Integrating Mobile Phones in Teaching Auditory and Visual Learners in an English Classroom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it