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Record W4407820024 · doi:10.5430/wjel.v15n4p111

Effects of Process Drama on English Speaking Competence among Undergraduate EFL Learners in China

2025· article· en· W4407820024 on OpenAlexvenueno aff
Lilliati Ismail, Norhakimah Khaiessa binti Ahmad, Qian Guo

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsDramaChinaMathematics educationComputer scienceProcess (computing)Competence (human resources)PsychologyPolitical scienceProgramming languageLiteratureArt

Abstract

fetched live from OpenAlex

Process drama has been widely utilized in educational settings to enhance student engagement and achieve communicative goals. Despite its growing application, there is limited empirical evidence assessing its effectiveness specifically for improving English as a Foreign Language (EFL) speaking competence among undergraduate students in China. This study investigates the efficacy of process drama in enhancing EFL speaking competence among non-English major undergraduates in China. A total of 84 participants were involved, with 42 assigned to an experimental group (EG) that received process drama-based instruction, and 42 to a control group (CG) that followed conventional teaching methods. Data were collected through pre- and post-tests evaluating speaking competence. The analysis employed paired samples t-tests, independent t-tests, and ANCOVA to compare the pre- and post-test results. The results demonstrated significant improvements in speaking competence in both groups; however, ANCOVA revealed that the EG exhibited significantly greater improvement compared to the CG (p = .003). This study confirms that process drama is significantly more effective than conventional teaching methods in enhancing English speaking competence, providing novel insights into the specific benefits of process drama, highlighting its potential to address the limitations of traditional teaching approaches and offering a valuable contribution to the field.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.644
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.273
Teacher spread0.268 · 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 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
Published2025
Admission routes1
Has abstractyes

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