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Problem Solving and Decision-Making Skills for ESD: A Bibliometric Analysis

2023· article· en· W4390009987 on OpenAlexaboutno aff
Mujib Ubaidillah, Putut Marwoto, Wiyanto Wiyanto, Bambang Subali

Bibliographic record

VenueInternational Journal of Cognitive Research in Science Engineering and Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management in Higher Education
Canadian institutionsnot available
FundersKementerian Pendidikan, Kebudayaan, Riset, dan Teknologi
KeywordsScopusIndonesianField (mathematics)Theme (computing)Computer sciencePsychologyData sciencePolitical scienceWorld Wide WebMathematicsMEDLINE

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.1180.153
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.058
GPT teacher head0.493
Teacher spread0.435 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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