Learning in the Simulated Setting: A Comparison of Expert-, Peer-, and Computer-Assisted Learning
Bibliographic record
Abstract
PURPOSE: To compare the effectiveness of expert-assisted learning (EAL), peer-assisted learning (PAL), and computer-assisted learning (CAL) on participants' procedural skills acquisition in the simulated setting. METHOD: Sixty medical and nursing students practiced urinary catheterization in an expert-, peer- or computer-assisted, simulation-based, learning environment. Effectiveness of training was evaluated in the simulated setting using an immediate posttest and, one week later, on a retention and standardized patient-based transfer test. Measures included number of breaks in aseptic technique and blinded expert assessments. RESULTS: All groups performed similarly on the pre-, post-, and retention tests. At transfer, the EAL group performed significantly better than the PAL group as measured by global clinical performance, catheterization checklist scores, and number of breaks in aseptic technique (P < .05). Communication and catheterization global ratings were equivalent for all groups (P > .05). CONCLUSIONS: CAL is as effective as expert feedback for teaching procedural skills to novices in the simulated setting. When extrinsic feedback is provided, the expertise level of the teacher seems to be a critical factor influencing effectiveness of training, with EAL being more effective than PAL.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".