Drawing and Peer Teaching Through Illustrations in a Cadaveric‐based Undergraduate Human Anatomy Program
Bibliographic record
Abstract
The University of Guelph offers dissection‐based human anatomy courses to a large cohort of over 1000 undergraduate students per academic year. The third‐year class is the largest, comprised of students enrolled in the Human Kinetics and Bio‐Medical Sciences degree programs. Students attend weekly lectures that introduce the anatomy in a regional‐based approach. Lectures are taught using PowerPoint slides supplemented with interactive activities, including progressive schematic drawings demonstrating anatomical structures and relationships. Weekly cadaver‐based laboratories follow. Literature indicates extensive benefits to drawing in human anatomy education. Its integration has been positively associated with visual literacy and hand‐eye coordination. Drawing improves student awareness of anatomical details, by facilitating reasoning and deeper understanding of anatomical concepts. Additionally, drawing could promote peer teaching, for example, by encouraging students to create anatomical illustrations for their peers to use as study resources, therefore mutually benefiting both the illustrator‐instructor and learner. Registered course students with interest in drawing were invited to attend weekly sessions (n=48). The first half an hour involved a surface anatomy review lesson in tandem with the weekly laboratory goals. Participants were then allotted two hours to explore and draw from various cadaveric prosections related to the discussed region. Moreover, to promote peer teaching, students worked one‐on‐one with a graduate student to design an illustration‐based learning resource for their fellow peers of a region the student perceived as challenging. Preliminary results suggest a positive correlation between drawing and the understanding of anatomical structures, depth, relationships, and overall concepts. Additionally, the production and use of peer‐developed learning tools presents similar benefits. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".