Taking a Bite out of the Lab Book: Stereoscopic Laboratory Models in Student's Hands
Bibliographic record
Abstract
Decreasing time for anatomical teaching, coupled with a new generation of techno‐savvy students, has encouraged educators to explore new pedagogical approaches and instructional tools. An evolving tool is a stereoscopic 3D anatomical computer model of the head and neck. The objective of this study was to explore its effectiveness as an instructional and laboratory aid. Third year student volunteers (N=74) were randomly assigned to one of three lab groups. All groups studied the muscles of mastication and completed identical learning objectives during a 45‐minute lab. Each group utilized a different laboratory model: Group I‐gross prosections; Group II‐3D stereoscopic computer model; and Group III‐a hybrid model utilizing both resources. Model efficacy was measured with a pre‐post multiple‐choice quiz. We hypothesized that students using the 3D stereoscopic model or gross prosections would yield similar change scores (post‐test minus pre‐test), while students experiencing the hybrid model would have higher change scores. One week following the initial lab session, participants completed a second post‐test and a qualitative questionnaire to gain insights of knowledge retention and participant preferences respectively. Information gathered may help form new curricula direction. Grant Funding Source TSC‐Small Grant on Teaching
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".