Second Language (L2) Learners’ Perceptions of Online-Based Pronunciation Instruction
Bibliographic record
Abstract
The COVID-19 pandemic resulted in the widespread adoption of online instruction all around the world. In fact, in the post-pandemic era, online teaching and learning are proliferating and are considered as alternatives to traditional learning. The current study investigated L2 learners’ perceptions of an online pronunciation course. Sixty L2 learners, ranging in age from 18 to 60, were recruited from different intensive English programs (IEPs) across the United States and six other countries, including India, Brazil, China, France, Russia, and Canada. The participants received online-based computer-assisted pronunciation training (CAPT) on Moodle over a period of three weeks and completed an online survey on Qualtrics. The results of the quantitative and qualitative data collected from the learners at the end of the course showed that the learners were highly satisfied with their own performance and that they found the online course highly useful and preferred it over a face-to-face pronunciation course. The findings provide valuable insights into the design and delivery of online courses for pronunciation teachers. The findings also suggest that CAPT can effectively support asynchronous L2 pronunciation teaching.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".