Teaching in Tough Times: Examining EFL Teachers’ Perceptions of Online Learning Challenges in the Context of Higher Education in Saudi Arabia
Bibliographic record
Abstract
The global spread of online learning is noticeable and has further expanded due to the COVID-19 pandemic. Although this mode of learning is effective, several concerns have been raised by teachers. This qualitative study aimed to explore English as a Foreign Language (EFL) teachers’ perceptions of the online learning environment in order to uncover the challenges they face while teaching online. To achieve this objective, semi-structured interviews were conducted with 25 EFL teachers from the Preparatory Year Program (PYP) at King Abdulaziz University (KAU) in Jeddah, Saudi Arabia to examine their perceptions of online teaching. The transcribed interviews were coded using NVivo®, after which a thematic analysis was employed to reveal the emerging themes. The results showed challenges related to four main themes: students; institution; teachers; and the system, as well as a number of sub-themes. This study found that the most significant challenge that EFL teachers faced in an online learning environment was related to students, specifically their participation, motivation, tendency to cheat during online exams, and not taking responsibility for their learning. Another major challenge was the result of copying face-to-face learning to the online learning environment without making suitable adjustments. This research also shed light on some of the negative consequences of online learning for EFL teachers; its findings could help institutions and policymakers to modify content and support teachers by training them to develop their online teaching skills.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".