The Influence of Educational Background on Malaysian Chinese Learners’ Mispronunciation of /l/ and /r/
Bibliographic record
Abstract
Maintaining intelligibility among interlocutors while communicating in English remains a challenging task for many second or foreign language learners. This problem is attributable to many reasons, including learners’ obstacles with pronunciation. The recurring report of Chinese learners having incomprehensible pronunciation of /l/ and /r/ in English words has engendered debate on various factors underlying the problem. Despite the extensive discussion of this issue, previous studies had overlooked educational background as a potential factor which could affect learners’ pronunciation. Thus, this study investigated mispronunciations of /l/ and /r/ among Malaysian Chinese undergraduates vis-à-vis their educational background, namely Chinese-educated (CE) and non-Chinese educated (NCE). The study objectives were to determine CE and NCE learners’ frequency of mispronunciation of English words containing /l/ and /r/ according to phoneme, phoneme position, and mispronunciation characteristics. To this end, a quantitative approach was employed to conduct the study. For data collection, two pronunciation word lists covering /l/ and /r/ in initial, medial, and final positions were provided to 20 CE and NCE undergraduates respectively for assessment purposes. The participants’ pronunciations were recorded, transcribed and transformed into numerical data. The results of the study reveal that Chinese-educated Malaysian undergraduates tend to mispronounce English words containing /l/ in medial and final positions. Furthermore, words containing /l/ in the medial position tend to be substituted; while words with /l/ in the final position tend to be deleted or vocalized by the students. The findings of this study provide valuable insights into the teaching and learning of English pronunciation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".