Worked examples for teaching electrocardiogram interpretation: Salient or discriminatory features?
Bibliographic record
Abstract
CONTEXT: Cognitive load theory states that one way to optimise learning is to decrease extraneous cognitive load, defined as information not relevant to task completion. Worked examples, which show the learner the logic behind the solving of a problem, can decrease extraneous load. However, there is little research to guide the optimal formatting of worked examples. METHODS: In a crossover design, first-year medical students were randomised to worked examples of bradycardias with salient features first and tachycardias with discriminatory features second (n = 33) or worked examples of bradycardias with discriminatory features first and tachycardias with salient features second (n = 32). After each learning phase, participants completed a testing phase. Diagnostic accuracy and reported cognitive load were compared between the two worked example formats, as well as with data for a group of historical controls, consisting of medical students interpreting electrocardiogram rhythms without worked examples. Each module concluded with a questionnaire in which the learner was asked to rate his or her perceptions of the difficulty of the core content, the clarity with which the information was presented, and perceived learning. RESULTS: Worked examples highlighting salient and discriminatory features were associated with similar levels of diagnostic accuracy (56% and 60%, respectively; P = .32). Both worked example conditions were associated with higher diagnostic accuracy than was found in historical controls (P < .0001). There was no difference in the extraneous load experienced between worked examples highlighting salient features and those highlighting discriminatory features (12.5 ± 6.1 and 11.9 ± 6.1, respectively; P = .52). Participants reported greater intrinsic load in the worked examples highlighting salient rather than discriminatory features (17.1 ± 4.9 and 15.5 ± 4.6, respectively; P = .01). CONCLUSIONS: Discriminatory feature-based worked examples were associated with less intrinsic cognitive load, but this did not translate into any meaningful difference in diagnostic performance. Instruction with worked examples improved diagnostic performance regardless of whether salient or discriminatory features were highlighted.
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 | 0.003 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".