Exploring the Effectiveness of Implementing the Action Oriented Approach in Improving English Language Skills Among ESL Learners: Teachers’ Perception
Bibliographic record
Abstract
A Communicative approach to language instruction emphasises real-life communication acts combined with vocabulary and grammatical constructions about a particular theme or context. The system now has a sociocultural element that motivates students to actively participate in their education. The technique has changed into the Action-Oriented Approach, which views pupils as social actors who must perform tasks in a wider social context in addition to linguistic ones. This study aims to ascertain how well an Action-Oriented strategy would be useful in enhancing English language proficiency among tertiary-level students in India. A sample of 70 English language instructors from various Indian institutions participated in the study and a 5-point Likert scale was employed to collect and analyse the data. The findings of the study reveal that the majority of ESL teachers agreed with the use of the Action-Oriented Approach to improve language learning inside and outside the classroom. Most of the respondents (91.4%) gave the statement "Practising English-speaking skills outside the classroom is essential for the learners' development" with the maximum rating (x=4.5857) and 95.7% of participants responded that English language teaching approaches should emphasize the use of tasks and activities that involve learners in practical, interactive, and goal-oriented communication through tasks and activities. The conclusions of this research would shed light on the efficacy of using an Action-Oriented strategy to improve English language proficiency among tertiary-level students in India. Most ESL teachers had positive opinions about implementing the Action-Oriented Approach, especially while imparting English language skills.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".