Preparation With Web-Based Observational Practice Improves Efficiency of Simulation-Based Mastery Learning
Bibliographic record
Abstract
INTRODUCTION: Our current understanding of what results in effective simulation-based training is restricted to the physical practice and debriefing stages, with little attention paid to the earliest stage: how learners are prepared for these experiences. This study explored the utility of Web-based observational practice (OP) -featuring combinations of reading materials (RMs), OP, and collaboration- to prepare novice medical students for a simulation-based mastery learning (SBML) workshop in central venous catheterization. METHODS: Thirty medical students were randomized into the following 3 groups differing in their preparatory materials for a SBML workshop in central venous catheterization: a control group with RMs only, a group with Web-based groups including individual OP, and collaborative OP (COP) groups in addition to RM. Preparation occurred 1 week before the SBML workshop, followed by a retention test 1-week afterward. The impact on the learning efficiency was measured by time to completion (TTC) of the SBML workshop. Web site preparation behavior data were also collected. RESULTS: Web-based groups demonstrated significantly lower TTC when compared with the RM group, (P = 0.038, d = 0.74). Although no differences were found between any group performances at retention, the COP group spent significantly more time and produced more elaborate answers, than the OP group on an OP activity during preparation. DISCUSSION: When preparing for SBML, Web-based OP is superior to reading materials alone; however, COP may be an important motivational factor to increase learner engagement with instructional materials. Taken together, Web-based preparation and, specifically, OP may be an important consideration in optimizing simulation instructional design.
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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".