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Record W4309849228 · doi:10.5539/jel.v12n1p40

Critical Thinking Instruction Incorporated in Cross-Cultural Communication Course Design: A Needs Analysis Report Based on Voices of Chinese International College Undergraduates

2022· article· en· W4309849228 on OpenAlexvenueno aff
Xichang Huang, Yuan‐Cheng Chang

Bibliographic record

VenueJournal of Education and Learning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsCritical thinkingCourseworkPsychologyContext (archaeology)PedagogyMathematics education

Abstract

fetched live from OpenAlex

Critical thinking represents one of the most absolutely vital talents for every individual, both in the workplace and in their personal lives. Critical thinking has become a primary concern for all educational institution students in recent decades, particularly Chinese undergraduates. The intention of this study was to investigate the needs analysis of Chinese international college undergraduates’ perceptions of critical thinking skills incorporated into a cross-cultural communication course, as well as their expectations of critical thinking skills instructional coursework throughout the cross-cultural communication context. Throughout this mixed-methods investigation, 78 Chinese international college students volunteered to complete a critical thinking disposition inventory (CTDI) as the primary research instrument, in addition to a semi-structured interview. The research uncovered that Chinese international undergraduates exhibited unclear notions of critical thinking abilities. As a byproduct of China’s education system, the majority of Chinese international college undergraduates were flooded with dismissive attitudes toward critical thinking abilities. In order to control their academic advancement in a more critical and analytical fashion, Chinese students expressed a pressing need for critical thinking skills to be included in their cross-cultural communication course.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.028
GPT teacher head0.396
Teacher spread0.367 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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