ESL Learners’ Sense of Alienation: An Exploratory Mixed Method Research on the Role of ESL Teachers’ Remarking Practices
Bibliographic record
Abstract
The study attempts to highlight a major cause of learners’ detachment and low performance in ESL classrooms at graduation levels in Bahawalpur City, Punjab, Pakistan. In this connection, this study tries to focus on the role of teachers’ feedback remarks as a major cause of either instilling or accelerating sense of alienation among ESL learners. This study underpinned exploratory sequential mixed method research design to prove its hypotheses. The qualitative data shows that ESL learners receive evaluative remarks from their teachers in the form of 'face-threatening acts' more than ‘face-saving acts’ during classroom activities. Resultantly, they experience a sense of alienation from the language-related tasks and try to avoid the classroom situation feeling it a threat. The quantitative analysis shows the average range of sense of alienation experienced by learners which are highest in oral activities, lower in written tasks and lowest in comprehension-based activities. ESL teachers' evaluative feedback either instils or accelerates the sense of alienation among learners during various classroom activities. The type of alienation experienced more was an accelerated sense of alienation. This is why the majority of learners avoid getting engaged in the activities in which they find chances of losing self-image. Keeping the results in view, training sessions on ‘Face Wants, Politeness theory, and Speech Acts’ are recommended for ESL teachers to enhance their follow-up remarking practices. Moreover, there is a need to develop an anxiety-free classroom atmosphere to strengthen learners' autonomy and linguistic self-concept.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".