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Record W2906907713 · doi:10.5539/ach.v11n1p1

The Research on Effectiveness of Communicative Language Teaching in China

2018· article· en· W2906907713 on OpenAlexvenueno aff
Bao e Song

Bibliographic record

VenueAsian Culture and History · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsCommunicative language teachingChinaCommunicative competencePopularityArgument (complex analysis)PedagogySociologyClass (philosophy)Norm (philosophy)Class sizeMathematics educationLanguage educationPsychologyPolitical scienceEpistemologySocial psychologyLaw

Abstract

fetched live from OpenAlex

Since China initiated Communicative Language Teaching (CLT) practice, it has enjoyed increasing popularity amongst educational practitioners as well as professional researchers. This paper undertakes an in-depth and all-around analysis of pedagogical practices of English class so as to ascertain the feasibility and effectiveness of CLT in China. Although China’s educational system is centrally-controlled, the top-down intervening policy of CLT fails to improve students’ interactive competence. Due to the contextual constraints including excessive class size, limited class hours, Confucian heritage culture, teacher equalizations as well as norm-referenced assessment, current situation of English Language Teaching (ELT) nevertheless is far from aligning with the tenet of CLT. This paper reveals that direct transfer of western–originated CLT practice is infeasible and ineffectual without considering the specific contextual factors in China and doomed to be a failure. Based on this argument, a combination of traditional pedagogy and CLT with an eclectic and dichotomous perspective is proposed and recommended to put into practice in the hope of adapting CLT paradigm to the particular Chinese contexts.

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)0.010
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.317
Teacher spread0.277 · 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 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

Citations8
Published2018
Admission routes1
Has abstractyes

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