The principles and practices of educational neuroscience: Comment on Bowers (2016).
Bibliographic record
Abstract
In his recent critique of Educational Neuroscience, Bowers argues that neuroscience has no role to play in informing education, which he equates with classroom teaching. Neuroscience, he suggests, adds nothing to what we can learn from psychology. In this commentary, we argue that Bowers' assertions misrepresent the nature and aims of the work in this new field. We suggest that, by contrast, psychological and neural levels of explanation complement rather than compete with each other. Bowers' analysis also fails to include a role for educational expertise-a guiding principle of our new field. On this basis, we conclude that his critique is potentially misleading. We set out the well-documented goals of research in Educational Neuroscience, and show how, in collaboration with educators, significant progress has already been achieved, with the prospect of even greater progress in the future. (PsycINFO Database Record
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.061 | 0.059 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".