Systematics review of the interdisciplinary exchange among mathematics education and neuroscience
Bibliographic record
Abstract
Abstract This paper presents systematic survey of empirical studies that implement neurocognitive tools to study mathematical processing, learning and problem solving. The survey comprised three stages: identification, screening, and analysis. The search was restricted to English-language papers published in research journals. Of a total of 35,692 records that were identified initially, 598 papers were found eligible for precise data analysis through screening procedure. The bibliometric analysis focused on publication years, journals and authors as well as on collaboration between the researchers. In the content analysis, along with the analysis of neurocognitive tools used in the studies, we screened the papers for the groups of research participants; mathematical topics, concepts and skills examined in the studies. We found that there has been tremendous growth in the past decade in the use of neurocognitive tools to research mathematics learning. The most commonly used tools are the fMRI, EEG, and eye tracking, while use of tools such as GSR and fNIRS remains highly uncommon. There is a strong focus on studying arithmetic, and a recent trend toward examining problem-solving skills, but higher mathematics learning and equation solving remain under-researched. Finally, we found that despite the immense growth in neuroscience research relevant to mathematics education, few studies of this type are published in mathematics education journals.
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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 | 0.030 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.054 | 0.040 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".