Analysing the Situation of ESL Teaching and Learning in Large University Classes in Pakistan
Bibliographic record
Abstract
The present study is based on a chapter of the PhD project conducted by the main researcher. It aims to explore the ESL teaching and learning practices in a Pakistani university by focusing on difficulties perceived and confronted by learners and teachers, and solutions suggested by them. One of the most significant issues at the university is large classes-exceeding to 100 and more students on average. The main researcher, being an ESL teacher at the target university, faced the same problem of large size and found it difficult to teach these classes. He embarked on analysing the situation so that he might come across some solutions through the suggestions and experiences of the ESL teachers and students of the same university. The design of the study is descriptive and the results of the present study come from the quantitative data collected through student and teacher questionnaires. The Student-participants were 300 undergraduate students from various major subjects attending English language support classes and 22 ESL teachers teaching these English language support classes at different institutes of the university. The data were analysed descriptively and presented with help of the boxplots. The views, commonly held by teachers are supported by the study’s findings i.e., large classes are likely to endorse teacher-centred approaches of teaching; very little significant student-student and teacher-student interaction is practised because of the inadequate physical environment; majority of learners remain off-task and appear to be unruly and they are given little, if any, feedback on their in-class and home tasks. Conversely, many teachers and learners reported that the adoption of group/pair work is likely to be an effective technique to use in these classes. Albeit a few teachers revealed having adopted group work infrequently, none used it all the time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".