Teaching ESL through SMS: Prospects and Problems in Nigeria Idegbekwe, Destiny
Bibliographic record
Abstract
There is a growing call for English as a second language teachers to explore different learning environments and teaching options to spice up the traditional ‘boring’ English language classes. The onus has been on language teachers to discover these innovative platforms and implement same in their classes. It is against this background that the present study presents the prospects and problems of using the SMS on mobile phones for teaching the English language in Nigeria knowing fully well that at least 15.5 million Nigerians of different ages own at least one mobile phone. The study reveals that most English language teachers do not recognise the cheap, exciting and handy nature of the SMS learning environment. The study also discovers that amongst all the network providers in the country, only MTN provides an SMS based ESL class that is mainly focused on grammar tips. The study examines some problems associated with the SMS learning environment; chief amongst them being the restricted text typing environment of SMS and the inability to take pictures for illustrations. However, the study recommends that despite the short comings, the SMS platform still provides an efficient and cheap learning environment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".