Reverse engineering of content to find usability problems: a healthcare case study
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.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".