Identification of the Difficulties Faced by Teachers in Teaching English as a Second Language at Public Sector Schools
Bibliographic record
Abstract
This study investigates the challenges faced by female teachers in teaching English as a second language (ESL) at public sector schools in Lahore, Pakistan, with a focus on elementary-level education.English, recognized as a global language critical for communication, education, and professional opportunities, poses unique difficulties when taught as a second language in non-native contexts.The research aims to identify specific barriers that hinder effective ESL instruction, particularly for female teachers in public schools.A descriptive survey method was employed, utilizing a questionnaire with 20 statements based on a 5-point Likert scale, administered to a convenience sample of 50 female English teachers from five government schools in Lahore.Data analysis involved calculating frequencies, percentages, and means to assess teachers' perceptions of instructional challenges.Findings reveal multiple obstacles, including students' lack of motivation, shyness, and fear of making mistakes, which impede active participation in English classes.Additional challenges include insufficient teaching resources, limited instructional time, overcrowded classrooms, and a non-conducive learning environment.Teachers reported that students with Urdu backgrounds struggle to adjust to English, often feeling confused or hesitant due to fear of inaccuracy, which negatively impacts their performance and achievement.The use of dual languages in instruction and a lack of refresher courses for teachers further exacerbate these issues.Moreover, defective textbooks and monotonous syllabi fail to engage students, contributing to low interest and ineffective learning outcomes.Despite these challenges, teachers acknowledge the importance of English as a medium of instruction for academic and professional success, yet they face significant burdens in fostering productive student engagement.The study highlights the need for systemic improvements, such as enhanced teacher training, updated curricula, and better resource allocation, to address these challenges.By identifying these difficulties, the research provides valuable insights for school administrators and policymakers to improve ESL instruction in public sector schools, ultimately enhancing students' language acquisition and academic performance in a globalized world.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".