Learning the pronunciation of English words from textual input: Should we listen first?
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
This study investigated factors influencing incidental English word pronunciation acquisition by upper-intermediate L2 learners through exposure to spoken discourse. Due to inconsistent English spelling-sound correspondences, silent reading is likely to leave learners with inaccurate pronunciations. This study explored whether these inaccuracies could be easily corrected through listening. Two sequences were compared: silent reading followed by listening and listening followed by silent reading.\nIn a counterbalanced within-participant design, 50 upper-intermediate ESL learners at a research-intensive University in Ontario engaged with a text containing 16 target words. The text was divided into to parts. Participants either read a part silently, then aloud, followed by listening, or they listened first, then read silently and aloud. The sequence was reversed for the other part of the text. Post-tests assessed pronunciation improvements and interviews explored individual differences.\nThe results indicated that a single audio exposure was insufficient for accurate pronunciation acquisition. Both the trial-and-error and retrieval approaches yielded comparable final outcomes. However, the Input-Output-Input sequence (listening, reading, and listening again) showed potential as a more effective teaching strategy, combining the benefits of both approaches to enhance learning outcomes.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it