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 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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".