Prospect for Cell Phones as Instructional Tools in the EFL Classroom: A Case Study of Jahangirnagar University, Bangladesh
Bibliographic record
Abstract
AbstractThough mobile assisted language learning is an important issue in ELT in many countries, Bangladesh is far away from this method of learning. The present paper has focused on the mobile assisted language learning where the author has tried to explore the potential of cell phone usages as instructional tool in EFL classroom of Bangladesh. The researcher conducted a case study on Jahangirnagar University of Bangladesh. Some undergraduate EFL students participated in the study as subjects. To find out the benefits and challenges of using cell phones in the classroom, some SMS based class tests were conducted where the teachers sent mobile SMS to students as a means of instruction for teaching appropriate use of preposition and to find out the efficacy of SMS based class, a test was also taken via SMS and students also replied and students were given feedback through SMS. After that, students and teachers opinion regarding the cell phone usages; its benefits and inhibiting factors for the implementations in the EFL classroom are collected through students’ questionnaire, teacher interviews and classroom observation reports. The research results imply that cell phone has great potential as instructional tool despite some challenges that can be solved by the sincere attempts of the authority, teachers and by changing the ethical point of view that consider cell phones as mere a disturbing factor in the classroom. Some probable solutions for the challenges of using cell phone in the class are also presented. The study will be beneficial for educators of Bangladesh and many developing countries like Bangladesh. It will also be supportive for the EFL teachers and students who are always pursuing for more innovative way for teaching and learning English. Such studies are rare in Bangladesh, though some studies regarding m-learning are done by some researchers previously.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".