Proposition for improving the classical models of conceptual change based on neuroeducational evidence: conceptual prevalence
Bibliographic record
Abstract
In this article we propose some adjustments to the models of conceptual change that belong to the "classical" tradition for the purpose of improving the efficiency of science teaching that aims at producing such "conceptual changes".These adjustments are suggested on the basis of recent research results in neuroeducation and psychopedagogy.We first present a synthetic description of the classical tradition of conceptual change, its founding principles, and the literature that supports it, as well as pointing out some of its shortcomings.Next, we present the relevant results that call the model into question, and we propose some adjustments in the form of a three-step procedure that we believe can better produce appropriate "conceptual prevalence."Finally, we present plausible implications of the discussed neuroeducative findings for learning in general.Potvin Proposition for improving the classical models of conceptual change
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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 | 0.045 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.040 |
| Scholarly communication | 0.006 | 0.026 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".