Directed self‐regulated learning versus instructor‐regulated learning in simulation training
Bibliographic record
Abstract
OBJECTIVES: Simulation training offers opportunities for unsupervised, self-regulated learning, yet little evidence is available to indicate the efficacy of this approach in the learning of procedural skills. We evaluated the effectiveness of directed self-regulated learning (DSRL) and instructor-regulated learning (IRL), respectively, for teaching lumbar puncture (LP) using simulation. METHODS: We randomly assigned internal medicine residents in postgraduate year 1 to either DSRL ('directed' to progress from easy to difficult LP simulators during self-regulated learning) or IRL (in groups of four led by an instructor). All participants practised for up to 50 minutes and completed a pre-test, post-test and delayed (by 3 months) retention test on the simulator. Pairs of blinded trained experts independently rated all videotaped performances using a validated global rating scale and a modified version of a validated checklist. Participants provided measures of LP experience and self-reported confidence. We analysed the pre-post (n = 42) and pre-post-retention performance scores (n = 23) using two separate repeated-measures analyses of variance (anovas) and computed Pearson correlation coefficients between participants' confidence and performance scores. RESULTS: Inter-rater agreement was strong for both performance measures (intra-class correlation coefficient > 0.81). The groups achieved similar pre-test and post-test scores (p > 0.05) and scores in both groups improved significantly from the pre- to the post-test (p < 0.05). On retention, a significant interaction (F(2,42) = 3.92, p = 0.03) suggests the DSRL group maintained its post-test performance, whereas that in the IRL group dropped significantly (p < 0.05). Correlations between self-reported confidence and post-test performance were positive and significant for the DSRL group, and negative and non-significant for the IRL group. CONCLUSIONS: Both IRL and DSRL led to improved LP performance immediately after practice. Whereas the IRL group's skills declined after 3 months, the DSRL group's performance was maintained, suggesting a potential long-term benefit of this training. Participants in the DSRL group also developed a more accurate relationship between confidence and competence following practice. Further research is needed to clarify the mechanisms of self-regulated learning and its role in simulation contexts.
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 | 0.005 | 0.014 |
| 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 | 0.001 | 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".