Essay Writing and Its Problems: A Study of ESL Students at Secondary Level
Bibliographic record
Abstract
Writing is the most important genre of all four modules of language. In Pakistan, English is taught as a second language and developing English writing competence is essential for successful communication at all levels of the education system. However, students face challenges in mastering English essay writing skills. The main objective of this study is to investigate the challenges faced in English essay writing by Secondary school students in District Rahim Yar Khan. However, the specific objectives were to determine strategies employed by teachers for teaching essay writing skills, problems faced and strategies employed by students for learning these skills. Finally, methods were proposed for teachers and students for enhancing English essay writing skills among students. A descriptive survey research methodology was adopted. The target population was teachers and students of public secondary schools of District Rahim Yar Khan except for schools of Tehsil Liaquatpur. The sample consisted of 170 students and 27 teachers from 17 sampled schools. Questionnaire from teachers and students and an essay writing test from students were conducted to collect data. The descriptive statistical technique was used to analyze quantitative data in the form of percentages and frequencies. It was evaluated that most common teaching methods used are demonstrations, lectures and question and answers. However, effective teaching methods like oral presentations, peer teaching, group discussions, and role play are not widely used. Moreover, teachers do face problems like low salaries and high workload which effects teaching. Based on the study, recommendations were made for students, teachers, and government to address the challenges students face in English essay writing at secondary level.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".