The Effects of YouTube in Multimedia Instruction for Vocabulary Learning: Perceptions of EFL Students and Teachers
Bibliographic record
Abstract
The present study sought to examine the improvement in vocabulary comprehension and retention of Saudi English as foreign language female students at King Abdul Aziz University as a result of integrating YouTube in their reading classes. The study also investigated the perceptions of both students as well as teachers towards the inclusion of YouTube on the development of vocabulary. One hundred female intermediate level students aged between 18-20 years old participated in the study. Students were divided into two groups: an experimental group who watched YouTube during the reading activities and a control group who was not exposed to the videos. Data were collected using pre-tests and post-tests in addition to questionnaires. The findings of the study reveal that the group who viewed the YouTube clips outperformed the group who was not exposed to YouTube videos in the posttest. The results clearly show that YouTube provided statistically significant effects on the students' vocabulary acquisition. The findings of the study indicate that the participants positively viewed the use of YouTube in their lessons. The findings also revealed significant improvement in the students’ vocabulary achievement. The implications of the findings are discussed within the context of foreign language learning and teaching.
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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 | 0.005 |
| 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".