Bibliographic record
Abstract
In this study two complementary models, which were based on the strengths of existing models, were developed to analyze students’ critical thinking skills. One model was used to categorize the types of critical thinking displayed by students; the other was used to evaluate the quality of the critical thinking. The models were refined and tested for usefulness through an analysis of students’ messages posted in two sequential two-week computer conferences. The study also examined the level of critical thinking displayed compared with that shown by other studies and highlighted critical thinking skills that were not frequently demonstrated by participants. Dans cette etude, deux modeles complementaires, bases sur les points forts de modeles existants, ont ete developpes pour analyser la capacite de pensee critique des etudiants. Un modele a ete utilise pour categoriser les types de pensee critique demontres par les etudiants, alors que l’autre a ete utilise pour evaluer la qualite de la pensee critique. Les modeles ont ete raffines et testes en fonction de leur utilite grâce a une analyse des messages des etudiants places dans deux forums numeriques sequentiels de deux semaines. L’etude a aussi examine le niveau de pensee critique demontre en comparaison avec celui indique dans d’autres etudes et a mis en lumiere des habiletes de la pensee critique peu frequentes chez les participants.
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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 | 0.025 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.020 | 0.007 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".