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Record W2604486737 · doi:10.1075/jicb.5.1.04che

A sneak peek at training English-medium instructors in China

2017· article· en· W2604486737 on OpenAlexaboutno aff
Rui Cheng

Bibliographic record

VenueJournal of Immersion and Content-Based Language Education · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)EMIChinaTraining (meteorology)Medical educationPerceptionPsychologyEngineeringPolitical sciencePedagogyMedicineGeographyTelecommunications

Abstract

fetched live from OpenAlex

English-medium Instruction (EMI) has become increasingly common in non-English speaking countries such as China. EMI instructors’ inadequate English proficiency has been reported as a major hindrance to successful EMI. This study examined EMI instructors’ perceptions on the effectiveness of overseas training programs. The participants of the study were 75 instructors from 20 universities in a big metropolis in China who were engaged in a four-month international assignment in one of the universities in Australia, Canada and the US between 2009 and 2010. Data sources included survey and written reports. Constant comparison was applied to generate common themes. Results indicated that the instructors regarded the training programs that focused more heavily on pedagogy as more effective and hoped for more context-specific pedagogy applicable to Chinese educational systems. They perceived the training programs with an emphasis on supervised teaching practices as more effective. Implications are provided for EMI instructors and administrators.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.262
Teacher spread0.227 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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Same venueJournal of Immersion and Content-Based Language EducationSame topicSecond Language Learning and TeachingFrench-language works237,207