Mobile-Assisted Shadowing: Transforming Pronunciation for Arab English Learners
Bibliographic record
Abstract
Shadowing helps improve various aspects of pronunciation of second language learners. The present action research study investigated the effect of mobile technology-enhanced shadowing on the pronunciation of Arab learners of a second language. Sixteen Arab learners of English used iPods to shadow short dialogues for eight weeks. The study participants were asked to practice at least four times per week for 10 minutes per practice as they recorded the shadowing session. Extemporaneous speaking and shadowing tasks were administered as pre-test, mid-test, and post-test. This was followed by 2 native speakers of English who rated the tests. Speakers rated extemporaneous speaking tasks for fluency, accentedness, and comprehensibility and rated the shadowing task for the ability of learners to imitate a speech model. Based on the study's results, there was a significant improvement in three speaking measures, namely, the ability of learners to imitate a speech model, comprehensibility, and fluency, but not in accentedness. Results indicated that the participants improved significantly on all speaking measures apart from accentedness. These results show that mobile-enhanced shadowing can be engaging and effective in teaching second-language pronunciation and that teachers should incorporate it into second-language classrooms. Given these results, teachers and lecturers should embrace shadowing in second-language classrooms. Further, pedagogical implications are proposed, and study limitations are discussed accordingly.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".