Integrating Mobile Phones in Teaching Auditory and Visual Learners in an English Classroom
Bibliographic record
Abstract
This paper explores the possibilities of using mobile technology in the teaching and learning of the English language. A sample of 50 Sandwich students/teachers of the English language was drawn through a multi-stage sampling technique. The instrument used to collect data for this study is a ten-item questionnaire on integrating mobile phones in the teaching and learning of English. This instrument was validated by two language experts in the Department of English and Literary Studies, University of Nigeria, Nsukka. Data collected for this study were analysed using the percentage system represented in line charts. The results showed that mobile phones are instrumental in teaching and learning of English in classrooms. The paper concludes that M-learning promotes cooperative and collaborative learning through the enhancement of learner’s use of authentic English language that would make it possible for them to construct their own knowledge. Based on the results of this research, the researchers recommend that mobile phone can be integrated in teaching and learning of English as a Second Language.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".