Improving the learnability of mobile devices for older adults
Bibliographic record
Abstract
Mobile computing devices, such as smart phones, offer benefits that may be especially valuable to older adults (age 65+). However older adults have been shown to have difficulty learning to use these devices, which is a barrier for technology adoption. The main goal of the research reported in this dissertation was to investigate three promising design approaches – increasing the interpretability of graphical icons, incorporating a multi-layered interface, and augmenting the mobile device’s display – to determine whether each can improve the learnability of mobile devices for older adults. We involved both older and younger adults in our studies to uncover benefits unique to older adults. In our investigation of graphical icons, we conducted an experiment to determine which icon characteristics affect initial icon interpretability for older adults. We found that icon interpretability can be improved for older adults by reducing the semantic distance between the objects depicted in the icon and the icon’s function, and by labelling icons. In our investigation of multi-layered interfaces, we prototyped a two-layer smart phone address book and conducted an experiment to assess its learnability over four learning phases. We found that the multi-layered interface, compared to a non-layered full-functionality control interface, provided greater benefits for older participants than for younger participants in terms of faster task completion times during initial learning, lower perceived interface complexity, and greater interface preference for learning. In our investigation of augmenting a mobile device display with a larger display to help older adults learn new devices, we conducted a comprehensive survey of older adults’ learning needs and preferences. Based on the survey findings, we designed and prototyped Help Kiosk, an augmented display system for helping older adults to learn to use a smart phone. An informal evaluation found preliminary evidence that Help Kiosk may be able to assist older adults in performing new mobile phone tasks. Through these three investigations, our research identified and validated design approaches that researchers and developers can use to improve the learnability of mobile devices for older adults, which should increase the chances of technology adoption.
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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 | 0.012 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".