Usability of a Mobile App for Improving Literacy in Children With Hearing Impairment: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Children with hearing loss, even those identified early and who use hearing aids or cochlear implants, may face challenges in developing spoken language and literacy. This can lead to academic, behavioral, and social difficulties. There are apps for healthy children to improve their spoken language and literacy and apps that focus on sign language proficiency for children with hearing loss, but these apps are limited for children with hearing loss. Therefore, we have developed an app called Hear Me Read, which uses enhanced digital stories as therapy tools for speech, language, and literacy for children with hearing loss. The platform has therapist and parent/child modes that allow (1) the selection of high-quality, illustrated digital stories by a speech-language pathologist, parent, or child; (2) the modification of digital stories for a multitude of speech and language targets; and (3) the assignment of stories by a therapist to facilitate individualized speech and language goals. In addition, Hear Me Read makes the caregiver a core partner in engagement through functionality, whereby the caregiver can record video and audio of themselves to be played back by the child. OBJECTIVE: This study aimed to evaluate the user experience of the Hear Me Read app through a focus group study with caregivers and their children. METHODS: We recruited 16 participants (8 children with and without hearing loss and 8 caregivers) to participate in 1-hour focus groups. Caregivers and children interacted with the app and discussed their experiences through a semistructured group interview. We employed thematic analysis methods and analyzed the data. We used feedback from the focus group to improve the elements of the app for a larger clinical trial assessing the impact of the app on outcomes. RESULTS: We identified three themes: default needs, specific needs, and family needs. Participants found the app to be esthetically pleasing and easy to use. The findings of this study helped us to identify usability attributes and to amend app functionalities to best fit user needs. Caregivers and children appreciated the enhancements, such as highlighting of parts of speech and caregiver reading of video playback, which were made possible by the digital format. Participants expressed that the app could be used to enhance family reading sessions and family interaction. CONCLUSIONS: The findings from this focus group study are promising for the use of educational apps designed specifically for those with hearing loss who are pursuing listening and spoken language as a communication outcome. Further investigation is needed with larger sample sizes to understand the clinical impact on relevant language and literacy outcomes in this population.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".