Bibliographic record
Abstract
Ease of use with digital tabletops in public settings is contingent on how well the system invites and guides interaction. The same can be said for the interface design and individual graphical user interface elements of these systems. One such interface element is menus. Prior to a menu being used however, it must first be discovered within the interface. Existing research pertaining to digital tabletop menu design does not address this issue of discovering or opening a menu. This thesis investigates how the interface and interaction of digital tabletops can be designed to encourage menu discoverability in the context of public settings. \n \nA set of menu invocation designs varying on the invocation element and use of animation are proposed. These designs are then evaluated through an observational study at a museum to observe users interactions in a realistic public setting. Findings from this study propose the use of discernible and recognizable interface elements – buttons – supported by the use of animation to attract and guide users as a discoverable menu invocation design. Additionally, findings posit that when engaging with a public digital tabletop display, users transition through exploration and discovery states before becoming competent with the system. Finally, insights from this study point to a set of design recommendations for improving menu discoverability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Open science | 0.001 | 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".