Modern English Drama and the Students’ Fluency and Accuracy of Speaking
Bibliographic record
Abstract
Speaking a language involves more than simply knowing the linguistic components of the message, and developing language skills requires more than grammatical comprehension and vocabulary memorization. In teaching-learning processes, drama method may have some positive effects on ELL students’ speaking fluency and accuracy. This study attempts to probe one of the main concerns of language learners, that is, how to improve their speaking components, e.g. oral fluency and accuracy. To attain this aim, the researchers investigated the effect of two selected texts from modern English dramas on students’ speaking fluency and accuracy. They distinguished fluent from non-fluent and accurate from no accurate learners. Therefore, the current study was designed as a true experimental research and the data were gathered from 60 EFL students, whose ages are between 19-25 (80 percent girls and 20 percent boys),of English language and literature at Hormozgan University in Iran. The data were the recorded speaking transcripts which were analyzed to show the probable progresses after four-time (10 weeks) treatment. The factors to be considered in present study were the numbers of filled and unfilled pauses in each narration, the total number of words per minute, mean length of utterance, and number of stressed words. The results were compared and their temporal and linguistic measures were correlated with their fluency scores. They revealed that the speech rate, the mean length of utterance, phonation time ratio and the number of stressed words produced per minute were the best predictors of fluency scores, and thus, students’ speaking fluency increased, whereas the students’ speaking accuracy decreased in some areas of speaking abilities and oral communications.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".