Using a Social Educational Network to Facilitate Peer-Feedback for a Virtual Simulation
Bibliographic record
Abstract
Virtual simulation offers a viable alternative to traditional educational and training practices, offering trainees the opportunity to train until they reach a specific competency level in a safe and cost-effective manner. One of the benefits of virtual simulation is the ability to provide the trainee with feedback regarding his or her performance in the simulation, thus providing the trainee the opportunity to monitor and adapt progress toward the goal. With respect to learning and development, it has been long known that feedback plays a vital role in learning; we learn faster and more effectively when we know how we are doing and what must be changed to improve our performance. Taking advantage of the benefits afforded by peer-feedback, here we present a preliminary study that examined the application of a customized social educational network—the Observational Practice and Educational Network (OPEN)—to facilitate peer-feedback with respect to recorded performances of a gamified virtual simulation session developed specifically for medical-based cultural competence training. A virtual simulation session was recorded and uploaded to OPEN, which then facilitated peer-feedback by allowing the peers and educators/experts to evaluate, provide comments, and generally discuss the recorded virtual simulation sessions. Questionnaires were employed to gauge the feasibility of a social educational network (OPEN in particular) to facilitate peer-feedback, as well as participant satisfaction with using and interacting with OPEN (i.e., examining the usability of OPEN).
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 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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".