Evaluating the effectiveness of height visualizations for improving gestural communication at distributed tabletops
Bibliographic record
Abstract
In co-located collaboration, people use the space above the table for deictic gestures, and height is an important part of these gestures. However, when collaborators work at distributed tables, we know little about how to convey information about gesture height. A few visualizations have been proposed, but these have not been evaluated in detail. To better understand how remote embodiments can show gesture height, we developed several visualizations and evaluated them in three studies. First, we show that touch visualizations significantly improve people's accuracy in identifying the type and target of a gesture. Second, we show that visualizations of height above the table help to convey gesture qualities such as confidence, emphasis, and specificity. Third, we show that people quickly make use of height visualizations in realistic collaborative tasks, and that height-enhanced embodiments are strongly preferred. Our work illustrates several designs for effective visualization of height, and provides the first comprehensive evidence of the value of height information as a way to improve gestural communication in distributed tabletop groupware.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".