Navigating Challenges and Solutions in English as a Second Language Speaking Instruction: Insights from the Teachers’ Perspectives
Bibliographic record
Abstract
Speaking skills play a significant role in language learning, and they support learners by engaging effectively in academic, professional, and social interactions. It is necessary for language teachers to help learners participate efficiently in various academic and social contexts. The perceptions of teachers are influential in deciding effective techniques for teaching speaking skills, as they impact the learning environment and learners' language improvement directly. The main objective of this study is to examine the perspectives of English as a Second Language (ESL) schoolteachers in teaching speaking skills. The current study employed a survey method to collect information on the skills needed for language teachers, the factors that influence the learners' speaking skills, and information regarding teachers who teach speaking skills. A total of 72 English school teachers across the state of Tamil Nadu, situated in India, participated in this study. Although teachers use interactive and student-centered methods in the language classroom, they often rely on conventional teaching methods to address the lack of motivation, confidence, and courage among the learners. The findings of this study revealed that the reduction of psychological barriers and the development of self-esteem among language learners should be the prime focus of ESL teachers. Teachers can create holistic and supportive learning environments that address linguistic and psychological barriers by implementing psychological and therapeutic modalities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".