Perceptions of ESL Students About Online Collaborative Learning During Covid-19 In Hazara Division, Khyber Pakhtunkhwa, Pakistan
Bibliographic record
Abstract
With respect to Covid-19 pandemic and dilemma of an abrupt transition between physical setup and online setup, the following research study is focused on analyzing the perception of ESL students regarding Online Collaborative Learning (OCL), used as a learning tool to carry on academic practice when Covid-19 pandemic is ongoing in Hazara Division, Khyber Pakhtunkhwa, Pakistan. Moreover, the study aimed at exploring the elements of Online Collaborative Learning, which were viewed by ESL students as the most demanding, and then influenced their academic performance in terms of the targeted situation. In this regard, the needs of ESL students as far as online classes are concerned are identified, some relevant strategies given to meet the connected problems and an easy path to the students to realize the assigned missions and thus make a common goal to be attained by way of mutual efforts or collective work. The study was cross-sectional hence population based descriptive survey was employed; in addition, adapted questionnaire was performed using Google forms to gather the information and desired answers by sending them through emails. Further, the data collected was analyzed under SPSS to give valid results. Finally, with the help of the study results it has been found that most of the answers given by the students to the question about OCL is satisfying because they find it useful in building new thoughts and information related to a language through working together with others whereas bad internet connection, unawareness of using the resources adequately in the right manner, confusion given by ineffective peers and numerous others can be overcome by implementing the right solutions to virtual ESL environment at the right time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".