What If There Is Nobody Around to Speak English? Then Keep Your Voice Diary
Bibliographic record
Abstract
This study aims to explore English Language Teaching (ELT) prep-class students’ perceptions of keeping personal voice diaries via a voice recorder as a way to extend speaking practice beyond the classroom walls. Following a ten-week treatment under which 12 voluntary students attending ELT prep-class at Ondokuz Mayıs University kept voice diaries in English outside the school on a regular daily basis, data were collected through semi-structured interviews. The qualitative analyses of the students’ answers indicate that an overwhelming majority of them regard speaking as a challenging skill due to factors like lack of fluency and excessive anxiety. As for the accessible chances to practice oral English, most of the participants report that there is an apparent inadequacy of opportunities beyond school. When asked about the contribution of individual voice diaries to the alleviation of the lack-of-practice-opportunities problem, almost all of the participants except one evaluate keeping voice diaries as an effective means of speaking improvement. It is praised mostly for its boosting effect on self-expression skills, fluency, and pronunciation; and for its lowering effect on anxiety and stress. In line with the findings of this study, it can be concluded that there is no equivalent substitute for the improvement of oral skills through dialogue and interaction between human beings in flesh and blood; however, keeping voice diaries on an individual basis can somehow help EFL learners overcome the limited conditions they face during their struggle to practice and improve oral English.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".