Usability testing of a multimedia e‐learning resource for electrolyte and acid‐base disorders
Bibliographic record
Abstract
Abstract The usability of computer interfaces may have a major influence on learning. Design approaches that optimize usability are commonplace in the software development industry but are seldom used in the development of e‐learning resources, especially in medical education. We conducted a usability evaluation of a multimedia resource for teaching electrolyte and acid‐base disorders by studying the interaction of 15 medical doctors with the application. Most of the usability problems occurred in an interactive treatment simulation, which was completed successfully by only 20% of participants. A total of 27 distinct usability problems were detected, with 15 categorized as serious. No differences were observed with respect to usability problems detected by junior doctors as compared with more experienced colleagues. Problems were related to user information and feedback, the visual layout, match with the real world, error prevention and management, and consistency and standards. The resource was therefore unusable for many participants; this is in contrast to good scores previously reported for subjective user satisfaction. The findings suggest that the development of e‐learning materials should follow an iterative design‐and‐test process that includes routine usability evaluation. User testing should include the study of objective measures and not rely only on self‐reported measures of satisfaction.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".