Undergraduate EFL Students’ Perceptions About Their Experiences Attending Online Classes During the COVID-19 Pandemic at a Saudi University
Bibliographic record
Abstract
The purpose of this study was to examine undergraduate EFL students’ perceptions about their experiences attending online classes at a Saudi higher education institute during the COVID-19 pandemic. Random sampling was used to obtain the subjects of the study, twelve undergraduate EFL students who attended online classes for the first time at Al-Baha University, Saudi Arabia. A SWOT analysis was used to process the collected data. The main strengths of using online classes in the EFL context were time/place flexibility, promoting a more active/interactive learning style, and the availability of recorded sessions, all of which helped the students when they were reviewing the asynchronously-delivered content. The weaknesses were all related to technical issues (access to an adequate internet connection and an appropriate device on which to access the internet). This study is expected to generate new insights into the process of implementing online classes or blended classes to teach the English language in the Saudi context, and to examine the potential strengths, weaknesses, opportunities, and threats to such an adoption at the target university during the shift to online classes during the COVID-19 pandemic. These findings may be beneficial for other higher education institutions with a similar context in Saudi Arabia and may benefit higher education policymakers in Saudi Arabia.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".