Gesturing in the Wild: Understanding the Effects and Implications of Gesture-Based Interaction for Dynamic Presentations
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it