Bibliographic record
Abstract
This investigation intends to seek into investigating the effect of teaching the pronunciation through a fun activity of Rhythm on the learners' pronunciation improvement.This study is to find out whether students in this group will perform a significant improvement in pronunciation comparing to the other group.Rhythm of English is considered as one of the biggest difficulties for many foreign learners of English.It is more important for EFL learners who have a very different system in their L1 (e.g.Persian).These learners are not usually motivated for pronunciation practice.Therefore, this study will explore the effect of musical rhythm, on pronunciation improvement.120 Iranian EFL elementary learners in an English language institute aging from 7-9 years old will participate in this study.After the pretest they will be divided in two groups namely, control and experimental.In one group, teacher uses musical rhythm to teach as treatment of the study while in the other one, she does not.At the end of the term, a posttest will be given to both experimental groups to check any significant difference between their performances."Rhythm, actually, is timing patterns among syllables.However, the timing patterns are not the same in all languages.There are two opposite types of rhythm in languages: stress-timed and syllable-timed.According to Mackay (1985), stress-timed rhythm is determined by stressed syllables, which occur at regular intervals of time, with an uneven and changing number of unstressed syllables between them; syllable-timed rhythm is based on the total number of syllables since each syllable takes approximately the same amount of time.English, with an alternation of stressed and unstressed syllables, is obviously stress-timed" (Chen, C. et al., 1996).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.943 | 0.939 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".