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Record W2983940301 · doi:10.5539/elt.v12n12p30

A Comparative Study of Critical Thinking Skills Between English and Japanese Majors in a Normal University

2019· article· en· W2983940301 on OpenAlexvenueno aff
Fenglin Zhou, Yuewu Lin

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsCritical thinkingPsychologyMathematics educationDimension (graph theory)Foreign languageCognitively Guided InstructionInterpretation (philosophy)PedagogyTeaching methodLinguistics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.318
Teacher spread0.305 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations7
Published2019
Admission routes1
Has abstractyes

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