Concurrent Think-Aloud Verbalizations and Usability Problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The concurrent think-aloud protocol—in which participants verbalize their thoughts when performing tasks—is a widely employed approach in usability testing. Despite its value, analyzing think-aloud sessions can be onerous because it often entails assessing all of a user's verbalizations. This has motivated previous research on developing categories to segment verbalizations into manageable units of analysis. However, the way in which a category might relate to usability problems is currently unclear. In this research, we sought to address this gap in our understanding. We also studied how speech features might relate to usability problems. Through two studies, this research demonstrates that certain patterns of verbalizations are more telling of usability problems than others and that these patterns are robust to different types of test products (i.e., physical devices and digital systems), access to different types of information (i.e., video and audio modality), and the presence or absence of a visualization of verbalizations. The implication is that the verbalization and speech patterns can potentially reduce the time and effort required for analysis by enabling evaluators to focus more on the important aspects of a user's verbalizations. The patterns could also potentially be used to inform the design of systems to automatically detect when in the recorded think-aloud sessions users experience problems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it