Effectiveness of Mobile-assisted Language Learning in Developing Oral English in Higher Education: A Comparative Systematic Review
Bibliographic record
Abstract
English is a globally prominent language, and oral English proficiency is both crucial and challenging. Mobile technology offers a promising avenue for language enhancement, but research on the role of Mobile-Assisted Language Learning (MALL) in English-speaking skills is relatively scarce. Literature reviews on this topic are even rarer, particularly those that provide comparative analyses between China and other nations. This study addresses this gap through a comparative systematic literature review of 30 relevant studies from 2019 to 2023. The findings reveal similarities between Chinese and global studies, with only slight differences in sample size and oral English proficiency assessment methods. The preferences for mixed research methods, tests, questionnaires, and interviews were found. Additionally, this review identifies limitations in previous research, including a lack of theoretical frameworks, limited large-scale studies, and a need for deeper exploration of mobile app utilization. This comparative analysis provides valuable insights that can guide future studies and foster a more comprehensive understanding of MALL’s effectiveness in enhancing oral English proficiency, both in China and globally.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".