Learner Satisfaction with Peer‐to‐Peer Teaching in Virtual Dissection Laboratories
Bibliographic record
Abstract
Introduction Virtual dissection is a novel method for teaching anatomy through radiological images. While the role of peer learning has been studied in cadaveric dissection, not much is known about the role of peer teaching in virtual dissection. Aim To determine first year medical students' satisfaction with peer‐to‐peer teaching using this emerging technology. Methods Second year medical students prepared and taught virtual dissection laboratories for their first‐year colleagues, which were voluntary and extra‐curricular. The case‐based laboratories were designed to expose participants to clinical radiology images. Participants completed a post‐laboratory survey based on the Kirkpatrick Hierarchy for curriculum evaluation and results were tabulated. Results 34 first year students (24 females, 10 males) participated in this laboratory. All respondents found the cases were presented an appropriate level of difficulty, with 94% of participants believing the peer tutor facilitated the session effectively. Upon completing the session, the majority of participants felt that they understood the imaging findings (88%) and the clinical cases (91%), perceived the session as a valuable learning experience (97%), and would recommend the peer‐to‐peer virtual dissection laboratory to a colleague (94%). Almost all participants (97%) agreed or strongly agreed that the session was a valuable learning experience. Conclusion Students reported that peer‐to‐peer teaching was a valuable learning experience in virtual dissection laboratories. Peer‐to‐peer based virtual dissection laboratories provide an innovative and collaborative mode of learning that can complement more traditional anatomy education methods. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".