On a New Taxonomy of Concepts and Conceptual Change: In Search of the Brain’s Probabilistic Language of Learning Scientific Concepts
Bibliographic record
Abstract
Abstract Over four decades of conceptual change studies in science education have been based on the assumption that learners come to science classrooms with functionally fixated intuitive ideas. However, it is largely ignored that such pre-instructional conceptions are probabilistic, reflecting some aspects of an idiosyncratic sampling of their experiences and intuitive decision-making. This study foregrounds the probabilistic aspect of international students' intuitive and counterintuitive conceptions when learning pendulum motion. The probability here is rooted in a moving neural time average in the mind for characterizing these students' cognition (sampling and decision-making) and learning processes (resampling and making a new decision). To sharpen the said focus, we would argue that a new taxonomy of physics concepts is needed to save the mathematical identification of the isochrony of pendulum motion. To connect the mathematical core-based taxonomy with reality, we conducted an experimental study to characterising these students' reaction time and error rates in matching the period of a visually presented pendulum, which embodied its mathematical identity: T = 2π√l/g. The reaction times and error rates data have converged on the probabilistic aspects of the students' active learning mechanisms in their mind. The pedagogical implications of such a probabilistic cognitive mechanism have also been discussed.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.034 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".