GLOBAL VISIONS AND FUTURE PERSPECTIVES IN TEACHING SUSTAINABILITY IN ENGINEERING
Bibliographic record
Abstract
ABSTRACT: The purpose of this study is to map the current status of research in engineering education from the perspective of sustainability, bibliometrix and biblioshiny packages were employed to perform data mining and quantitative analysis of publications in this area of knowledge from 2012 to 2022 in the Web of Science database. The results showed that in the last 10 years, the number of articles on teaching sustainability in engineering has increased. The increase was gradual and can be divided into three stages, between 2012 and 2016 the publications ranged from 130 to 150, from 2017 to 2019 the publications went from 150 to 190 and from 2020 to 2021 it exceeded 200 articles. The top countries in terms of research development in the area of sustainability education in engineering are the United States, Spain, China, Australia, the United Kingdom, Germany, Canada, and Italy, as well as being the most important countries for international cooperation in this area. Sustainable development, engineering education, students, education, curriculum, teaching, and sustainability were the most frequently mentioned keywords in this field in the last 10 years. Within this field, the use of active methodologies, sustainable development, good pedagogical and sustainability practices, and the construction of competencies and skills are emerging as research topics. The teaching of sustainability in engineering is a relatively new theme, however, due to the urgency of the subject many studies have been developed and many others are unfolding, however, the complexity of the theme does not exhaust the gaps in the subject
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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 | 0.017 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".