Effects of Bend Gesture Training on Learnability and Memorability in a Mobile Game
Bibliographic record
Abstract
Bend gestures can be used as a form of Around Device Interaction to address usability issues in touchscreen mobile devices. Yet, it is unclear whether bend gestures can be easily learned and memorized as control schema for games. To answer this, we built a novel deformable smartphone case that detects bend gestures at its corners and sides, and created PaperNinja, a mobile game that uses bends as input. We conducted a study comparing the effect of three pre-game training levels on learnability and memorability: no training, training of the bend gestures only, and training of both the bend gestures and their in-game action mapping. We found that including gesture-mapping positively impacted the initial learning (faster completion time and fewer gestures performed), but had a similar outcome as no training on memorability, while the gestures-without-mapping led to a negative outcome. Our findings suggest that players can learn bend gestures by discovery and training is not essential.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".