Examining incidental vocabulary acquisition from captioned video
Bibliographic record
Abstract
Abstract Previous comparisons of vocabulary uptake from captioned and uncaptioned audio-visual materials have almost consistently furnished evidence in favour of captioned materials. However, it is possible that many such comparative studies gave an advantage to the captioned input conditions by virtue of their use of written word prompts in the tests. The present study therefore examines whether aurally presented test prompts yield equally compelling evidence for the superiority of captioned over uncaptioned video. Intermediate EFL learners watched a ten-minute TED Talks video either with or without captions and were subsequently given a word recognition and a word meaning test, with half of the test prompts presented in print and the other half presented aurally. While the results of the word recognition test were inconclusive, the word meaning test yielded significantly better scores by the group that watched the captioned video. However, this was due entirely to their superior scores on the printed word prompts, not the aural ones. This suggests that evaluations of the benefits of captions for vocabulary acquisitions should take input-modality – test-modality congruency into account.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".