Voice-over as a didactic resource in foreign language education: The VOCAL Project
Bibliographic record
Abstract
The VOCAL (VOiCe-over and lAnguage Learning) Innovation Project is aimed at assessing the potential didactic benefits of using the audiovisual translation (AVT) mode of voice-over as a resource in foreign language (FL) education. The experimental design consisted of a didactic intervention in which students had to complete six voice-over-based lesson plans in which they had to create their own voice-over versions of six pre-selected short video extracts. The lesson plans were designed bearing in mind the importance of developing integrated skills and linguistic mediation. The nature of the study is mixed, since quantitative and qualitative variables have been considered to triangulate the perception of the participants with the data obtained from proficiency tests of integrated skills. The main findings of this study point towards a series of benefits of using didactic voice-over and the results seem to converge towards an improvement of production skills. In addition, development of oral reception was also observed and, subsidiarily, there was also an improvement in digital skills and linguistic mediation. The conclusions are consistent with the very nature of performing voice-over and call for further research in the area.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".