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Record W4390341623 · doi:10.5539/elt.v17n1p83

Unveiling Chinese Approaches to British Case Study Group Discussions: Insights for Global Business Education

2023· article· en· W4390341623 on OpenAlexvenueno aff
Liyuan Wang

Bibliographic record

VenueEnglish Language Teaching · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
FundersChina Scholarship Council
KeywordsContext (archaeology)General partnershipPsychologyGlobalizationBusiness EnglishBusiness communicationBusiness educationIntercultural communicationPedagogyHigher educationMathematics educationPolitical science

Abstract

fetched live from OpenAlex

In the context of globalization in business education, students from all over the world participate in mixed case study group discussions to enhance their skills in risk forecasting and intercultural communication through collaborative exploration. Learners who possess effective case-based discussion techniques and strategies for success in one cultural context may find them either impactful or ineffective when applied in another learning culture. This study scrutinized the case study group discussion process involving a group of Chinese undergraduate students enrolled in a split-site degree program and their English-speaking partners. Three group discussion approaches—spiral, individual, and cumulative—were identified by analyzing the Chinese students’ strategies for manipulating topics and reacting to others’ opinions. These Chinese approaches illustrate unique autonomous learning strategies of self-reflection and inner dialogue within the study groups. The findings hold implications for the course design of English for Business Purposes (EBP) in business partnership degree programs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.048
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0120.012
Scholarly communication0.0070.006
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.047
GPT teacher head0.284
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes1
Has abstractyes

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