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Record W4407238403 · doi:10.5430/wjel.v15n4p30

An Experimental Study of the Effectiveness of Role-play in Improving Fluency in Jordanian EFL Students' Speaking Skills

2025· article· en· W4407238403 on OpenAlexvenueno aff
Luqman Rababah

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsFluencyMathematics educationPsychologyComputer scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

This research examined whether role-play exercises improved Jordanian EFL students' speaking fluency. Fifty intermediate EFL students were studied for six weeks. The experimental and control groups were randomly assigned. Both groups got classroom instruction and speaking practice; however, the experimental group also participated in role-plays. Pre- and post-tests assessed participants' speaking fluency. The research compares the fluency levels of a role-play group with a typical classroom teaching group. The experimental and control groups differed significantly in speaking fluency development. Role-playing improved speaking fluency more than the control group. These findings imply that role-playing may improve Jordanian EFL students' speaking fluency. Role-play games help students to utilize the target language spontaneously and naturally, improving speaking fluency and confidence. Thus, EFL teachers should use role-play to develop students' speaking abilities.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.277
Teacher spread0.272 · 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 designNon-randomized trial
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
Published2025
Admission routes1
Has abstractyes

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