The Perspectives of EFL Students at Yarmouk University towards Using YouTube in Learning and Understanding English during Covid-19 Pandemic
Bibliographic record
Abstract
The Covid-19 epidemic has forced several nations to adjust to new conditions in a variety of fields, including education. Jordan has made the decision to go from in-person instruction to online classes utilizing a variety of programs, including WhatsApp, Teams, and Zoom. During the pandemic, students used YouTube to learn and comprehend English. This study examines how watching YouTube videos affects students' English language proficiency and contrasts it with lectures delivered by professors through Zoom and WhatsApp. Additionally, it illustrates the challenges of using YouTube videos for online learning as well as possible solutions. To achieve the objectives of the study, the researchers use qualitative and quantitative method to be applied on 100 fourth-year college students from the department of English language and literature, College of Education, Yarmouk University. The researchers conclude that students consider YouTube as a learning tool as they have motivation for using YouTube videos to understand academic materials to the extent that they believe that YouTube videos help them to improve their performance and language skills more than the lectures given by teachers on Zoom and WhatsApp. The researchers find three Barriers of using YouTube videos in learning and understanding English identified from the students of English as a Foreign Language (EFL) at Yarmouk University during Covid-19 pandemic. The three barriers are lack of interpersonal contact, technological barriers and physical barriers. The researchers also suggest approaches to overcome those obstacles. A number of recommendations were also given in this publication in light of the study's findings.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".