Mobile-Assisted Language Learning (MALL) in Senior High School English Classes
Bibliographic record
Abstract
As technology continues to offer promising results in teaching and learning, teachers also shift their methods to maximize the potential it can offer. One popular method is the integration of mobile devices in teaching and learning macro skills. Thus, this study describes the implementation of the Mobile-Assisted Language Learning (MALL) strategy in Senior High School English classes. With the use of validated MALL-based lesson exemplars, the researcher has described the integration of MALL in different oral communication lessons in six public senior high schools. This involved qualitative analysis of classroom observations and focus group discussions with students. The results of the implementation revealed that the utilization of mobile devices can support language learning in several ways. MALL in English classes can be seamlessly integrated and provide access to authentic language input, practice opportunities, and personalized learning experiences. Even with the technical limitations during the implementation, students find learning English fun, engaging, and worthwhile. Thus, English teachers may use this research as a guide in their integration and implementation of MALL-based English lessons. Further, given the limitations of the study, it is recommended that larger-scale studies be conducted to examine long-term effects of MALL to provide a more comprehensive understanding of its potential to improve students’ language performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".