Attitudes, Instructional Practices and Difficulties Faced by English Teachers While Teaching Through ‘Quality Drive’
Bibliographic record
Abstract
Quality Education is the bedrock foundation of primary level of educational pyramid. It helps not only in the development of individuals but also improve their living standard at academic and professional domains. Government of Punjab has initiated a literacy and numeracy drive movement to enhance the literacy rate. English is the major focus of concern in Literacy and Numeracy drive (LND). Present study has been conducted to study the attitudes, instructional practices and difficulties faced by the English primary teachers while teaching English through quality drive. The study was descriptive in nature based on the mixed method i.e. quantitative and qualitative approach. It was conducted in the rural and urban schools of the elementary wing of tehsil Khanpur district Rahimyar Khan. The results reflected that teachers showed positive behavior towards teaching English through LND. But they faced many problems like unhealthy environment, lack of facilities, influence of mother tongue, non cooperative behavior of parents and lack of interest in learning English. The weak base of grammar, vocabulary, pronunciation, spellings and comprehension are also the basic problems. It showed that traditional method is in common practice for teaching English language. The study recommends improvement in the mechanism of literacy and numeracy drive to produce better outcomes of English teaching.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".