Bibliographic record
Abstract
Advances in developmental systems neuroscience (DSN) highlight plasticity, functional specialization, and executive-control dynamics, yet their translation into teacher education remains limited, particularly in inequitable contexts. This study evaluates the Neuro-Embedded Lesson Problem-Solving (NLPS) framework as a translational approach for developing equity-oriented problem-solving among novice early childhood teachers. A transformative mixed-methods field trial with 29 first-year preservice teachers in Southern Thailand implemented six NLPS workshops across eight weeks, addressing linguistic disadvantage, socio-emotional regulation, and resource constraints. Outcomes included a Rasch-calibrated Problem-Solving Competency (PSC) rubric, the Teacher Self-Efficacy equity subscale (TSE-equity), a composite Equity-Oriented Problem-Solving Index (EOPSI), and qualitative analyses of journals and concept maps. Bayesian paired-sample models showed credible improvements in PSC (Δ = 0.75), TSE-equity (Δ = 0.60), and EOPSI (Δ = 0.85). Qualitative findings revealed a shift toward DSN-informed, equity-centred reasoning and increased conceptual integration with “equity” as a central hub. Collectively, the results demonstrate that embedding DSN within structured problem-solving cycles can strengthen scientifically grounded and justice-responsive pedagogical orientations, particularly in Global South teacher-education contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".