The persistence of matching teaching and learning styles: A review of the ubiquity of this neuromyth, predictors of its endorsement, and recommendations to end it
Bibliographic record
Abstract
Educational neuroscience tries to bridge neuroscience and education. It tries to combat neuromyths: beliefs that appear grounded in neuroscientific research but that are not supported by empirical evidence. One such neuromyth claims that matching teaching style to students’ preferred learning styles (e.g., visual teaching to visual learning) will lead to improved academic outcomes. The only formal way to test this meshing hypothesis is by finding a statistical crossover interaction effect which shows that matching teaching and learning styles improves academic outcomes, while non-matching teaching and learning styles negatively affects academic outcomes. Several studies are reviewed and none of these yielded empirical support for the meshing hypothesis. Reviewed studies suggest that educators widely believe the veracity of the meshing hypothesis. Predictive factors are discussed: even having some formal knowledge of neuroscience does not protect educators from endorsing neuromyths like the meshing hypothesis. An elaboration on teaching focused neuroscience to future educators is provided as a potential solution.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".