Evaluating the effectiveness of learning ear anatomy using holographic models
Bibliographic record
Abstract
BACKGROUND: Computer-assisted learning has been shown to be an effective means of teaching anatomy, with 3-D visualization technology more successfully improving participants' factual and spatial knowledge in comparison to traditional methods. To date, however, the effectiveness of teaching ear anatomy using 3-D holographic technology has not been studied. The present study aimed to evaluate the feasibility and effectiveness of learning ear anatomy using a holographic (HG) anatomic model in comparison to didactic lecture (DL) and a computer module (CM). METHODS: A 3-D anatomic model of the middle and inner ear was created and displayed using presentation slides in a lecture, computer module, or via the Microsoft HoloLens. Twenty-nine medical students were randomized to one of the three interventions. All participants underwent assessment of baseline knowledge of ear anatomy. Immediately following each intervention, testing was repeated along with completion of a satisfaction survey. RESULTS: Baseline test scores did not differ across intervention groups. All groups showed an improvement in anatomic knowledge post-intervention (p < 0.001); the improvement was equal across all interventions (p = 0.06). Participants rated the interventions equally for delivery of factual content (p = 0.96), but rated the HG higher than the DL and CM for overall effectiveness, ability to convey spatial relationships, and for learner engagement and motivation (p < 0.001). CONCLUSIONS: These results suggest that 3-D holographic technology is an effective method of teaching ear anatomy as compared to DLs and CMs. Furthermore, it is better at engaging and motivating learners compared to traditional methods, meriting its inclusion as a tool in undergraduate medical education curriculum.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".