Evaluation of a Virtual 3D Learning Resource in Neuroanatomy for Undergraduate Medical Students
Bibliographic record
Abstract
In contrast to traditional teaching strategies, e‐learning presents the opportunity for development of learner‐centered educational tools, tailored to meet each student's distinct needs; however this has yet to be examined fully in the literature. Our study's cross‐over design divided participants into two groups, each beginning with anatomy knowledge and visuo‐spatial ability tests, followed by access to either the 3D online learning module or the gross anatomy laboratory. Participants were administered a second anatomy knowledge test, prior to switching to the other learning modality. There were no significant differences between groups in their baseline anatomy knowledge or visuo‐spatial abilities. Students who first accessed 3D online resources scored significantly better than students who accessed gross anatomy resources on the first anatomy knowledge test. After learning with both modalities, there were no significant differences between groups. No correlations were found between spatial ability and assessment score. Students responded positively to the 3D module, and their learning outcomes were equivalent or improved compared to when taught in the gross anatomy lab. Larger studies are required to confirm these preliminary results. Results may be used to help establish guiding principles to facilitate the design and implementation of effective and efficient e‐learning curricula.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".