Dispelling Educational Neuromyths: A Review of In‐Service Teacher Professional Development Interventions
Bibliographic record
Abstract
ABSTRACT Despite considerable progress made in educational neuroscience, neuromyths persist in the teaching profession, hampering translational endeavors. The initial wave of interventions designed to dispel educational neuromyths was predominantly directed at preservice teachers. More recent work in the field, reviewed here, has shifted its focus primarily to in‐service teacher professional development interventions. We discuss various interventional approaches, including refutation texts embedded into a brief training in foundational neuroscience, personalized refutation texts, insightful reflections upon science of learning key concepts (e.g., brain plasticity), and immersive experiences within research groups, highlighting their strengths and limitations. The evolving nature of scientific knowledge, the imperative to respect educators' personal and professional sensitivities, as well as challenges posed by conceptual change, are also addressed. This narrative review underscores the need to bring neuromyth investigations into the classroom environment.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".