Does Mode of Input Affect How Second Language Learners Create Form–Meaning Connections and Pronounce Second Language Words?
Bibliographic record
Abstract
Abstract This study examined how mode of input affects the learning of pronunciation and form–meaning connection of second language (L2) words. Seventy‐five Japanese learners of English were randomly assigned to 1 of 3 conditions (reading while listening, reading only, listening only), studied 40 low‐frequency words while viewing their corresponding pictures, and completed a picture‐naming test 3 times (before, immediately, and about 6 days after treatment). The elicited speech samples were assessed for form–meaning connection (spoken form recall) and pronunciation accuracy (accentedness, comprehensibility). Results showed that the reading‐while‐listening group recalled a significantly greater number of spoken word forms than did the listening‐only group. Learners in the reading‐while‐listening and listening‐only modes were judged to be less accented and more comprehensible compared to learners in the reading‐only mode. However, only learners receiving spoken input without orthographic support retained more target‐like (less accented) pronunciation compared to learners receiving only written input. Furthermore, sound–spelling consistency of words significantly moderated the degree to which different learning modes impacted pronunciation learning. Taken together, the findings suggest that simultaneous presentation of written and spoken forms is optimal for the development of form–meaning connection and comprehensibility of novel words but that provision of only spoken input may be beneficial for the attainment of target‐like accent.
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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 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".