Toward a durable prevalence of scientific conceptions: Tracking the effects of two interfering misconceptions about buoyancy from preschoolers to science teachers
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
Abstract While the majority of published research on conceptual change has focused on how misconceptions can be abandoned or modified, some recent research findings support the hypothesis that acquired scientific knowledge does not necessarily erase or alter initial non‐scientific knowledge but rather coexists with it. In keeping with this “coexistence claim,” this article presents an analysis of scientific understanding in four groups of individuals with varying degrees of expertise (preschoolers, elementary students, secondary students, and science teachers) using a cognitive task on buoyancy. This task allowed us to determine the prevalence of certain conceptions and the interference caused by two possible conceptual distractors with regard to producing accurate answers. Results describe the progression of the desired (scientific) conception with age/expertise as well as the evolution or regression of the statuses of two misconceptions. Results also show that misconceptions continue to interfere with performance even when there is a higher degree of scientific expertise, and that patterns of such interference can be studied. In keeping with these conclusions, we argue for the use of a model of conceptual learning called “conceptual prevalence.” © 2017 The Authors. Journal of Research in Science Teaching Published by Wiley Periodicals, Inc. J Res Sci Teach 54:1121–1142, 2017
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 | 0.061 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.007 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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