Does Familiarization with Virtual Reality Improve Anatomy Learning in a Virtual Reality Environment?
Bibliographic record
Abstract
Despite a scarcity in substantive evidence, virtual reality (VR) is heralded as the future of anatomy education. Recent research in our lab suggests that VR headsets are substantially inferior to traditional plastic models as educational tools. This effect appears to be mediated by the VR headsets' inability to create convincing stereopsis. Our study aims to investigate if familiarization with the VR environment improves performance. When presented with a new learning environment (e.g., a room in VR), one may encounter a “novelty effect” where the new environment demands focus and distracts from learning. An introduction to the VR environment prior to learning could minimize this effect. Therefore, we hypothesize that familiarization will improve test scores. Undergraduate university students with no prior formal anatomy education (n=50) will be randomized to a familiarization or non‐familiarization group. The former group is allowed to orient themselves with a VR car engine model, ad libitum. Then, both groups will undergo a learning phase with a VR pelvis model for 10 minutes. Participants will be tested using a 15‐item evaluation, consisting of an equal amount of nominal, spatial, and functional questions, immediately and 48 hours after learning. Preliminary data (n=8) suggest that there are currently no statistically significant differences in short‐ or long‐term evaluation scores (p=0.109, p=0.254, respectively) between the familiarization and non‐familizarization groups. If this trend persists through completion of the study, it would suggest that a familiarization phase does not improve test scores. Data collection and analysis are anticipated to be completed in January 2019. Gaining an understanding of which factors influence VR learning allows it to become a more effective, evidence‐based tool for anatomy education. Support or Funding Information Self‐funded. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".