The effects of enhancing L2 multiword items in captions: An approximate replication of Majuddin, Siyanova-Chanturia, and Boers (2021)
Bibliographic record
Abstract
Abstract Studies investigating the acquisition of multiword items (MWIs) from reading have furnished evidence that the likelihood of acquisition improves considerably if such items are typographically enhanced (e.g., bolded or underlined) in the texts. In the case of captioned audio-visual materials, however, an earlier study by the authors did not find such compelling evidence. In that study, indications of an effect emerged only when the same video was watched twice. Arguably, for learners to benefit more immediately from typographic enhancement in captions, they may need to be made aware of its purpose beforehand. The present article therefore reports an approximate replication of Majuddin et al. (2021), but this time the students were informed about the MWI-learning purpose of watching the video. As in the original study, the learners watched a video once or twice with standard captions, with captions in which MWIs were enhanced, or without captions. The positive effect of enhancement for MWI learning was clearer than in the original study, and it already emerged after a single viewing. On the downside, enhancement was found to have a negative effect on lower-proficiency learners' comprehension of the content of the video.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".