Exploring Omani EFL Students’ Perceptions of the Newly Adopted Online Learning Platforms at the University of Technology and Applied Sciences-Salalah
Bibliographic record
Abstract
Due to the rapid advancement of the relevant technology and the COVID-19 health pandemic, educational institutions have had to adapt to ongoing and ever-changing circumstances at a very rapid rate. Thus, the Moodle and Microsoft Teams platforms are being used by teachers to more directly teach students, as well as fulfilling its initial role in providing a supplementary tool to maintain, the convention of independent learning. The current study explores the perceptions of a group of Omani students who are currently enrolled in the English Language Center of the University of Sciences and Applied Technology, Salalah campus, (hereafter referred to as UTAS) regarding the new online learning platforms, i.e. Moodle and Microsoft Teams. To this end, a questionnaire was adopted from Rojabi’s (2020) study to measure the perceptions of the students towards both Moodle and Microsoft Teams platforms. A sample of 100 students was randomly selected from the population. The findings of the study have offered some important suggestions on how to improve the existing online platforms and pave the way for further research to be conducted in the same area.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".