The Online Learning and Students’ Fear of COVID-19: Study in Malaysia and Pakistan
Bibliographic record
Abstract
The COVID-19 pandemic has forced millions of students to stay indoors and adapt to the new normal, namely distance learning at home, placing online learning in the spotlight. However, students’ motivation for online learning and its effectiveness in skill development during the COVID-19 pandemic has not been widely studied. This study examined the relationship between students’ fear of COVID-19 and students’ social presence in online learning while investigating the parallel mediating role of student psychological motivation and cognitive problem-solving skills related to online learning. The participants were 472 university students in Malaysia and Pakistan. An online data collection technique using Google Forms was employed. Faculty members of the universities were asked to share the survey with their students. Moreover, using a snowball sampling technique, students were requested to share the survey with their friends. SPSS Statistics (Version 21) was employed to do preliminary data analysis, AMOS (Version 21) software was used to conduct confirmatory factor analysis using a maximum likelihood estimation, and Hayes’ PROCESS model was used to examine proposed hypotheses. The results show that only cognitive problem solving mediates the relationship between fear of COVID-19 and students’ social presence in online learning in Malaysian samples. In Pakistan, cognitive problem solving and psychological motivation mediate the relationship between fear of COVID-19 and students’ social presence in online learning. The study found that developing cognitive problem-solving skills and providing psychological motivation could enhance their engagement with online learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".