Problem Solving and Decision-Making Skills for ESD: A Bibliometric Analysis
Bibliographic record
Abstract
Problem-solving and decision-making skills are essential for individuals across various fields. These skills emphasize the importance of preparing a generation capable of solving problems and making informed decisions. Therefore, this study aimed to learn the publication trends related to problem-solving and decision-making skills for ESD (Education for Sustainable Development) from 2013 to 2022 through Bibliometric analysis. In line with the analysis, a VOSviewer software was used to graphically analyze the obtained bibliographic data. A total of 1519 documents were also analytically acquired from the Scopus database. The results showed a fluctuating trend in the number of publications, with the Journal of Chemical Education and Social Sciences being the highest contributor and the most prevalent field of study at 147 and 689 documents, respectively. The United States was also ranked first in the documents emphasizing problem-solving and decision-making skills, at 512 documents. Moreover, the University of Toronto was the most prolific affiliation, contributing the most publications at 17 documents. The representatives from Indonesia were also grouped into two institutions in the global top twenty affiliates, namely (1) the Indonesian University of Education and (2) the State University of Malang. In line with the results, 159 study experts from Indonesia contributed to the analyzed theme, as the top author originated from the United States having 7 documents. The top document excerpts were also published 240 times in the journal Expert Systems with Applications. The trend of the study visualization subsequently produced 9 clusters, problem-solving and decision-making skills, human, psychology, clinical competencies, education, curriculum, support systems, creativity, and content analysis. These results were helpful to relevant experts, regarding the analytical trend in problem-solving and decision-making skills, recommending directions for future analyses.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.118 | 0.153 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".