Radical neuroconstructivism: a framework to combine the how and what of teaching and learning?
Bibliographic record
Abstract
Recent advances in pedagogical research have called attention to the dynamic nature of the teaching and learning process in which the actors mutually influence one another. The understanding of how this works in the brain—the specialized neural networks related to this process—is often limited to neuroscientists but are slowly becoming available to other learning scientists, including teachers. A transdisciplinary approach combining the best information about observable teaching-learning processes from education with newer information from the neurosciences may aid in resolving fundamental questions in the learning process. Teachers’ professional formation and development is often structured in segmented topical ways (e.g., pedagogy, evaluation, planning, classroom management, social–emotional learning), to identify important content knowledge (e.g., art, reading, mathematics, STEM), or to appreciate life skills (e.g., collaboration, critical thinking, social–emotional learning). While important, knowledge about the brain, the organ responsible for learning, is typically absent from teacher education. This paper reexamines the evidence from neuroconstructivism and the hierarchy of learning trajectories and combines it with evidence from psychology and the ways humans interact during the teaching-learning process to suggest radical neuroconstructivism as a framework within which to organize teachers’ professional development. The radical neuroconstructivism framework may contribute to making the content knowledge of teachers’ continual professional development more visible.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.054 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".