Students' Preferred Pedagogical Approaches to Peer‐to‐Peer Teaching with Virtual Dissection
Bibliographic record
Abstract
Introduction In virtual dissection, near life‐sized three‐dimensional computed tomography (CT) scans are used to study human anatomy. Preferred pedagogical approaches for peer‐to‐peer teaching using this technology have not yet been explored. Aim To identify preferred pedagogical approaches in peer‐to‐peer teaching and to better understand how this adds educational value to virtual dissection in medical education. Methods Second‐year medical students developed and taught a virtual dissection laboratory for their first‐year colleagues, using an anatomy visualization table. This case‐based laboratory was designed to complement the other subjects taught in first year medicine. Participants completed a post‐laboratory survey in which collected their preferred methods for learning this new technology. Results were tabulated and summarized. Results 34 first‐year medical students (24 females, 10 males) participated in this virtual dissection laboratory. Results indicated that small group demonstration sessions were thought to be a better teaching method than large group demonstration sessions (97% vs. 24%) for peer‐to‐peer teaching using virtual dissection. Participants agreed or strongly agreed that the sessions improved their understanding of general anatomy (97%) and disease (85%), the imaging appearance of normal (94%) and pathological (97%) structures, visuospatial relationships (91%), the clinical relevance of anatomy (94%), and the role of radiology in patient care (94%). Most respondents agreed or strongly agreed that watching a virtual dissection (91%) and performing the virtual dissection themselves (82%) facilitated their learning. Conclusion Students reported that the small group demonstration format of peer‐to‐peer teaching was the preferred educational approach for virtual dissection laboratories. The use of novel anatomy visualization technology by peer tutors was an effective method for improving students' understanding of radiologic anatomy and may be used to complement medical education curricula. 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".