Is a Dissective Virtual Reality Model an Effective Learning Tool?
Bibliographic record
Abstract
Three‐dimensional (3D) visualization technology such as virtual reality (VR) has the ability to illustrate and replicate physical dissection, and its novelty has captured the interest of many educational institutions. Unfortunately, the testing of 3D technology lags behind development, and most research is confined to case studies. This study's objective is to (1) analyze the short‐term and long‐term efficacy of VR dissection technology compared to an interactive, physical dissective model, and (2) determine if other factors, such as spatial ability, impacts the effectiveness of learning anatomy from VR models. Based on previous research in our lab, static physical models have been shown to be superior to VR models when learning anatomy. Thus, the physical dissection model is hypothesized to perform better in teaching anatomy. The interactive, physical model consists of a 3D‐printed bony pelvis and fabric perineal structures to effectively display the dissections. The physical model was scanned to produce an identical VR replica which is displayed on an HTC Vive. This crossover study will use undergraduate McMaster University students (n=52) with no formal anatomy education. Participants will be asked to learn anatomical structures from both physical and VR models, and be tested on the knowledge from each model in two separate tests. After 48 hours, they will be tested to determine if either model exhibits better long‐term retention. Tests will include nominal, functional, and spatial questions to assess recognition, critical thinking, and spatial awareness. Preliminary data (n=13) suggests that there is no statistically significant difference between either models when learning anatomy during short‐term testing (p=0.24) and long‐term testing (p=0.054). On short‐term retention tests, participants are receiving an average score of 7(3) and 6(2) out of 15 when learning from the VR and physical models, respectively. During long‐term testing, participants are achieving an average score of 7(3) and 5(2) out of 15 when also learning from the VR and physical models, respectively. Data collection is underway and expected to yield complete results by January 2019. Data from additional participants will further elucidate the impact of VR and physical dissection models on student learning. These results could help guide and improve the development of future anatomy education programs. Support or Funding Information This project was 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".