Improving EFL Secondary Learners’ Pronunciation through Multimodal Teaching
Bibliographic record
Abstract
This quasi-experimental study investigated the effect of multimodal teaching on Thai EFL secondary learners’ pronunciation. Their perception regarding the implementation of multimodal teaching was also explored. Selected from an intact class, the participants were 20 eighth graders receiving a teaching method based on the analytic-linguistic approach through audiovisual media combined with hand gestures. The English pronunciation pretest and posttest were administered to collect the data before and after the treatment, which lasted 6 weeks. In addition, a perception questionnaire and a semi-structured interview were conducted to investigate the participants’ perceptions pertaining to the multimodal teaching method. The quantitative data were analyzed using descriptive and inferential statistics, including means, standard deviations, dependent and independent sample t-tests. The qualitative data were analyzed using thematic analysis. The results demonstrated that multimodal Teaching utilizing audiovisual media combined with hand gestures significantly improved the participants’ pronunciation. Moreover, the participants showed their perceptions about multimodal teaching at a very high level. In conclusion, this study suggests the positive outcomes of implementing multimodal teaching in improving EFL secondary learners’ pronunciation. Finally, pedagogical implications and recommendations for future research are provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".