Developing theoretical coherence in teaching and learning: case of neuroscience-framed learning study
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe two cycles of learning study (LS) involving eight elementary teachers in British Columbia, Canada. The study explored the teachers’ experiences of learning to plan and teach lessons as informed by recent brain research. Design/methodology/approach The case study was constructed using data sources including teacher semi-structured interviews (pre-study, post-study and delayed post-study), classroom materials (including student assignments), LS training materials, fieldnotes and recordings of meetings and research lessons; sources were triangulated. Thematic analysis was applied. Contemporary neuroscience perspectives framed the LS discourse and analysis. Findings The teachers developed theoretical coherence and could better articulate reasons for their pedagogy. They developed understandings of the cognitive architecture underlying functions like learning and memory, allowing them to identify pedagogical actions that are consistent with human biology and understand why these actions are effective in promoting learning. Practical implications LS is shown to be an effective professional development (PD) model where theoretical knowledge, like neuroscience, could be employed and tested in classroom settings to provide depth to support teachers’ praxis. This teaching–research nexus supports exploration of fruitful connections between theoretical knowledge and education to advance the science of learning and the science of instruction. Originality/value Findings demonstrated how LS could be employed with alternative theoretical perspectives to promote teacher PD, thus extending beyond the dominant use of variation theory. Also, illustrated is the potential use of LS to bridge the knowledge gap between neuroscience and education.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.035 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| 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".