Bridging Private and Shared Interaction Surfaces in Collocated Groupware.
Bibliographic record
Abstract
Multi-display environments (such as the pairing of a digital tabletop computer with a set of handheld tablet computers) can support collocated interaction in groups by providing individuals with private workspaces that can be used alongside shared interaction surfaces. However, such a configuration necessitates the inclusion of intuitive and seamless interactions to move digital objects between displays. While existing research has suggested numerous methods to bridge devices in this manner, these methods often require highly specialized equipment and are seldom examined using real-world tasks. This thesis investigates the use of two cross-device object transfer methods as adapted for use with commonly-available hardware and applied for use in a realistic task, a familiar tabletop card game. \nA digital tabletop and tablet implementation of the tabletop card game Dominion is developed to support each of the two cross-device object transfer methods (as well as two different turn-taking methods to support user identification). An observational user study is then performed to examine the effect of the transfer methods on groups’ behaviour, examining player preferences and the strategies which players applied to pursue their varied goals within the game. The study reveals that players’ choices and use of the methods is shaped greatly by the way in which each player personally defines the Dominion task, not simply by the objectives outlined in its rulebook. Design considerations for the design of cross-device object transfer methods and lessons-learned for system and experimental design as applied to the gaming domain are also offered.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".