A Meta-analysis of the Literature on Mobile Assisted Language Learning in Response to COVID-19 in Saudi Arabia
Bibliographic record
Abstract
This study attempts a meta-analysis of research conducted in Saudi Arabia on Mobile Assisted Language Learning (MALL) related to the teaching and learning of English language in response to COVID-19 that led to the lockdown of education institutions. In this connection, a comprehensive search on Google Chrome and Google Scholar was conducted to collect data to answer the research questions and thus achieves its objectives. Fifty research articles and PhD dissertations were identified, but only seven of them met two selection criteria used in this study: the study should be conducted during or after COVID-19; and it should focus on mobile applications per se. These criteria excluded forty-two articles and PhD dissertations from selection. The studies that were not selected for meta-analysis were either review research articles or data-driven research articles that did not center upon specific mobile applications as in the case of articles that simply focused on “pronunciation applications” without naming one such application. The studies selected for meta-analysis used qualitative, quantitative, and mixed methods to collect their respective data. Positive results emerged from all the studies regarding the use of mobile applications in EFL learning in the Saudi context. This conclusion is equally true for motivation, perception and attitude studies. The results fell roughly into three major categories: the use of mobile applications in informal learning, learners’ motivation, perceptions and attitudes towards mobile phone applications as learning platforms, and the effect of mobile applications on learning style.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".