Virtual Reality Instructional Design in Orthopedic Physical Therapy Education: A Mixed-Methods Usability Test
Bibliographic record
Abstract
Background Physical therapy education benefits from innovative and authentic learning opportunities. However, factors that influence the acceptance of educational technology must be assessed prior to curricular adoption. The purpose of this study was to assess the perceived ease of use and perceived usefulness of a virtual reality (VR) learning experience developed to promote the clinical decision-making of student physical therapists. Methods A VR learning experience was developed, and an established two-stage usability test assessed player experience as well as the user’s perception of both ease of use and usefulness. Two experts evaluated the VR learning experience and provided feedback. Six student physical therapists and five faculty members completed the VR experience, responded to two questionnaires, and participated in a semi-structured interview to further assess ease of use and utility. Results High levels of perceived ease of use, perceived usefulness, and positive player experiences were reported by both faculty and student users. Faculty users perceived a significantly greater amount of educational and clinical utility from the VR simulation than did student users. Semi-structured interviews revealed themes related to ease of use, benefits, modeling of professional behaviors, and realism. Conclusion Quantitative data supported faculty and student users’ perceptions of ease of use, utility towards learning, practical application, and several constructs related to user experience. Qualitative data provided recommendations to modify design features of the VR experience. This study provides a template to design, produce, and assess the usability of an immersive VR learning experience that may be replicated by other health professions educators where current evidence is limited.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".