Neuroeducación en la era digital desde el prisma de la literatura científica en Web of Sciences
Bibliographic record
Abstract
Introduction: Neuroeducation, understood as an interdisciplinary field aimed at optimizing teaching–learning processes, has gained relevance in the so-called digital era. In this context, accelerated technological transformations demand systematized evidence to support the implementation of innovative educational methodologies. Objective: To characterize the scientific production indexed in Web of Science on neuroeducation in the digital era during the period 2000–2024. Method: A descriptive and cross-sectional bibliometric study was conducted using thematic searches with Boolean operators [TS=("neuroeducat*" OR "brain-based learn*") AND TS=("digital" OR "online" OR "virtual")]. The results were processed with Bibliometrix and TALL in R, analyzing indicators of annual production, citation, co-authorship, internationalization, most productive countries, co-occurrence of core terms, and thematic polarization. Results: A total of 283 publications were identified, showing an annual growth rate of 17.08%, consolidating a positive linear trend. The average co-authorship was 3.01 authors per document, with an internationalization rate of 17.31%. Spain leads the production (63.96%), followed by the United States (20.85%) and Canada (17.31%). Semantic analysis revealed six thematic clusters: digital technologies, cognition and neuroscience, educational policies, learning environments, outcome assessment, and research methodologies. Thematic polarization showed a positive predominance, with more than 74% of studies considering neuroeducation as an innovative and promising approach. Conclusions: Neuroeducation in the digital era emerges as a growing field in terms of scientific production, which still requires further research to provide solid evidence supporting its suggested benefits.
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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.048 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".