Emergence of Analogies in Collaboratively Conducted Computer Simulations
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
To learn by means of analogies, students have to see surface and deep structures in both source and target domains. Educators generally assume that students, presented with images, texts, video, or demonstrations, see what the curriculum designer intends them to see, that is, pick out and integrate information into their existing understanding. However, there is evidence that students do not see what they are supposed to see, which precisely inhibits them to learn what they are supposed to learn. In this extended case study, which exemplifies a successful multimedia application, 3 classroom episodes are used (a) to show how students in an advanced physics course do not see relevant information on the computer monitor; (b) to exemplify teaching strategies designed to allow relevant structures to become salient in students’ perception, allowing them to generate analogies and thereby learn; and (c) to exemplify how a teacher might assist students in bridging from the multimedia context to the real world.
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 | 0.000 |
| 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 | 0.000 |
| 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