Training ESL students to reproduce beat gestures in discourse leads to L2 pronunciation improvements
Bibliographic record
Abstract
The main goal of the present study is to assess whether training foreign language students to reproduce natural beat gestures in discourse can trigger pronunciation gains. A total of 18 young adult Catalan learners of English with an intermediate proficiency level participated in a 15-minute discourse-based pronunciation training session. Participants were randomly assigned to two groups. While one group was asked to simply repeat the instructor’s multimodal responses to discourse prompts by focusing on speech, the other group was asked to repeat the utterances together with the natural beat gestures that the instructor was using. Before and after training, participants were recorded producing a discourse completion task and their speech was rated for accentedness. Results showed that participants who accompanied their verbal repetition with beat gestures during training significantly reduced their accentedness scores more than those who were asked to only repeat the utterances without reproducing the beat gestures. These results support recent findings that show the value of embodied prosodic training for pronunciation instruction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".