cross-device applications in other domains has demonstrated higher potential for collaboration, this form of interaction remains relatively unexplored in ad hoc cross-device gaming applications. That is because these interactions typically require additional hardware support, which can hinder the ad hoc nature of the cross-device social games. In this paper, we present the iterative user-centred design process of creating an ad hoc co-located cross-device maze exploration game named Snap-To-Eat that supports both on-screen cross-device interactions (e.g., moving elements across devices) and spatial device rearrangement interactions without requiring any additional hardware. We refined the game design by conducting two rounds of workshops, the first with a low-fidelity paper prototype and the second with a high-fidelity digital prototype, with 36 undergraduate and graduate students in total. Findings from both workshops demonstrated that integrating spatial device rearrangement interactions in an ad hoc co-located cross-device game created opportunities for collaboration and engagement among the players, which elevated their overall social experience. These findings also suggested new design opportunities and future research directions for ad hoc collocated cross-device games, e.g., introducing new interactions, as well as exploring the game mechanics in a 3D space to elevate the gaming experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".