The Role of Prior Knowledge in Learning From Analogies in Science Texts
Bibliographic record
Abstract
Two experiments examined whether inconsistent effects of analogies in promoting new content learning from text are related to prior knowledge of the analogy per se. In Experiment 1, college students who demonstrated little understanding of weather systems and different levels of prior knowledge (more vs. less) of an analogous everyday situation read a text about weather systems that included the analogy or a control version that did not. Results indicated that those with more prior knowledge of the analogy performed better on weather system learning measures (sentence verification and number of concepts in essays). Prior knowledge of the analogous domain interacted with presence of the analogy in the text on 1 learning measure: Those with more prior knowledge who read the analogy text had fewer misconceptions in their conceptual models of weather than those who read the control text. Think-aloud protocols collected in Experiment 2 suggested that analogies in the text constrained prior knowledge activation and processing of the weather system content. Whereas previous research has shown that prior knowledge of a to-be-learned target domain positively impacts learning, this research elaborates this effect by showing that prior knowledge of an analogically related domain positively impacts target domain learning.
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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 | 0.005 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".