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Record W2944144211 · doi:10.5539/elt.v12n6p72

The Effect of Using Communicative Language Teaching Activities on EFL Students’ Speaking Skills at the University of Jeddah

2019· article· en· W2944144211 on OpenAlexvenueno aff
Shorouq Ali AL-Garni, Anas Almuhammadi

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyInterviewMathematics educationCommunicative language teachingClass (philosophy)Significant differenceControl (management)Experimental researchTeaching methodPedagogyLanguage education

Abstract

fetched live from OpenAlex

The aim of the present study was to examine the effect of using communicative language teaching (CLT) activities on EFL students’ speaking skills at the English Language Institute (ELI) of the University of Jeddah (UJ). The researcher conducted the current study in two classes of 21 female EFL students each; one class was the experimental group and the other the control group. The experimental group was taught using three communicative activities—interviewing, problem-solving, and role-playing—while the control group was taught using traditional methods. The current study followed a quasi-experimental study to answer the primary research question. The quasi-experimental study was conducted using a pre- and post-test design to determine if there was a significant difference between the scores of the experimental and control groups. The findings of the current study show that the experimental group scored higher than the control group. These findings have positive implications for the continued implementation of CLT teaching practices at the ELI of UJ.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.008
GPT teacher head0.260
Teacher spread0.253 · 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

Citations43
Published2019
Admission routes1
Has abstractyes

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