THE IMPACT OF TECHNOLOGY ON LANGUAGE LEARNING: A STUDY OF MOBILE ACCENTS IN ENGLISH- SPEAKING COUNTRIES
Bibliographic record
Abstract
The rapid incorporation of technology into language education has altered the landscape of acquiring dialects of English, but less is known about how technology is used to learn dialects in non-native contexts.In this study, we explored the use of technology (mobile applications, digital media (YouTube, podcasts), and augmented reality (AR)) to learn English dialects (British, American, Australian, and Canadian) with 200 learners in Iraq.Using a mixed-methods approach, the study included administration of surveys, in-depth interviews, content analysis of 50 digital products, and a case study of the app "Accent Coach".The results showed that mobile applications (85% usage) and digital media (YouTube and podcasts; 70% videos, and 60% podcasts) assisted in understanding dialects, especially British and American dialects.Insights revealed that access and authenticity of material were contributing factors to the success, but limited resources for Australian and Canadian dialects and inconsistencies in content delivery posed challenges in the learner experience.The findings show that learners reported higher confidence applying British (M = 4.3) and American (M = 4.1) dialects compared to their confidence applying either or both Australian (M = 3.5) and Canadian (M = 3.4) dialects.The study also highlights the recommendation to diversify resources available within the language curriculum, create effective and low-cost tools that include dialects, and document teacher readiness in using these tools.This study emphasizes the opportunities that technology provides to bridge dialectical learning gaps in a non-native context and stresses the need for sustainable, effective, and quality digital solutions that are inclusive.
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How this classification was reachedexpand
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".