ANATOMY EDUCATION AND VIRTUAL REALITY
Bibliographic record
Abstract
Institutions are looking to find the best learning technologies to deliver anatomy curricula to diverse student populations, often working with financial and time-based constraints. Visualization techniques, particularly the widespread use of virtual reality headsets, have made once-impossible learning experiences possible. This thesis explores the effect of different learning modalities (virtual reality headset, computer screen, and 3D-printed models) and environments (clinical context or context-free) on knowledge acquisition and learning experiences for a pelvic floor anatomy module. We investigated how these factors, along with mental rotation ability and stereoacuity, impact various aspects of learning, including performance on anatomy tests, perceived workload (measured using the NASA Task Load Index), cybersickness (measured using the Simulator Sickness Questionnaire), and engagement during learning (measured using the User Engagement Scale). Significant interactions were found between modality and environment for test scores and workload, a significant main effect of modality and environment for cybersickness, and a significant main effect of modality for engagement. Importantly, though significant differences were found between modalities and environments, participants reported concerningly high levels of workload and cybersickness across all conditions. High levels of engagement were also reported across all learning conditions. The lack of meaningful differences between intervention groups emphasizes the importance of curricular design over the implementation of new technologies and the need to be critical of the impression that a one-size-fits-all solution exists. Theories of cognitive load, constructivism, syncretion, visuospatial ability, cybersickness, and embodied learning in the context of technology-enhanced anatomy education are discussed as the foundation upon which design decisions should be made. A multi-faceted approach focused on aligning learning objectives with learning activities is outlined as a means of driving more impactful research and improving anatomy education.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".