Exploration and Exploitation of Mobile Apps for English Language Teaching: A Critical Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the progression of various mobile technologies, mobile applications have tremendously increased, especially during the Covid-19 pandemic, and such applications have been exploited much in teaching and learning. This study explores the educational potential of using mobile applications in English language teaching (ELT) or Mobile Assisted Language Teaching (MALT). A critical review of the research in mobile applications in English language teaching is explored in this study, specifically from the published papers since 2015. Initially 131 articles were selected from ScienceDirect, SAGE, IEEEXplore, and Google Scholar. However, only 13 articles matched the inclusion criteria. These articles were analyzed and reviewed using the following categories: the role of mobile technology, pedagogical practices, research methodologies, the context of usage, and outcomes. The research found that mobile technologies in teaching language are increasing, and it is expected to rise in the future. In addition, teachers use different technologies to enhance English language teaching in the settings of inside and outside classrooms. During the COVID-19 pandemic, schools have closed indefinitely. This unexpected situation has forced students to stay at home, and online learning seems to grow exponentially. Thus, through this research review, significant educational outcomes are identified for future investigation practices.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it