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Improving the Components of Speaking Proficiency

2011· article· en· W1892418488 on OpenAlexvenueno aff
Taher Bahranı, Rahmatollah Soltani

Bibliographic record

VenueCanadian social science · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsnot available
Fundersnot available
KeywordsListening comprehensionFluencyLanguage proficiencyVocabularyHumanitiesPsychologyLinguisticsActive listeningPedagogyMathematics educationArtCommunicationPhilosophy

Abstract

fetched live from OpenAlex

One of the main concerns of language learners is how to improve their speaking proficiency in general and different components of speaking proficiency such as fluency, accuracy, accent, vocabulary, comprehension, and communication in particular. Accordingly, the present research attempts to investigate the effect of listening to different TV programs on improving different components of speaking proficiency. To achieve this purpose, a sample speaking test was given to twenty language learners as a pre-test. During the study, the participants had exposure to different programs from TV. After a period of three months, a post-test was administered. Then, the scores of each component in the pre-test were compared with that of the post-test. The result showed that the use of vocabulary as a component of speaking proficiency improves more. On the contrary, accuracy improves less than the other components. Key words: Speaking proficiency; Improve; Vocabulary; Accuracy Resume: L'une des preoccupations principales des apprenants de langue est de savoir comment ameliorer l'expression orale en general et maitriser de differents composants de la competence de l’expression orale comme la fluidite, la precision, l'accent, le vocabulaire, la comprehension et la communication en particulier. En consequence, la presente recherche tente d'etudier l'effet d'ecouter des emissions de differents programmes a la tele sur l'amelioration de la maitrise de differents elements de l'expression orale. Pour atteindre ce but, un test de langue a ete donnee a vingt apprenants de langue comme un pre-test. Au cours de l'etude, les participants ont ete exposes a de differents programmes de la television. Apres une periode de trois mois, un post-test a ete donne. Ensuite, les scores de chaque composant dans le pre-test ont ete compares avec ceux du post-test. Le resultat a montre que l’un des elements de competence orale, l'utilisation du vocabulaire, s'ameliore le plus. Au contraire, la precision s'ameliore moins que les autres composants. Mots-cles: Competence de l’expression orale; Ameliorer; Vocabulaire; Precision

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.244
Teacher spread0.156 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2011
Admission routes1
Has abstractyes

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