MATHEMATICS EDUCATION AND COGNITIVE NEUROSCIENCE: INTERFACES REVEALED BY RESEARCHERS FROM THE CANADIAN LABORATORY ENGRAMMETRON (EDUCATIONAL NEUROSCIENCE AND MIXED RESEARCH LABORATORY)
Bibliographic record
Abstract
This study aims to highlight the interfaces between Mathematics Education and Cognitive Neuroscience revealed by the Canadian research group of the ENGRAMMETRON laboratory (Educational Neuroscience and mixed research laboratory) of Simon Fraser University from some aspects and results achieved by the group. According to the Canadian group, Cognitive Neuroscience seeks to highlight the role of the neurophysiological mechanisms underlying cognitive functions and to identify the mind-brain mechanisms that enable us to develop new teaching and learning strategies. In this way, I seek to highlight the main ideas pointed out by the group in the context of Mathematics Education and Cognitive Neuroscience. I emphasize that the focus will be given, primarily, to the perspective of the mathematical educator and, in a second focus, to that of the neuroscientist, valuing the interdisciplinary and multidisciplinary context of teaching and learning.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".