Research Trends in Critical Thinking: Bibliometric Analysis Using VosViewer (1994–2023)
Bibliographic record
Abstract
Critical thinking skills have emerged as a prominent aspect of 21st-century competencies over the past three decades. The authors have shown a growing interest in investigating these skills in recent years. This research employs bibliometric analysis through the VOSviewer application, spanning the period from 1994 to 2023, to scrutinize the current trends and status of critical thinking skills research. Utilizing Scopus data, a total of 16,011 journal articles were considered for bibliometric mapping analysis. The findings reveal that in 2022, there is a peak of publications is 1602. The most commonly used keywords include critical thinking, education, and higher education. Noteworthy papers by Anderson T. et al., Andrews-Hanna J.R. et al., and Carini R.M are identified as the most frequently cited documents. Prolific authors such as Hwang, Pascarella, and Oxman are highlighted, and journals like Nurse Education Today, Thinking Skills and Creativity, and Journal of Nursing Education stand out. Leading institutions include the University of Toronto, State University of Malang, and Monash University, while the most active countries in this field are the US, UK, and Australia. The findings from the science mapping reveal an increasing focus on the development of critical thinking abilities, particularly within the realms of Health and Education. This indicates that the subject is likely to remain a prominent discussion point in the future, given its significance in enhancing both work quality and educational standards.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.170 | 0.208 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".