Impact of Virtual Teaching on ESL Learners' Attitudes under Covid-19 Circumstances at Post Graduate Level in Pakistan
Bibliographic record
Abstract
Covid-19 proved and pandemic that has affected the whole world on a large scale. Every walk of life got disturbed by this pandemic. Educational institutions not only in Pakistan but all over the globe remain close, which causes a loss of study for the students of all Grades, notably Higher education (Postgraduate Level), which directly affected education, learners, and teachers in terms of learning, time, and economically. Virtual Teaching (VT) is proving an emerging method of teaching in the field of education all over the world. Developed countries have opted for this method of teaching much before. In Pakistan, universities under the directions of HEC started Virtual Teaching VT (Online Teaching) for the students, which was an attempt to cover the loss on an experimental basis. This study is conducted to know the impact of VT on ESL students' behavior. For this purpose among 100 students of KFUEIT, RYK University distributed a questionnaire to measure their behavior level. Students' participation was inspiriting, and their response found positive in this new field of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".