Unravelling cognitive shifts: Neuroscience-based strategies in mathematics education
Bibliographic record
Abstract
As the global population of older adults rises, refining educational strategies to meet their specific cognitive needs becomes essential. This review explores how neuroscience can enhance mathematics education for older adults, focusing on cognitive changes such as declines in memory and processing speed. We analyzed literature from 2013 to 2023, drawing from various electronic databases including MEDLINE (via PubMed), PsycINFO, Scielo, and Google Scholar, and identified nine relevant studies. These studies emphasize the importance of targeted teaching methods and adaptive technologies. They reveal that while older adults maintain strong foundational numerical skills, effective learning hinges on practical applications and user-friendly technology. Key gaps include the need for longitudinal studies and challenges in implementing interventions across diverse socio-economic contexts. Integrating neuroscience with educational practices is crucial, with adaptive teaching and accessible technology being central. Future research should address the long-term impact of these interventions, their adaptability across different socio-economic backgrounds, and the interplay of cognitive changes, cultural factors, and individual learning styles to develop effective and scalable educational strategies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".