Students’ Perceptions and Experiences of Online Education in Pakistani Universities and Higher Education Institutes during COVID-19
Bibliographic record
Abstract
This study investigates the perceptions and experiences of students regarding the various aspects of online education while studying at the Pakistani Higher Education Institutes (HEIs) and universities that shifted to online modes of instruction during the COVID-19 pandemic. The focus of this study was to identify the level of satisfaction of students with the support being provided to them by their institutes and instructors; the use of different modes of communication and assessment methods; and their home study environment. It also explored the positively and negatively influencing factors affecting online education, as perceived by them. An online questionnaire-based cross-sectional survey research design was chosen for conducting this study. Data were collected from 707 respondents belonging to various Pakistani HEIs and universities and analyzed using the SPSS software. The results revealed a considerable dissatisfaction among the study population regarding online education being provided to them during the COVID pandemic. The participants raised concerns over the lack of institutional support and the quality of online instruction. Other issues raised included unsuitable study environments, unavailability of electricity, and connectivity issues. Overall, the majority of the students indicated that they would not like to opt for online classes in the future once the pandemic was over.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".