What Predicts the Effectiveness of Foreign-Language Pronunciation Instruction? Investigating the Role of Perception and Other Individual Differences
Bibliographic record
Abstract
Abstract: This study investigated second language (L2) learners’ perception of L2 sounds as an individual difference that predicted their improvement in pronunciation after receiving instruction. Learners were given explicit pronunciation instruction in a series of modules added to their Spanish as a foreign language curriculum and were then tested on their pronunciation accuracy. Their perception of the target sounds was measured with an AX discrimination task. Though the best predictor of pronunciation post-test score was pre-test score, perception made a unique and significant contribution. The other factors associated with better pronunciation of some L2 sounds were age, attitude, and time spent using Spanish outside the classroom. The results suggest that instructors should give adequate time for learners to hone their perception of target sounds at the outset of pronunciation instruction, because their initial ability to perceive the target sounds will, in part, determine how much they learn from such instruction. The results support models of L2 speech acquisition that claim that target-like perception is a precursor to target-like production, in this case in a formal learning context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".