Design and implementation of an online systemic human anatomy course with laboratory
Bibliographic record
Abstract
Systemic Human Anatomy is a full credit, upper year undergraduate course with a (prosection) laboratory component at Western University Canada. To meet enrollment demands beyond the physical space of the laboratory facility, a fully online section was developed to run concurrently with the traditional face to face (F2F) course. Lectures given to F2F students are simultaneously broadcasted to online students using collaborative software (Blackboard Collaborate). The same collaborative software is used by a teaching assistant to deliver laboratory demonstrations in which three-dimensional (3D) virtual anatomical models are manipulated. Ten commercial software programs were reviewed to determine their suitability for demonstrating the virtual models, resulting in the selection of Netter's 3D Interactive Anatomy. Supplementary online materials for the central nervous system were developed by creating 360° images of plastinated prosected brain specimens and a website through which they could be accessed. This is the first description of a fully online undergraduate anatomy course with a live, interactive laboratory component. Preliminary data comparing the online and F2F student grades suggest that previous student academic performance, and not course delivery format, predicts performance in anatomy. Future qualitative studies will reveal student perceptions about their learning experiences in both of the course delivery formats.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".