Error Analysis in English Vowel Acquisition: A Case Study of Yi Ethnic Junior High School Students in Liangshan
Bibliographic record
Abstract
This study focuses on the challenges faced by Yi minority students in Liangshan Yi Autonomous Prefecture, China, in acquiring English phonetics within a trilingual educational context (Yi/Mandarin/English). Through field questionnaire surveys, various phonetic errors made by Yi students in English vowel pronunciation were identified and statistically analyzed. The study attempts to explore the underlying causes from the perspectives of language acquisition and trilingual education. Additionally, exam-oriented motivational factors further exacerbate learning difficulties. The research reveals a dual transfer mechanism: cognitive-perceptual filtering dominated by L1 phonological categories and socio-affective influences such as classroom anxiety. Based on dynamic multilingualism and selective transfer theories, an integrated intervention framework is proposed, combining contrastive tri-lingual vowel training with culturally responsive pedagogy. Key strategies include articulatory visualization, pronunciation activities grounded in Yi oral traditions, and structured perceptual-production training sequences. This approach transforms L1 transfer from a learning obstacle into a pedagogical resource, offering practical solutions for improving vowel acquisition in minority trilingual education contexts. The study emphasizes the importance of integrating psycholinguistic perspectives with culturally sustaining pedagogy to effectively address persistent phonological challenges in third language acquisition.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".