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Record W1660690229 · doi:10.20355/c5b59j

Content-Based English Education in China: Students’ Experiences and Perspectives

2012· article· en· W1660690229 on OpenAlexaffvenue
Gulbahar H. Beckett, Li Fang

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChinaSubject matterPsychologyContent (measure theory)PreferencePerceptionEmpirical researchMathematics educationContent analysisCollege EnglishSubject (documents)PedagogySociologyPolitical scienceSocial scienceComputer scienceLibrary sciencePhilosophy

Abstract

fetched live from OpenAlex

This study explores undergraduate students’ experiences and perceptions of the content-based EFL instruction at a northwestern Chinese university. It is one of the first empirical studies of content-based EFL in China. Through a three-part open-ended questionnaire administered with 34 undergraduate students majoring in finance, the study reveals overwhelming support for this approach to EFL. Participants believed that learning English and content knowledge simultaneously was helpful and that the spread of English in China can benefit the nation and its people. The findings also indicate that some participants were critical of the approach, stating that it is “shallow content teaching” and suggesting that subject matter content be taught in Chinese. The participants praised their original English texts and expressed their preference for student-centered learning.

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)0.003
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0060.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.319
Teacher spread0.282 · 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

Citations42
Published2012
Admission routes2
Has abstractyes

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