Exploring Methods to Improve Pen-Based Menu Selection for Younger and Older Adults
Bibliographic record
Abstract
Tablet PCs are gaining popularity, but many individuals still struggle with pen-based interaction. In a previous baseline study, we examined the types of difficulties younger and older adults encounter when using pen-based input. The research reported in this article seeks to address one of these errors, namely, missing just below. This error occurs in a menu selection task when a user’s selection pattern is downwardly shifted, such that the top edge of the menu item below the target is selected relatively often, while the corresponding top edge of the target itself is seldom selected. We developed two approaches for addressing missing just below errors: reassigning selections along the top edge and deactivating them. In a laboratory evaluation, only the deactivated edge approach showed promise overall. Further analysis of our data revealed that individual differences played a large role in our results and identified a new source of selection difficulty. Specifically, we observed two error-prone groups of users: the low hitters, who, like participants in the baseline study, made missing just below errors, and the high hitters, who, in contrast, had difficulty with errors on the item above. All but one of the older participants fell into one of these error-prone groups, reinforcing that older users do need better support for selecting menu items with a pen. Preliminary analysis of the performance data suggests both of our approaches were beneficial for the low hitters, but that additional techniques are needed to meet the needs of the high hitters and to address the challenge of supporting both groups in a single interface.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".