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Record W2623215819 · doi:10.5539/elt.v10n7p104

Communicative Competence of the Fourth Year Students: Basis for Proposed English Language Program

2017· article· en· W2623215819 on OpenAlexvenueno aff

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsPsychologyCommunicative competenceGrammarCompetence (human resources)Communicative language teachingNounAdverbVerbLinguistic competenceLanguage assessmentLanguage educationMathematics educationPedagogy

Abstract

fetched live from OpenAlex

This study on level of communicative competence covering linguistic/grammatical and discourse has aimed at constructing a proposed English language program for 5 key universities in Vietnam. The descriptive method utilized was scientifically employed with comparative techniques and correlational analysis. The researcher treated the surveyed data through frequency counts, means and percentage computations, and analysis of variance/t-test to compare two main area variables. The respondents was 221 students from 5 universities randomly chosen. The major findings of the study generally reveal that the students’ level of communicative competence is a factor of their parents’ academic influence. Their linguistic/grammatical and discourse competence is helped by their chance for formal and intensive learning, conversing with a native speaker of the English language, rich exposure to social media networks, and reading materials written in English. Moreover, the students’ greatest strength along linguistic competence is on the use and function of noun, pronoun and preposition, while their weaknesses are on the use and function of conjunction, adverb, interjection, and verb. It is a general finding that the 4th year students who are linguistically competent on the whole system and structure of a language or of languages in general (consisting of syntax, morphology, inflections, phonology and semantics) have the tendency to speak or write authoritatively about a topic or to engage in conversation. Basing on the findings from this study, an enhancement program was proposed with the certainty that this proposed English language program would bring the best efficiency in the second language acquisition.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.077
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.314
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 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

Citations35
Published2017
Admission routes1
Has abstractyes

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