Teaching basic science to optimize transfer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Basic science teachers share the concern that much of what they teach is soon forgotten. Although some evidence suggests that relatively little basic science is forgotten, it may not appear so, as students commonly have difficulty using these concepts to solve or explain clinical problems: This phenomenon, using a concept learned in one context to solve a problem in a different context, is known to cognitive psychologists as transfer. The psychology literature shows that transfer is difficult; typically, even though students may know a concept, fewer than 30% will be able to use it to solve new problems. However a number of strategies to improve transfer can be adopted at the time of initial teaching of the concept, in the use of exemplars to illustrate the concept, and in practice with additional problems. AIM: In this article, we review the literature in psychology to identify practical strategies to improve transfer. METHODS: Critical review of psychology literature to identify factors that enhance or impede transfer. RESULTS: There are a number of strategies available to teachers to facilitate transfer. These include active problem-solving at the time of initial learning, imbedding the concept in a problem context, using everyday analogies, and critically, practice with multiple dissimilar problems. Further, mixed practice, where problems illustrating different concepts are mixed together, and distributed practice, spread out over time, can result in significant and large gains. CONCLUSION: Transfer is difficult, but specific teaching strategies can enhance this skill by factors of two or three.
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.
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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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 it