Teaching of Remedial English and the Problems of the Students: A Case of University of Sindh, Jamshoro, Sindh, Pakistan
Bibliographic record
Abstract
The research paper is designed to explore the achievement of the aims and objectives of teaching remedial English. Italso aims to know the importance of the course and the problems of the students regarding the teaching of remedialEnglish at university level in Pakistan with special reference to university of Sindh, Jamshoro. In this regard manyefforts were taken by the tutors, lecturers, assistant professors, professors and the administration of the university toenhance the capabilities and efficiencies of the students of undergraduate level. All students of undergraduate leveland the teachers who take remedial English classes are constituted as the Population of the study. Five (n=5) teacherswho teach remedial classes and forty (n=40) students from different departments were recruited as the sample of thestudy through purposive and random sampling techniques. Interviews were conducted from students and tutors whoattend and teach remedial English course respectively. 90% students found unsatisfied from the administration of theclasses and they stressed that the classes should be conducted separately at department level and they demanded forthe basic needed facilities during the classes such as the facility of language laboratory, availability of computers,multimedia, audio and video resources in order to accelerate and enhance the teaching learning process to improveEnglish 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".