Can Critical-thinking Skills be Measured by Analyzing Metacognition?
Bibliographic record
Abstract
Metacognition refers to the capacity to comprehend and manage one's own cognitive processes, enabling the recognition of biases and presumptions that might influence them. Numerous published studies indicate that through metacognition, enhancements in critical-thinking skills allow individuals to query information, assess data, and make more evidence-grounded decisions—a cornerstone of critical and analytical thinking. This study aims to delineate the association between metacognition and critical-thinking skills via a meta-analysis. The data sources encompass articles published on Scopus between 2013 and 2023, identified using the keywords "Metacogni*" AND "Critical Thinking". This meta-analysis included 60 studies from diverse global locations. Employing a random-effects model, the analysis evaluated both effect size and potential publication biases using Jamovi 2.2.5 and OpenMEE software. The outcomes affirm a significant correlation between metacognition and critical-thinking skills, highlighting a robust effect of metacognition on critical-thinking skills, reflected in a significant measure of r = 0.649. Thus, increasing metacognitive abilities will improve critical-thinking abilities.
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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 | 0.053 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".