Improving Speaker’s Use of Segmental and Suprasegmental Features of L2 Speech
Bibliographic record
Abstract
Unlike L1 acquisition, which is based on automatic acquisition, L2 adult learners’ acquisition of English phonology is based on mental reflection and processing of information. There is a limited investigation of L2 phonology research exploring the contribution of the cognitive/theoretical part of pronunciation training. The study reports on the use of online collaborative reflection for improving students’ use of English segmental and suprasegmental features of L2 speech. Ninety participants at the tertiary level at Tabuk university in the kingdom of Saudi Arabia were divided into two groups which used an online instruction. The only difference between the instruction of the experimental group and the control group is that the experimental group spent part of the time of instruction on collaborative reflection, while the control group spent this time on routine activities without using collaborative reflection (but all other activities were the same). The results showed that the online collaborative reflection improved the pronunciation of the experimental group. The learners learned the pronunciation of the major segmentals (e.g., vowels, consonants, diphthongs), minor segmentals (e.g., the way of articulation), and the suprasegmental features (e.g., intonation, stress). The results also showed that students perceived the online collaborative reflection as a helpful means in improving their use of L2 English phonology features. The findings have important implications and contribute to our theoretical knowledge of second language acquisition and L2 phonetics instruction research.
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.
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 | 0.002 |
| 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 | 0.001 |
| 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".