Teachers learning to apply neuroscience to classroom instruction: case of professional development in British Columbia
Bibliographic record
Abstract
Little is known about the integration of current neuroscience knowledge to classroom teaching, although many teachers rely on neuromyths to shape their pedagogies. Through a professional development approach, the learning study, we explored how teachers learned to apply neuroscience to teaching instruction. The teachers collaborated to design, enact and evaluate neuroscience-framed lessons as part of classroom research. Theories relating to neural plasticity, including the neural network hypothesis for memory and learning, hierarchical relational binding theory, and attention and awareness acted as the theoretical frame for the study. Borrowing phenomenographic methods, we drew on a variety of data sources to construct categories describing the teachers’ engagement with neuroscience. Findings highlighted the pivotal role analogies played in the teachers’ interpretation of neuroscience content and its application. Through the analogies of the ‘rose’, ‘butcher on the bus’, ‘deepening the trenches’, and ‘walking the pathway’, we illustrated how teacher learning manifested as the teachers’ deepened understandings of knowledge construction, moving away from didactic forms of instruction and increasing the use of multiple modalities, and creating coherent student learning experiences. Findings suggest how neuroscience holds the potential to support teachers’ development of theoretical coherence in their understandings of learning and pedagogy. Implications are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".