Mapping the Neuroeducation Landscape: A Bibliometric Analysis (2020–2025)
Bibliographic record
Abstract
Background: Neuroeducation is an interdisciplinary area of study which combines insights of neuroscience, psychology, and education to enhance learning, using the body of scientific knowledge regarding the brain. Even though scholars have already investigated different details related to neuroeducation, thorough bibliometric research in the area remains absent. Summary: This review will provide a conceptual framework that will be used to analyse neuroeducation studies published in 2020-2025 on a medical database that would be accessed through Dimensions AI. The analyses involving VOSviewer of co-authorship, co-citation, and keywords in relation to 1,507 peer-reviewed articles were assessed. Key contributors, institutions, and theme clusters are suggested in the study. The United States, Canada and Spain became the leading contributors whereas such researchers as Antonopoulou Hera and Steve Masson made a significant contribution to the field. Key Message: The current bibliometric analysis gives us a vivid picture of the development of neuroeducation, its trends, and collaboration which can be used by educators, researchers, and policymakers when establishing the global network of research and filling the conceptual divide between neuroscience and practice in education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.130 | 0.157 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".