A Bibliometric and Thematic Analysis of Educational Neuroscience Research in Early Childhood Education, 1970–2024
Bibliographic record
Abstract
This review employed bibliometric methods to test the meta-data of documents related to educational neuroscience in early childhood education (ECE) published over a period of 55 years, from its beginnings in 1970 to 2024. The study analysed a total of 498 documents. Using bibliometric techniques, it summarised descriptive trends, uncovered the foundational intellectual framework, identified popular themes, and suggested new avenues for future research. Thematic analysis highlighted the evolution of themes across three distinct developmental phases. The integration of bibliometric techniques with thematic analysis offered a comprehensive overview and deeper understanding of the historical, present, and future trajectories of educational neuroscience research in ECE. Research Findings: There has been a notable increase in educational neuroscience publications in ECE, with a significant surge since 2021. The United States, Canada, and China are the leading contributors. Influential research primarily examines the impact of brain injury or neuropsychological deficiencies and the efficacy of intervention programs. The intellectual structure consists of three main research clusters, while conceptual themes focus on neurodevelopment, interventions, and neuro damage. Additionally, eight prominent research fronts were identified. Practice or Policy: The findings have implications for future educational neuroscience research in ECE, methodology, policy, and practice.
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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 | 0.070 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.273 | 0.277 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".