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Record W2556595959 · doi:10.5539/ijel.v6n6p129

The Effect of Oral Summary of Short Stories on Iranian Intermediate EFL Learners’ Vocabulary Learning: With a Focus on Gender

2016· article· en· W2556595959 on OpenAlexvenueno aff
Amir Reza Nemat Tabrizi, Setareh Abbasi

Bibliographic record

VenueInternational Journal of English Linguistics · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyTest (biology)Vocabulary learningMathematics educationPsychologyLanguage proficiencyControl (management)PedagogyComputer scienceLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This study investigated the effect of producing oral summary of short stories by language learners on vocabulary learning of EFL learners at intermediate level and the possible different effect of this classroom activity on male and female participants. In order to pursue the purpose of the study, one hundred twenty Iranian EFL learners were given Nelson proficiency test as the proficiency test and pretest. Seventy five learners were chosen as the participants for the study who formed one control group and two experimental groups. After twenty sessions of treatment, the participants were given a piloted version of a researcher-made test based on the vocabulary used in the short stories. The findings of the study proved that oral production of short stories can have a positive effect on vocabulary learning among intermediate learners, but the effect of this mode of teaching was not different on male or female language learners. The findings of the study could be used by language teachers who aim at conducting learner-centered language classes and material producers who aim at increasing the outcome of language courses by opting suitable course content.

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)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.270
Teacher spread0.248 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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