Short Message Service (SMS) as an Innovative Mobile Learning Approach in Malaysia
Bibliographic record
Abstract
Mobile phones usage had extensively penetrated into the world. A study done by the Malaysian Communication and Multimedia Commission (MCMC) found that in the first quarter of year 2010, the penetration rate for cellular phone in Malaysia is 121 per 100 inhabitants. Penetration rate over 100% occurs because of multiple subscriptions (Adnan, 2012). Malaysia has the second highest mobile penetration in South East Asia after Singapore (Baharom, 2013). The learners appreciated the text messages and felt that SMSes had helped them to stay focused and engaged in their studies. SMS emphasized learner-centered, where the learner can access information anytime, anywhere in order to build their skills and knowledge. SMS is also a community-centered where the learners were required to collaborate and share their views on the discussion topic enhancing constructive learning experience. This article presents the importance of SMS as an innovative approach for mobile learning in Malaysia.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".