‘I Couldn’t Join the Session’: Benefits and Challenges of Blended Learning amid COVID-19 from EFL Students
Bibliographic record
Abstract
COVID-19 has changed the process of teaching considerably, as educational institutions around the world moved to adopt blended learning initiatives to ensure continuity, while managing the spread of this infectious disease. All Saudi Arabia’s universities have continued to deliver courses via digital platforms. This study draws on traditional views about blended learning (Sharma, 2010) and examines the pedagogical changes to English courses implemented at King Saud University following the start of the COVID-19 pandemic. It aims to explore the benefits and challenges of blended learning during the spread of COVID-19 from the perspective of English as a foreign language (EFL) student. Qualitative data were collected from two focus group sessions, and one-to-one interviews with twelve students taking a general intensive English course at King Saud University over a six-week period. The results reveal that blended learning benefited the EFL students by supporting their writing skills and encouraging them to search online, as well as by matching their circumstances and being economical. It also identifies that the challenges EFL students faced included technological problems, flaws in the instructor’s performance, difficulties with online tests, attitudes to online learning and limited resources, and the university council’s decisions. The paper concludes with recommendations to exploit the benefits identified, and overcome the challenges of blended learning when teaching English in an EFL context.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".