Effect of screen configuration and interaction devices in shared display groupware
Bibliographic record
Abstract
Interactive tabletop and wall surfaces support collaboration and interactivity in novel ways. Apart from the traditional keyboards and mice, such systems can also incorporate other input devices, namely laser pointers, marker pens with screen location sensors, or touch-sensitive surfaces. Similarly, instead of a vertically positioned desktop monitor, collaborative setups typically use much larger displays, which are oriented either vertically (wall) or horizontally (tabletop), or combine both kinds of surfaces. In this paper we describe an empirical study that investigates how system constraints can affect group performance in high pace collaborative tasks. For this, we compare various input and output alternatives in a system that consists of interactive tabletop and wall surface(s). We observed that the performance of a group of people scaled almost linearly with the number of participants on an (almost perfectly) parallel task. We also found that mice were significantly faster than laser pointers, but only by 21%. Also, interaction on walls was significantly faster than on the tabletop, by 51%. Categories and Subject Descriptors H.5.2.h [User Interfaces]: Input devices and strategies; H.5.3.c
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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 | 0.003 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".