The Role of Prior Knowledge in Learning From Analogies in Science Texts
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
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it