Bibliographic record
Abstract
This study aims to determine the distribution of STEM articles in the WOS database in the field of education by years, keywords and active concepts, active countries, active countries by the total number of citations, active countries by citation averages, active authors, active authors by local citation rankings, active researches by global citation rankings, active journals by article numbers, active journals by h_index values, active institutions, country-author-keyword network, and popular topics by years. Bibliometric analysis, one of the quantitative approaches, was used in the study. The dataset consisted of 10896 STEM education articles published between 1972 and 1 July 2024, retrieved from the WOS database. The “R” programming language “Biblioshiny” package was employed for the purpose of data analysis. According to the research results, if we consider STEM articles in the field of education, the annual growth rate is 14. 03%, the keywords are science, education, student, knowledge, achievement, mathematics, performance, gender, technology, impact, experience, teacher, belief, model, motivation, school, and self-efficacy. The active countries are USA, China, UK, Australia, Turkey, Spain, Canada, Germany, Ireland and Israel, the USA is far ahead in the citation ranking of the countries, according to the citation averages, the Netherlands overtook the USA and took the first place, Linn M. C. is the most influential author, Hazari Z. is the first in the local citation ranking. C. is the most influential author, Hazari Z. is first in the local citation ranking, Blickenstaff J.C. is first in the global citation ranking, “International Journal of STEM Education” is the most influential journal, Michigan State University is the most influential institution. In recent years topics such as 21st century skills and climate change have become popular. It is recommended that new bibliometric studies, including those pertaining to STEM research in the field of educational sciences, be conducted and compared with existing studies in different fields.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.338 | 0.702 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".