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Case Study of Factors Affecting Chinese Students’ English Communication Performance

2009· article· en· W1685636520 on OpenAlexvenueno aff
Ping Liu

Bibliographic record

VenueCanadian social science · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsDeci-PsychologyYardstickLearnabilityCompetence (human resources)Language acquisitionLinguistic competenceHumanitiesPedagogyMathematics educationLinguisticsSocial psychologyComputer scienceArtificial intelligencePolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

For ESL teaching in China’s universities, not enough emphasis is put on verbal communication as a yardstick of language mastery and methodological success. Developing student’s communication competence is not only concerned with the nature of language learning from linguistic perspectives, but also could be influenced by such exogenous factors as learning environment, learning psychology, and learning strategies. It is necessary to investigate whether these factors have an impact on Chinese university students’ English communication performance. This paper tries to examine the relationships among social needs, system inefficiencies, learning objectives, learning strategies, and effort, according to a constructed model. The model’s hypotheses are drawn from theories as diverse as person-environment (PE) fit (Caplan, 1987), intrinsic motivation (Ryan and Deci, 2000), conceptions about learning approach (Entwistle, 1990), and “learning strategy” (Biggs, Kember, & Leung, 2001). The sample was collected from one of the Chinese universities in Southeast for a case study to shed light on how to improve English teaching and learning in TESL of China. The quantitative research method is used with SPSS system in this essay to report the statistical analyses of the model. Among the eight hypotheses tested, six were confirmed to be true, and two could not be validated. Key words: communication performance; 5-factor model; person-environment (PE) fit; intrinsic motivation; learning strategies Resume: Dans les universites chinoises ou l’anglais est enseigne comme la deuxieme langue, l'accent n'est pas suffisamment mis sur la communication verbale en tant qu’un critere de maitrise de la langue et du succes methodologique. Le developpement de la competence communicative des eleves n'est pas seulement concerne par la nature de l'apprentissage des langues du point de vue linguistique, mais pourrait aussi etre influence par des facteurs exogenes comme l'environnement d'apprentissage, la psychologie de l'apprentissage et les strategies d'apprentissage. Il est necessaire d'examiner si ces facteurs ont une influence sur la performance de communication en anglais des etudiants chinois. Le present document tente d'etudier les relations entre les besoins sociaux, l'inefficacite du systeme, les objectifs d'apprentissage, les strategies d'apprentissage et des efforts, selon un modele construit. L’hypothese du modele vient de diverses theories, telles que la theorie de l’adaptation peronne-environnement-(PE) (Caplan, 1987), la motivation intrinseque (Ryan et Deci, 2000), les conceptions sur l''approche de l’apprentissage ( Entwistle, 1990), et les strategies d'apprentissage (Biggs, Kember, & Leung, 2001). Les sujets d’etudes viennent de l'une des universites chinoises situees dans le Sud-est pour montrer la facon d'ameliorer l'enseignement et l'apprentissage de l'anglais en tant que la deuxieme langue en Chine. La methode de recherche quantitative est utilisee avec le systeme de SPSS dans cet essai pour montrer des analyses statistiques du modele. Parmi les huit hypotheses testees, six ont ete confirmees d’etre vraies et deux n'ont pas pu etre validees. Mots-Cles: performance de communication; modele de 5-facteurs; le modele de l’adaptation personne-environment(PE); motivation intrinseque; strategies d’apprentissage

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.297
Teacher spread0.262 · 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 teacher head, not a consensus.

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

Citations3
Published2009
Admission routes1
Has abstractyes

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