Exploration and Exploitation of Mobile Apps for English Language Teaching: A Critical Review
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".