Online learning - Global Challenges and Opportunities for Students in Higher Education amid the COVID-19 Pandemic: The Libyan Context
Bibliographic record
Abstract
The spread of COVID-19 has had psychological effects on higher education students globally reflected in high level of anxiety associated with worries of failing to complete their studies (Holmes et al., 2020; Sawahhel, 2020). Due to COVID-19 all universities in Libya were closed for ten months causing a massive impact and leaving about quarter a million students without education. However, during this period some universities took preventive measures and maintained functioning from a distance. An attempt was made in this study to explore higher education students’ attitudes toward online learning and appreciate more the advantages and challenges associated with online learning. Of the 100 questionnaires sent out to university students, 58 responded back of whom 40 undergraduate and the remaining postgraduate students. The results of this study suggested that students are more interested in conventional way of learning in favour of face-to-face communication with tutors and peers as opposed to remote learning. For online learning to be successful in Libya, universities ought to upgrade their educational mode of delivery making the learning contents and assessment more desirable and responsive to the needs of the changing times. Furthermore, students must be technically and financially supported with unlimited access to internet.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".