Impact of E-Learning on High School Students’ English Language Learning
Bibliographic record
Abstract
The COVID-19 pandemic significantly affected all sectors, including education—schools were affected by widespread lockdowns, which necessitated the adoption of online learning platforms. Using a mixed-methods research methodology incorporating questionnaires and interviews, researchers in the current study examined the impact of e-learning on high school students’ English language learning, particularly their spoken skills, in Kuwait. The researchers studied a sample of 60 participants for the quantitative analysis and 18 students for the qualitative analysis. All were high school students in Kuwait enrolled in English classes. The study’s results revealed significant challenges associated with e-learning, including low acceptance rates among students. Most students disagreed that online learning is a perfect learning tool, suggesting that e-learning fails to promote critical thinking skills and facilitate learning. E-learning also affects learners’ capabilities to express their feelings and ideas. The interviews showed that e-learning failed to improve the students’ English language mastery. Some of the challenges we noted include technical hitches and the inability to deploy teaching strategies used successfully in physical classes. Overall, the results indicate that students disliked online learning in Kuwait. In conclusion, e-learning is a significant opportunity for students to improve their learning, but it must be effectively used to encourage students’ uptake. It is necessary to assess schools’ preparedness to implement it as well as to design complementary programs and strategies to ensure students gain mastery of the English language.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".