Saudi EFL Students’ Perceptions Towards the Impact of YouTube on Improving Speaking Skills
Bibliographic record
Abstract
The significance of integrating technology in education, especially the widely and frequently used platform “YouTube”, is found in its ability to provide accessible and various educational resources, meeting the needs of diverse learning styles and enhancing active engagement. Since YouTube is one of the preferred applications utilized by Saudi individuals, this study investigated the impact of using YouTube on enhancing the speaking skills of EFL male and female Saudi students from different Saudi universities. This study was conducted during the first semester of the academic year 2021- 2022. A questionnaire was used as the study instrument. It was adapted from Binmahboob’s (2020) study and modified according to the study’s purposes. The questionnaire contained 17 items divided into two sections. 79 EFL students from different Saudi universities participated. Fifty-nine of them were females, while 20 were males. The findings of the study revealed that YouTube videos had a high positive perspective from the participants. In other words, it can be concluded that YouTube videos enhance the speaking skills of EFL university students. Based on these finding, some recommendations and implications were provided.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".