INDICADORES BIBLIOMÉTRICOS SOBRE EDUCAÇÃO EM ENGENHARIA EM DIFERENTES BASES DE DADOS
Bibliographic record
Abstract
Looking for attend the market needs for competent professionals, not only in the technical area, but also with leadership skills, entrepreneurship and innovation, efforts are emerging around the world to modernize engineering courses, giving impulse to the field of research on Engineering Education. One of the ways to assess the impact of scientific research and analyze its results is through the use of science and technology indicators. This work aimed to develop bibliometric indicators on engineering education based on the research available on the Web of Science and Scopus in the last 20 years (2000 to 2019). Were analyzed: number of publications per year; the most relevant countries, institutions and authors and research trends in the area. After analyzing the data, it was possible to verify the USA hegemony in engineering education research's through the number of publications, main institutions and authors. Brazil, however, did not stand out in the area, being responsible for only a small portion of the analyzed publications, although public policies recently implemented have been trying to generate improvements in the current scenario. In addition, research trends were identified in active learning methodologies and the use of computational teaching tools, requiring further in-depth studies to establish the state of the art of these themes, collaboration networks between countries, institutions and authors and a projection of the area growth.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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; a candidate call from one teacher head, 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".