Problem Solving and Decision-Making Skills for ESD: A Bibliometric Analysis
Why this work is in the frame
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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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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.070 | 0.062 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it