A Comparative Study of Critical Thinking Skills Between English and Japanese Majors in a Normal University
Bibliographic record
Abstract
Critical thinking is one of the core objectives of talent training in higher education. Meanwhile, the cultivation of critical thinking skills in foreign language teaching has become more and more urgent, and it has also been written into the national standards for the training of foreign language talents. A good critical thinking includes both a skill dimension (Critical Thinking Skills) and a disposition dimension (Critical Thinking Dispositions). Critical Thinking Skills include interpretation, analysis, evaluation, inference, explanation and self-regulation. This study intends to explore the current situation of the critical thinking skills of undergraduates in foreign language majors (English and Japanese) in a Normal University, and then attempts to find out the similarities and differences in critical thinking skills between English majors and Japanese majors after years of study at college. The results show that a clear difference exists between English majors and Japanese majors in overall critical thinking skills. In particular, English majors are superior to Japanese majors. Another finding is that there are also differences between the two majors in the three core sub-skills of critical thinking skills, analysis, evaluation and inference.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".