Do One Then See One: Sequencing Discovery Learning and Direct Instruction for Simulation-Based Technical Skills Training
Bibliographic record
Abstract
PURPOSE: When teaching technical skills, educators often include a mix of learner self-regulation and direct instruction. Appropriate sequencing of these activities-such as allowing learners a period of discovery learning prior to direct instruction-has been shown in other domains to improve transfer of learning. This study compared the efficacy of learners trying a novel simulated suturing task before formal instruction (Do then See) versus the more typical sequence of formal instruction, followed by practice (See then Do) on skill acquisition, retention, and transfer. METHOD: In 2015, first-year medical students (N = 36) were randomized into two groups to learn horizontal mattress suturing. The See then Do group had access to instructors before independent practice, whereas the Do then See group explored the task independently before accessing instructors. Participants were assessed at the transition between interventions, and as training ended. Skill retention, and transfer to a novel variation of the suturing task, were assessed after one week. Performance was scored on a five-point global rating scale by a blinded rater. RESULTS: The groups did not differ significantly on immediate posttest or retention test (F[1,30] = 0.96, P < 0.33). The Do then See group (N = 16) outperformed the See then Do group (N = 16) on the transfer test; 2.99 versus 2.52 (F[1,28] = 10.14, P < 0.004, η(2) = 0.27). CONCLUSIONS: Sequencing discovery learning before direct instruction appeared to improve transfer performance in simulation-based skills training. Implications for future research and curricular design are discussed.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".