Multi-Layered Interfaces to Improve Older Adults’ Initial Learnability of Mobile Applications
Bibliographic record
Abstract
Mobile computing devices can offer older adults (ages 65+) support in their daily lives, but older adults often find such devices difficult to learn and use. One potential design approach to improve the learnability of mobile devices is a Multi-Layered (ML) interface, where novice users start with a reduced-functionality interface layer that only allows them to perform basic tasks, before progressing to a more complex interface layer when they are comfortable. We studied the effects of a ML interface on older adults’ performance in learning tasks on a mobile device. We conducted a controlled experiment with 16 older (ages 65--81) and 16 younger participants (age 21--36), who performed tasks on either a 2-layer or a nonlayered (control) address book application, implemented on a commercial smart phone. We found that the ML interface’s Reduced-Functionality layer, compared to the control’s Full-Functionality layer, better helped users to master a set of basic tasks and to retain that ability 30 minutes later. When users transitioned from the Reduced-Functionality to the Full-Functionality interface layer, their performance on the previously learned tasks was negatively affected, but no negative impact was found on learning new, advanced tasks. Overall, the ML interface provided greater benefit for older participants than for younger participants in terms of task completion time during initial learning, perceived complexity, and preference. We discuss how the ML interface approach is suitable for improving the learnability of mobile applications, particularly for older adults.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".