Comparative Analyses of the Teaching Methods and Evaluation Practices in English Subject at Secondary School Certificate (SSC) and General Certificate of Education (GCE O-Level) in Pakistan
Bibliographic record
Abstract
The study was conducted to compare the teaching methods and evaluation practices in English subject at secondary school certificate (SSC) and general certificate of education GCE-O -level in Pakistan. The population of the study was students, teachers and experts at SSC and 0- level in the Punjab province. Purposive and random sampling techniques were applied to select the schools and teachers. It was a descriptive study in which the questionnaires were developed to collect the data, and it was analyzed applying t test and chi square. It revealed that GCE- O level results are better due to the availability of competent teachers, educational equipments, management and incentives given to teachers. The examination system also promotes understanding conceptual clarity instead of cramming. It also revealed that the teacher practise Direct, Audio Lingual and activity based methods in GCE –O level system whereas at SSC level system Grammar Translation and Lecture methods were frequently used by the teachers. It was recommended that the steps may be taken from the concerned institutions to design and implement teacher training programme, equip the schools with the modern instructional technology and provision of language based textbooks.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".