Evaluation of an Ecological Interface Design–Driven Augmentative and Alternative Communication Interface
Bibliographic record
Abstract
This study evaluated the change in usability, mental workload and information transfer rate associated with an augmentative and alternative communication (AAC) interface designed through ecological interface design (EID). The design and development process is detailed in Shea et al. (2021) . Digital AAC interfaces are considered high-tech interventions for individuals who experience complex communication needs (e.g., from etiologies such as cerebral palsy) and enable users to select language options from a visual display. Interface usability, mental workload and information transfer rate collectively influence users’ communication. Ten AAC-naïve participants engaged in three semi–scripted conversations (verbal, AAC-mediated commercial interface, and EID interfaces) with an actor. Augmentative and alternative communication interfaces were accessed through a single switch pathway. Information transfer rate, error rate, heart rate variability and subjective workload performance measures were recorded for every trial. During AAC-mediated trials, interface interactions were also documented. The EID AAC interface presented improved communication in 5 out of 7 performance measures ( p < .05). The EID AAC interface was associated with a significantly higher information transfer rate, lower error rate, less time elapsed between switch activations, less switch activation per word communicated and lower subjective workload. The application of EID to an AAC interface can lead to a significantly improved communication experience.
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 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 | 0.005 | 0.017 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".