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Record W4378901472 · doi:10.5539/ijel.v13n4p15

The Positive Effect of Cultural Knowledge on Listening Comprehension of EFL Learners

2023· article· en· W4378901472 on OpenAlexvenueno aff
Zhaochun Sun, Aiqing Tan

Bibliographic record

VenueInternational Journal of English Linguistics · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersGuangdong University of Foreign Studies
KeywordsActive listeningPsychologyListening comprehensionCurriculumCompetence (human resources)Reading comprehensionReading (process)Test (biology)Cultural knowledgeMathematics educationPedagogyLinguisticsSocial psychologyCommunication

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the effect of target cultural knowledge on the listening comprehension proficiency of Chinese EFL learners. Two groups of intermediate-level English language learners participated in the study. One group received English lessons on reading, writing, listening, and speaking, as well as a curriculum focused on target cultural knowledge, specifically the culture of Britain and the US. The other group received only English lessons on reading, writing, listening, and speaking, without any focus on target cultural knowledge. The comparison between the results of a pre-test and a post-test administered to both groups indicated that target cultural knowledge had a positive impact on the listening competence of EFL learners. This finding was also confirmed by interviews with the EFL learners. The study is significant for the teaching of English listening comprehension. Furthermore, the findings also have important implications for the teaching of reading and writing.

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)0.001
metaresearch head score (Gemma)0.009
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.026
GPT teacher head0.316
Teacher spread0.290 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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