The Impact of Using YouTubes and Audio Tracks Imitation YATI on Improving Speaking Skills of EFL Learners
Bibliographic record
Abstract
The purpose of this study is to shed light on a developed approach to be adopted in EFL speaking classes and show the effectiveness of using YouTube videos and Listening Audio Tracks Imitation (YATI) for teaching English language in speaking classrooms as pedagogical tools to improve EFL learners’ speaking skills. To find out the impact of using You Tubes and Audio Tracks Imitation (YATI) on improving speaking skills of EFL learners, the qualitative experimental approach is used to conduct this study. The participants of this study are 48 students studying major English, divided into two sections studying Listening & Speaking Course at College of Science & Arts Muhayil, King Khalid University. One section was used as a control group and the other as an experimental group. Data was collected using speaking tests results which were analyzed using SPSS Pearson correlation coefficient. The results revealed that employing YATI technique has a positive impact on the effectiveness of the speaking skills, fluency and pronunciation of EFL learners. This study concluded that YouTube videos and Listening Audio Tracks Imitation (YATI) is a very effective CALL (Computer-Assisted Language Learning) tool towards improving students’ speaking skills. This study recommends the use of YATI approach in order to help students overcome speaking problems.
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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 | 0.006 |
| 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) | 0.004 | 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".