Ready or Not: Gulf Country Teachers’ Challenges toward Teaching Online Courses in Emergency Cases in Higher Education
Bibliographic record
Abstract
The purpose of this study was to determine the challenges faced by teachers from the Arabian Gulf countries of Saudi Arabia and Kuwait while teaching virtual online courses. Because online learning in higher education in these countries had not occurred before the current pandemic, the teachers and students faced new challenges for the first time, including online communication, inadequate training, insufficient practice, and incompetence in online assessment. Seventy-six teachers of higher education in Kuwait or Saudi Arabia participated in this study, which was a survey created by the first author to determine the effectiveness of communication, training, practicing and assessing students’ performance during the pandemic. Results indicated that no differences were found between the two countries; while participants felt that training was adequate for the task of converting to remote teaching, they were concerned about nonverbal aspects of communication and assessing online work. Suggestions included obtaining participants from other Gulf countries, refining the survey, and involving different types of institutions such as private colleges. The results of this study imply that for many teachers, improvements in communication and assessment are necessary to improve online teaching, which is likely to continue in these countries after the pandemic is 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".