Speaking Fluency: Technology in EFL Context or Social Interaction in ESL Context?
Bibliographic record
Abstract
Language learning can occur outside the classroom setting unconsciously through interaction with the native speakers or exposure to authentic language input through technology. EFL context lacks the social interaction to boost language learning. Accordingly, this study aimed at investigating the effectiveness of exposure to audio/visual mass media as a source of language input in EFL context and social interaction as a source of language input in ESL context on speaking fluency. To achieve this purpose, a sample speaking test was administered to one hundred language learners in Iran which is an EFL context and one hundred language learners in Malaysia which is an ESL context. Then, forty participants from each context where selected. During the experiment, EFL participants had exposure to audio/visual mass media while the ESL participants had exposure to social interaction. At the end, both groups took another sample speaking test. The post-test showed that the EFL group performed better which proved that exposure to technology promotes speaking fluency. Key words: Exposure; Mass media; Social Context; EFL Context; ESL Context
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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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".