The Use of Keyword Video Captioning on Vocabulary Learning Through Mobile-Assisted Language Learning
Bibliographic record
Abstract
Video captioning is a useful tool for language learning. In the literature, video captioning has been investigated by many studies and the results indicated that video captioning may foster vocabulary learning. Most of the previous studies have investigated the effect of full captions on vocabulary learning. One of the key aspects of vocabulary learning is pronunciation. However, the use of mobile devices for teaching pronunciation has not been investigated conclusively. Therefore, this paper attempts to examine the effect of implementing keyword video captioning on L2 pronunciation using mobile devices. Thirty-four Arab EFL university learners participated in this study and were randomly assigned to two groups (key-word captioned video and full captioned video). The study is an experimental one in which pre- and post-tests were administered to both groups. The results indicated that keyword captioning is a useful mode to improve learner’s pronunciation. The post test results indicate that there was no statistically significant difference between the two modes of captioning on vocabulary learning. However, learners at keyword video captioning performed better that full video captioning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".