A Bibliometric Analysis of Romanian Educational Research in Web of Science: Trends, Challenges, and Opportunities for Global Integration
Bibliographic record
Abstract
Over the past 30 years, Romanian Educational Research (RER) has strived to enhance its global visibility. This study examines the evolution and structure of RER through a bibliometric analysis of 10,396 publications indexed in the Web of Science Core Collection from 1975 to 2024. The results reveal fluctuating productivity, with growth driven by national and international initiatives, and challenges during the COVID-19 pandemic. Conference papers dominate research output, while articles and reviews are less frequent. Although domestic sources offer valuable publication opportunities, increasing visibility in high-impact journals is essential for international recognition. Universities across various regions contribute significantly, with research intensive institutions producing the bulk of articles and reviews. Five primary research clusters emerged, including studies on student performance factors, digital transformation in education, curriculum development and students’ skills, competency-based education and teacher training, as well as advancements in teaching, learning, and assessment practices. The study highlights the importance of international collaborations, particularly with institutions in Europe, the United States, Canada, and Australia, to boost the worldwide impact of Romanian research. These findings provide insights into the current state of RER and suggest that fostering international partnerships and leveraging local strengths can enhance its global presence and contribute to academic diversity.
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How this classification was reachedexpand
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 | 0.050 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.817 | 0.922 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".