Effects of Process Drama on English Speaking Competence among Undergraduate EFL Learners in China
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".