Pakistani Learners’ Perceptions Regarding Mobile Assisted Language Learning in ESL Classroom
Bibliographic record
Abstract
Technological advancement with its extensive use in every field of life has also impelled the educators to apply innovative techniques inside the classrooms. Mobile assisted language learning (here on wards MALL) is a latest technique which is gaining popularity. The current paper intends to explore the perceptions of Pakistani ESL learners on integrating MALL in English language classroom. The study used quantitative paradigm as research design. The population of the study comprised of Intermediate students, studying in public-sector colleges of Lahore. From the said population 60 students from 6 public sector colleges in Lahore were selected through simple random sampling. The data were congregated through a close-ended questionnaire. The collected data were later analyzed with the help of SPSS. The results illustrated that the Pakistani students have shown positive inclination towards MALL usage inside the ESL classrooms. The study also highlights another feature of MALL that it not only supports learning with ease and comfort but also motivates learners to learn in a collaborative ambiance. If MALL can be implemented intelligently in Pakistani classrooms it can be an influential tool for language learning.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".