Evaluation of an online three‐dimensional interactive resource for undergraduate neuroanatomy education
Bibliographic record
Abstract
Neuroanatomy is one of the most challenging subjects in anatomy, and novice students often experience difficulty grasping the complex three-dimensional (3D) spatial relationships. This study evaluated a 3D neuroanatomy e-learning module, as well as the relationship between spatial abilities and students' knowledge in neuroanatomy. The study's cross-over design divided the participants into two groups, each starting with tests for anatomy knowledge and spatial ability, followed by access to either the 3D online learning module or the gross anatomy laboratory. Participants completed a second knowledge test prior to accessing the other learning modality. Participants in both groups scored significantly higher on Quiz 1 than on the Pretest knowledge assessment (W = 47, P < 0.01; W = 30, P < 0.01). Students who initially accessed the 3D online resources scored significantly better on the Quiz 1 than students who accessed the gross anatomy resources (W = 397.5, P < 0.01). Scores significantly improved on Quiz 2 for participants who accessed the 3D learning module following exposure to the cadaveric resources (W = 94, P < 0.01). After exposure to both learning modalities, there were no significant differences between groups. Significant positive correlations were found between participants' spatial ability score and their performance on the Pretest, Quiz 1, and Quiz 2 assessments (r = 0.22, P = 0.04; r = 0.25, P = 0.02; r = 0.26, P = 0.02). These preliminary results found students appreciated working with the 3D e-learning module, and their learning outcomes significantly improved after accessing the resource. Anat Sci Educ 9: 431-439. © 2016 American Association of Anatomists.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".