Virtual Reality and Anatomy: Increasing Motivation and Learning Gains
Bibliographic record
Abstract
Anatomy and physiology courses are an integral part of the curriculum of the many Health, Wellness, and Sciences diploma (e.g., acupuncture, biotechnology, massage therapy, occupational therapy assistant and physical therapy assistant) and degree programs (Honours Bachelor of Science – Nursing Honours Bachelor Degree program) offered at Georgian College. In 2020, Georgian College received a Future Skills Centre (FSC) Shock-Proofing the Future of Work: Skills Innovation Challenge grant. As part of this grant, Georgian College is exploring, through two pilot projects, the benefits and challenges associated with integrating virtual reality(VR) technology in anatomy courses in Health, Wellness, and Sciences programs to enhance learning by offering to students in addition to the conventional content, new ways (i.e., VR or non-immersive 2D programs) to engage, experience, and learn course content. More specifically, the goal of the two pilots is to examine the effects of using either VR anatomy or 2D anatomy on experience-based learning outcomes (motivational and enjoyment) and content-based learning outcomes (pre/post-test comparisons). The work-in-progress paper describes the development and implementation of the two pilot projects. For the last eighteen months, students enrolled in specific Health, Wellness, and Sciences diploma and degree programs have had the chance to engage with either human anatomy VR experiences or 2D human anatomy. So far, these pilots have generated important discussions among different interested parties regarding the viability of incorporating VR technology in the curriculum of Health, Wellness, and Sciences diploma and degree programs, as well as how VR anatomy-based experiences can be improved to meet the needs of different diploma and degree programs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".