Usability testing of two e-learning resources: Methods to maximize potential for clinician use
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Rigorous usability testing of e-learn-ing resources is an important prerequisite to their wide-spread use among clinicians. This study demonstrates the application of an evidence-based approach to usability testing of two stroke-related e-learning resources (StrokEngine). METHODS: 14 stroke rehabilitation clinicians (occupational therapists and physiotherapists) from Ontario, Canada participated in a 1.5 h in-person testing session. Clinicians navigated StrokEngine in search of information to answer questions on stroke assessment/intervention. Their search patterns were observed and clinicians provided verbal/written feedback about StrokEngine. Content analysis was used to generate themes and categorize them under two broad categories: facilitators and barriers to use. RESULTS: Five key facilitators and three key barriers to Strok-Engine use were identified and related to screen format, layout/organization, ease of navigation, quality of content, likelihood of using StrokEngine in the future, and system dysfunctions. All 14 clinicians were very or extremely satisfied with the layout/organization, quality and clinical relevance of the content, stating that they were likely to use StrokEngine in the future. CONCLUSION: All identified barriers from this study were addressed with website modifications in order to maximize the usability and navigability of StrokEngine. This rigorous methodology for usability testing can be applied during the design process of any e-learning resource.
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.
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 | 0.146 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".