Does Mode of Input Affect How Second Language Learners Create Form–Meaning Connections and Pronounce Second Language Words?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.104 | 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 it