Evaluating the effect of instruction and practice schedule on the acquisition of ECG interpretation skills
Bibliographic record
Abstract
INTRODUCTION: Evidence of the benefit of distributed instruction and interleaved practice comes from studies using simple materials (e. g. word pairs). Furthermore, there is currently no evidence of the combined impact of these strategies in undergraduate medical education. The present study evaluated the impact of varying both instruction and practice schedules for the acquisition of ECG interpretation skills. METHODS: We conducted a 2 × 2 factorial study with two levels of instruction (massed and distributed) and two levels of practice (interleaved and blocked). A three-module introductory course in ECG interpretation was delivered to 80 first year medical undergraduate students. Students were assigned to one of four Instruction-Practice conditions: Massed-Interleaved, Massed-Blocked, Distributed-Interleaved and Distributed-Blocked. Learning was evaluated by a multiple choice quiz at the end of each module and a final multiple choice quiz at the end of the course. RESULTS: End of module mean scores showed that distributed instruction was consistently superior to massed instruction (52% vs 42%, p < 0.01). However, there was no effect of practice and no interaction between teaching and practice methods. The delayed final test scores revealed an advantage for blocked over mixed practice (34% vs 24%, p < 0.05) and distributed over massed instruction (34% vs 24%, p < 0.05). DISCUSSION: These results suggest that these popular strategies may have varying effects with complex learning materials. Further research is required to understand how these strategies affect the learning of simple and very complex skills.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".