Reverse engineering of content to find usability problems: a healthcare case study
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
For tools that involve the creation of an artifact or document, reverse engineering potentially provides an interesting alternative to task-based usability testing. In this case study, participants were shown an artifact and asked to recreate it using a software tool. Would the reverse engineering testing method be as successful as traditional task-based methods in uncovering usability problems? Would test participants be comfortable using the method? Participants used both reverse engineering and task-based approaches to usability testing in counterbalanced order. Using an online tool for developing asthma action plans, the reverse engineering method uncovered more usability problems than the traditional task-based usability testing method. The 12 test participants had a positive attitude towards the reverse engineering method although it took them longer to perform their tasks and they faced a greater number of issues. Both the longer task time and the greater number of problems uncovered were likely caused by the greater attention to detail that reverse engineering requires of participants. This case study demonstrates that reverse engineering may be a useful alternative to pre-defining the tasks for the participant when carrying out a usability test.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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