Using Songs to Enhance Thai EFL Primary Learners’ Pronunciation of Verb With the /ing/ Ending Sound
Bibliographic record
Abstract
Pronunciation is a crucial component of language acquisition, vital for achieving clarity and fluency in communication. This study, utilizing cycles of action research, aimed to evaluate the impact of songs on the pronunciation skills of Thai EFL learners. Additionally, this study explored the participants’ perceptions of using songs in their English class and employing a cyclical action research process. The study involved 25 third-grade students with poor pronunciation skills. Data was collected through song-based lessons, pronunciation pretest and posttest, and a semi-structured interview. Quantitative data were analyzed using descriptive and inferential statistics, including means, standard deviations, and paired-sample t-tests. Qualitative data were examined using content analysis. The findings revealed that songs significantly enhanced the pronunciation skills of the participants at the 0.05 significance level, with a mean score of 76%. Participants viewed song-based lessons positively, noting improved learning environments and emotional and functional benefits. These lessons made the classroom more engaging and enjoyable while helping reduce anxiety and boost motivation. Functionally, songs aided in improving pronunciation through repetitive and rhythmic practice. The study’s findings suggest that incorporating songs into the curriculum can be an effective strategy for enhancing pronunciation skills in Thai EFL learners, providing significant pedagogical advantages and fostering a more dynamic and supportive classroom atmosphere.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".