Exploring the Changing Learning Environment of the Gross Anatomy Lab
Bibliographic record
Abstract
PURPOSE: The objective of this study was to assess the impact of virtual models and prosected specimens in the context of the gross anatomy lab. METHOD: In 2009, student volunteers from an undergraduate anatomy class were randomly assigned to study groups in one of three learning conditions. All groups studied the muscles of mastication and completed identical learning objectives during a 45-minute lab. All groups were provided with two reference atlases. Groups were distinguished by the type of primary tools they were provided: gross prosections, three-dimensional stereoscopic computer model, or both resources. The facilitator kept observational field notes. A prepost multiple-choice knowledge test was administered to evaluate students' learning. RESULTS: No significant effect of the laboratory models was demonstrated between groups on the prepost assessment of knowledge. Recurring observations included students' tendency to revert to individual memorization prior to the posttest, rotation of models to match views in the provided atlas, and dissemination of groups into smaller working units. CONCLUSIONS: The use of virtual lab resources seemed to influence the social context and learning environment of the anatomy lab. As computer-based learning methods are implemented and studied, they must be evaluated beyond their impact on knowledge gain to consider the effect technology has on students' social development.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".