How Thai EFL Learners Deal With English Regular Past Forms: A Case Study of a Speech Sound Perspective
Bibliographic record
Abstract
The pronunciation of regular past tense verbs seems to be difficult for most L2 learners, especially in L2 learners whose first language phonological system is different from the English one. It is predicted that Thai EFL students could encounter difficulties perceiving English coda clusters like the ‘-ed’ ending sounds. For this reason, this research investigates the extent to which the first-year and the third-year English major undergraduate students in a public university perceive the English regular past tense verbs. To be precise, this study compares the ability to perceive the regular past tense of the first-year and third-year students. In addition, the strategies they use to perceive the ‘-ed’ ending verbs among the three different allomorphs ([t], [d] and [ɪd]) are investigated. The data collection was derived from the perception tests of 30 first-year and 30 third-year students and a Pronunciation Learning Strategy (PLS) questionnaire. The perception tests were divided into two subtests: perception test and perception syllable identification test. The PLS questionnaire was employed to find out the strategies they used in English pronunciation learning. The overall results show that the third-year students demonstrated a better performance than the first-year students in both tests (t=-2.778; p<.01 in the perception test; t=-1.466; p>.05 in the syllable identification test). However, the syllable identification test’s results do not show consistency with Solt et al.’s (2004) model, while the perception test’s do. Moreover, the findings from the questionnaire reveal no statistically significant difference between the first and the third-year students in terms of pronunciation learning strategies (p>.05).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".