Dubbing for Oral Language Acquisition: A Literature Review on Practices and Implementation (2017-2024)
Bibliographic record
Abstract
Dubbing, originally a technique used in movie or drama production, has been employed in language teaching and learning in recent years. This literature review aims to examine the current research state of dubbing used in language learning and the practices and implementation of using dubbing in second or foreign language learning between 2017 and 2024. By adopting the method of literature review, the initial search yielded 17847 articles, of which 45 were finally included in the analysis. The findings indicate that most studies were carried out in China, in the higher education context, and its application in English acquisition dominates. It summarises how dubbing is used in the classroom. It also elaborates that the selection of video clips is based on various factors, including the embedded knowledge, useful expressions in daily life, and the duration of the clips. Additionally, the type of speech in the video, the genre of the clips, the accents of the speakers, and instructor selection or student selection are also taken into consideration. It also concludes factors affecting the frequency of dubbing and ICT support. It implies that the proliferation of MALL and ICT may accelerate dubbing application to second or foreign language learning. The results should be considered a foundational step toward developing tailored and effective dubbing programs for learners aiming to improve their oral language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".