Applying Work Domain Analysis to Augmentative and Alternative Communication Systems
Bibliographic record
Abstract
Augmentative and alternative communication (AAC) systems enable communication for individuals with complex communication needs. The frequency users rely on the communication systems necessitates an interface design that supports usability. As a first step to systematically designing a user interface, the Work Domain Analysis (WDA) framework was applied to a popular commercial AAC system, Proloquo2Go, to analyze and understand system constraints and design a new user interface for an AAC system. For the purpose of the analysis, the system boundaries were that of verbal language-based communication. Information for the analysis of the system was gathered through exploratory operation of AAC systems and structured interviews with a speech-language pathologist and parents of AAC system users. Research into the process of language formation showed logical groupings of the different types of language components to communicate information regarding states, objects, relationships, and emotions; component types were subsequently grouped for high-level communication of ideas and requests. An abstraction hierarchy was developed to represent the various levels of abstraction of the system from which information requirements were determined. This paper presents a novel application of WDA for AAC systems. Results derived from the application of WDA have yielded valuable design considerations for an AAC system user.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".