Empirical Study on the Influence of Mobile Apps on Improving English Speaking Skills in School Students
Bibliographic record
Abstract
Since technology provides adaptable, learner-centered opportunities for language acquisition, smartphones, and mobile applications have become indispensable in the era of Industrial Revolution 4.0, especially in higher education. Research indicates that both teachers and students view mobile learning as an effective tool for learning foreign languages and that mobile-assisted language learning (MALL) has made significant strides in offering resources and language exercises that can be completed at any time and place. The objective of this empirical study is to evaluate how mobile apps affect EFL students' English-speaking abilities and look into the relationship between skill development and app usage frequency. Additionally, it looks for potential moderating and mediating factors that affect how well mobile applications improve English speaking, illuminating the complex dynamics present in the EFL learning environment. The study used a concurrent embedded design and collected data on students' attitudes and views of smartphone English language learning apps (ELLA) through the use of a 26-item questionnaire. The questionnaire had a good degree of internal consistency with a score of 0.95 following data analysis, and t-tests were used to evaluate significant differences between groups. The data were gathered using a Likert scale. The results show that using mobile apps improves English-speaking abilities moderately but consistently, regardless of socioeconomic status. An important factor in this relationship is self-motivation. With beneficial ramifications for educators and legislators, the study highlights the potential of mobile apps as a useful tool for improving English proficiency among different student populations.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".