The Power of the Plural: Effect of Conceptual Analogies on Successful Transfer
Bibliographic record
Abstract
BACKGROUND: Transfer, using a previously learned concept to solve a new, apparently different problem, is difficult. Students who know a concept will typically only be able to access it to solve new problems 10% to 30% of the time. However, one solution is to have students work through parallel, apparently different problems. METHOD: Learning materials for three cardiology-related concepts--Laplace Law, Starling Law, and Right Heart Strain--were devised. One group read a physiological explanation; two other groups read a combination of physiological and mechanical explanations, either paired up or separate. The sample was students in an undergraduate health sciences program (n = 35) who did the study for course credit. Outcomes were measured by accuracy of explanation on a test of nine clinical cases, as rated by one clinician on a seven-point scale. RESULTS: Groups who read two explanations did significantly better on the test, with mean scores of 3.6/5 and 4.1/5 versus 1.8/5 for the single group. Effect sizes were 1.3 and 1.7, respectively, against the single-example group. CONCLUSIONS: Active learning with multiple examples can have large effects on a student's ability to apply concepts to solve new problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".