Development and Assessment of Learners’ Experiences with a Virtual Reality Learning Platform: Constructivist and Experiential Learning Pedagogies in Master of Physical Therapy Curriculum
Bibliographic record
Abstract
Teaching competencies for psychomotor skill development for manual handling techniques in the cervical regions is necessary for safe practice in physiotherapy. However, grasping anatomy and palpation can be challenging for students, and practice in the lab can lead to discomfort for students. To facilitate teaching and learning of this complex skill, we worked in partnership with a virtual reality (VR) industry partner who developed a customized VR application focusing on transverse ligament stress testing for manual therapy skills for Master of Physical Therapy (MPT) students. In this scholarship of teaching and learning (SoTL) project, eight MPT students participated in the evaluation of an innovative VR learning experience for manual therapy in the cervical spine. Students’ learning experiences with the custom virtual reality learning application were assessed using an observational study design with semi-structured interviews. Interview questions aligned with constructs that are recommended to assess learners’ attitudes toward VR environments. Student participants appreciated the usefulness of the application for studying and practicing the transverse ligament stress test and provided recommendations for enhancing the learning experience.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".