Animate! Breathing new life into an old pedagogy: Human embryology teaching reenvisioned
Bibliographic record
Abstract
The teaching of human embryology is as ancient as that of human anatomy itself. Over the centuries up to the present time, embryology pedagogy has evolved from enlisting the dissection of fetal cadavers and use of two‐dimensional (2D) sketches and diagrams, to the modern technologies of 2D and 3D animations, CT scans of embryos and fetuses with 3D rendering of the images, and the use of ultrasound imaging. These techniques seek to balance accurate portrayal of embryonic structures with the cognitive load associated with learning the concepts presented using these images, and their value as learning adjuncts for students. This paper discusses the rationale, pedagogical basis and implementation of narrated 2D animations of embryonic development, which present the early stages of human life in accessible cartoon‐like sequences. In response to the demand from curriculum leaders at the University of British Columbia (UBC) medical school for high‐quality, simplified yet accurate rendition of early embryonic development, we have developed the first of a series of 2D animations, which presents key embryonic processes during the first 12 weeks of life contemporaneously. First and second year MD students at UBC will have access to these animations as adjuncts to the embryology components of the MD Undergraduate pre‐clerkship program. The animation was developed using Adobe Animate® software during approximately 300 hours of programming. Audio design, recording and editing were completed with Adobe Audition®, After Effects® and Adobe Premiere Pro®. In evaluating the animation, students rated it highly for visual appeal and helpfulness to learn basic embryology, while providing useful comments on how it may be improved. Support or Funding Information Southern Medical Program, UBC Faculty of Medicine 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".