Reducing interference in single display groupware through transparency. In: Proc ECSCW. Kluwer, Dordrecht, The Netherlands Saul Greenberg is a Full Professor in the Department of Computer Science at the University of Calgary. The work by Saul and his tale
Bibliographic record
Abstract
Abstract. Single Display Groupware (SDG) supports face-to-face collaborators working over a single shared display, where all people have their own input device. Although SDG is simple in concept, there are surprisingly many problems in how interactions within SDG are managed. One problem is the potential for interference, where one person can raise an interface component (such as a menu or dialog box) in a way that hinders what another person is doing i.e., by obscuring another person’s working area that happens to be underneath the raised component. We propose transparent interface components as one possible solution to interference: while one person can raise and interact with the component, others can see through it and can continue to work underneath it. To test this concept, we first implemented a simple SDG game using both opaque and transparent SDG menus. Through a controlled experiment, we then analysed how interference affects peoples ’ performance across an opaque and transparent menu condition: a solo condition (where a person played alone) acts as our control. Our results show that the transparent menu did lessen the effect of interference, and that SDG players overwhelmingly preferred it to opaque menus.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".