Mapping the Neuroeducation Landscape: A Bibliometric Analysis (2020–2025)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.061 | 0.421 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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 it