Developing EFL Learner’s Speaking Ability, Accuracy and Fluency
Bibliographic record
Abstract
The significant care and the globalization of English have been caused broad demand for good English-speaking skills in various realms. The evidence manifested that some features of speaking abilities are amenable to instruction in the second or foreign language classroom (Derakhshan, Tahery, & Mirarab, 2015). In spite of the verified evidence in speaking, there are still debates over English as a Foreign Language (EFL) learners’ speaking ability and approaches. Therefore, the present paper aimed to provide readers with interesting materials, empowering activities such as imitation, responsive, intensive extensive performance, transactional dialogue, and interpersonal dialogue to improve their speaking abilities. In addition, the EFL learners can boost their speaking ability by utilizing various instruments such as, role play, videos, flash cards, and graphs. Furthermore, this paper takes into account the significant components and keys to improve speaking competence accurately and fluently. To this goal, language teachers have vital roles in creating appropriate environment in the classroom that encourages both children and adults to firstly take part in classroom conversations and then, facilitate opportunities to keep doing it outside of the classroom. Thus, it is beneficial for both children and adults. Finally, this paper reviews some empirical studies to clarify the effectiveness of various methods and approaches to promote the speaking skill accurately and fluently.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".