Gesturing in the Wild: Understanding the Effects and Implications of Gesture-Based Interaction for Dynamic Presentations
Bibliographic record
Abstract
Driven by the increasing availability of low-cost sensing hardware, gesture-based input is quickly becoming a viable form of interaction for a variety of applications. Electronic presentations (e.g., PowerPoint, Keynote) have long been seen as a natural fit for this form of interaction. However, despite 20 years of prototyping such systems, little is known about how gesture-based input affects presentation dynamics, or how it can be best applied in this context. Instead, past work has focused almost exclusively on recognition algorithms. This paper explicitly addresses these gaps in the literature. Through observations of real-world practices, we first describe the types of gestures presenters naturally make and the purposes these gestures serve when presenting content. We then introduce Maestro, a gesture-based presentation system explicitly designed to support and enhance these existing practices. Finally, we describe the results of a real-world field study in which Maestro was evaluated in a classroom setting for several weeks. Our results indicate that gestures which enable direct interaction with slide content are the most natural fit for this input modality. In contrast, we found that using gestures to navigate slides (the most common implementation in all prior systems) has significant drawbacks. Our results also show how gesture-based input can noticeably alter presentation dynamics, often in ways that are not desirable.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| 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".